Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements

Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements

Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements

Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements

Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements

Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements

Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements

Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements

Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements

Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements

Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements

Express Data Science (EDS)

What is it

Our 3 months Express Data Science provides you with the following essential skills to:

• Understand the data analytics lifecycle, techniques and tools through Azure Machine Learning.
• Explore data using a variety of visualization, analytical, and statistical techniques through Transact-SQL and MS Excel.
• Utilize R to perform data analysis, data visualization and improve Machine Learning Models on Azure.

What you will learn

Our Data Queries and Visualization Basics module helps you to grip understanding and apply the different types of data visualisation techniques. You will be exposed to different methodologies and techniques to apply the following skills:

    • Write programs using T-SQL

    • Implement error handling and transactions using T-SQL

    • Identify key factors that may affect the success of data visualisation

    • Assess the data to be visualised based on the volume, cardinality, velocity and variety

    • Gather insights/stories using the relevant data visualisation techniques

    • Develop a data visualisation model that conveys the insights to the audience

Our Basic R Programming module helps you to understand operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

    • Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

    • Define analytics architecture requirements to deploy the statistical model

    • Develop the process to support the operations of the model with relevant stakeholder

    • Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to gain understanding on how to apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

    • Demonstrate understanding of data analytics lifecycle and its activities

    • Demonstrate understanding of different analytical techniques and tools to perform analytics project

    • Demonstrate understanding of the technologies used in big data analytics

    • Creating your first model in Azure Machine Learning

    • Working with probability and statistics; Simulation and hypothesis testing

    • Data munging and Visualization with Azure Machine Learning and R on Azure stack

    • K-means clustering with Azure Machine Learning

    • Create and customize visualizations using ggplot2

    • Perform predictive analytics using R

---------------------------------------------------------------------------------------------------------------------------

You may also want to study the following modular courses for bite-sized data analytics skills learning:

    • Introduction to Data Science

    • Data Queries and Visualization Basics

    • Statistical Thinking for Data Science and Analytics

    • Basic R Programming

    • Principles of Machine Learning

    • Spark on Azure HDInsight

Register above to receive more information.

Professional Diploma in Data Science (PDDS)

What is it

Our 9 months Professional Diploma in Data Science (PDDS) provides you with the following essential skills to:

• Use Microsoft Excel to explore data

• Use Transact-SQL to query a relational database

• Create data models and visualize data using Excel

• Apply statistical methods to data

• Use R to explore and transform data

• Follow a data science methodology

• Create and validate machine learning models with Azure Machine Learning

• Write R code to build machine learning models

• Apply data science techniques to common scenarios

• Implement a machine learning solution for a given data problem

What you will learn

Our Data Queries and Visualization Basics module will help you to understand and learn how to apply data visualization skills. You will be exposed to different methodologies and techniques to apply the following skills:

• Write programs using T-SQL

• Implement error handling and transactions using T-SQL

• Identify key factors that may affect the success of data visualisation

• Assess the data to be visualised based on the volume, cardinality, velocity and variety

• Gather insights/stories using the relevant data visualisation techniques

• Develop a data visualisation model that conveys the insights to the audience

Our Statistical Thinking for Data Science and Analytics module will help you to gain understanding and learn how to develop statistical model. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming

  language/tools for big data analytics tools

• Develop a report of the business insights for the relevant parties

• Use Bayesian modelling and inference for forecasting and studying public opinion

• Use Data to create compelling graphics

Our Basic R Programming module will help you to gain understanding of how to operationalise analytical models. You will be exposed to different methodologies and techniques to apply the following skills:

• Select runtime environment for the statistical model to be deployed and user requirements with the relevant stakeholders

• Define analytics architecture requirements to deploy the statistical model

• Develop the process to support the operations of the model with relevant stakeholder

• Monitor and tune the deployed model to ensure that it delivers the expected outcome and aligns with the business changes

Our Data Science Essentials module will help you to understand and apply data science and big data analytics knowledge. You will be exposed to different methodologies and techniques to apply the following skills:

• Demonstrate understanding of data analytics lifecycle and its activities

• Demonstrate understanding of different analytical techniques and tools to perform analytics project

• Demonstrate understanding of the technologies used in big data analytics

• Creating your first model in Azure Machine Learning

• Working with probability and statistics; Simulation and hypothesis testing

• Data munging and Visualization with Azure Machine Learning and R on Azure stack

• K-means clustering with Azure Machine Learning

• Create and customize visualizations using ggplot2

• Perform predictive analytics using R

Our Principles of Machine Learning module will teach you how to develop text analytics process. You will be exposed to different methodologies and techniques to apply the following skills:

• Identify text analytics solution and platform requirements

• Define the metadata and corpus for the data to be imported into the text analytics repository

• Develop a standardised set of text analytics artifacts with the relevant stakeholders

• Develop term-document frequency matrix to enable lookup of text and documents within the corpus

• Modify the text analytics solution to ensure that it produces the expected results

• Define the process to perform text analytics based on the business requirements and text analytics artifacts

• Use regularization on over-parameterized models

• Apply cross validation to estimating model performance

• Apply and evaluate k-means and hierarchical clustering models

• Apply Machine Learning models to real-life situations

Our Spark on Azure HDInsight module will help you understand how to analyse data and identify business insights, and gather data to identify business requirements. You will be exposed to different methodologies and techniques to apply the following skills:

• Review the hypothesis to address problem statement for the analytics project

• Explore the data in the analytics platform/organisation to familiarise with the data available for analysis

• Perform analysis on the data to prove/disprove the hypothesis and obtain business insights using the relevant programming language/tools for big data analytics tools

• Develop a report of the business insights for a case study

• Implement a predictive solution using Spark

• Identify and review key information sources related to the business problem / needs

• Elicit information from key stakeholders using appropriate information gathering methods

• Analyse and prioritise the business requirements to be aligned to the organisation’s directions

• Identify dependencies for the identified business requirements