Professional Diploma in Data Science
Professional Diploma in Data Science
(SCTP) WSQ Diploma in Infocomm Technology (Data)
Duration and Delivery Mode:
5 Months Full-time/ 9 Months Part-time
(Synchronous & Asynchronous E-learning)
5 Months Full-time/ 9 Months Part-time
(Synchronous & Asynchronous E-learning)
Professional Diploma in Data Science
(SCTP) WSQ Diploma in Infocomm Technology (Data)
Duration and Delivery Mode:
5 Months Full-time/ 9 Months Part-time
(Synchronous & Asynchronous E-learning)
5 Months Full-time/ 9 Months Part-time
(Synchronous & Asynchronous E-learning)
Course Brief
The “Professional Diploma in Data Science” offers learners a comprehensive understanding of the field and equips them with the necessary skills to excel in a data-driven world. Upon completion of this course, individuals can expect a wide range of job prospects and opportunities in various industries. They can pursue job roles such as Data Scientist, Data Analyst, Machine Learning Engineer, Business Analyst, and more. With the growing demand for data professionals, learners will have the chance to work with leading organizations, contribute to data-driven decision-making processes, and shape the future of businesses.
Throughout the course, learners will delve into various modules, each focusing on a key aspect of data science. In Module 1, “Python for Data Science,” learners will gain proficiency in Python programming and explore topics such as data manipulation, visualization, and packages like NumPy and Pandas. Module 2, “Data Modelling & Visualization,” introduces learners to the fundamentals of data analytics and visualization using Power BI, covering topics such as data transformation, modelling, reports, and dashboards.
Module 3, “Statistics for Data Science,” provides learners with a strong foundation in statistical concepts essential for data analysis. Topics covered include descriptive statistics, probability, random variables, sampling, confidence intervals, and hypothesis testing. Moving on to Module 4, “Data Science Principles,” learners will dive into the principles and practices of data science, including data validation and transformation, data transfer and modelling, as well as data integration on Microsoft Azure.
Module 5, “Machine Learning Algorithms and methods,” focuses on the core algorithms and methods used in machine learning. Learners will explore classification, regression, techniques for improving machine learning models, and optimization-based methods. In Module 6, “Machine Learning Applications,” learners will gain insights into real-world applications of machine learning, including supervised and unsupervised learning, deep learning, natural language processing, recommender systems, time series analysis, and reinforcement learning.
The final module, Module 7, “Data Science Modelling Project,” allows learners to apply their knowledge and skills to a comprehensive project. They will learn project planning and management, requirements elicitation, data preparation and analysis, model evaluation and selection, as well as deployment and production. Additionally, problem management, communication, and stakeholder management will be emphasized to ensure learners are equipped with essential project management skills.
Throughout this course, learners will engage in a hands-on learning experience, gaining practical skills through the instructional units within each module. By mastering Python programming, data modelling and visualization, statistical analysis, machine learning algorithms, and real-world applications, learners will acquire the necessary expertise to tackle complex data challenges.
The Professional Diploma in Data Science is designed to empower individuals with the knowledge and skills required to thrive in the data-driven landscape. With a strong foundation in Python, data modelling, statistics, machine learning algorithms, and practical application, learners will be well-prepared to enter the data science field and make meaningful contributions to organizations worldwide.
Throughout the course, learners will delve into various modules, each focusing on a key aspect of data science. In Module 1, “Python for Data Science,” learners will gain proficiency in Python programming and explore topics such as data manipulation, visualization, and packages like NumPy and Pandas. Module 2, “Data Modelling & Visualization,” introduces learners to the fundamentals of data analytics and visualization using Power BI, covering topics such as data transformation, modelling, reports, and dashboards.
Module 3, “Statistics for Data Science,” provides learners with a strong foundation in statistical concepts essential for data analysis. Topics covered include descriptive statistics, probability, random variables, sampling, confidence intervals, and hypothesis testing. Moving on to Module 4, “Data Science Principles,” learners will dive into the principles and practices of data science, including data validation and transformation, data transfer and modelling, as well as data integration on Microsoft Azure.
Module 5, “Machine Learning Algorithms and methods,” focuses on the core algorithms and methods used in machine learning. Learners will explore classification, regression, techniques for improving machine learning models, and optimization-based methods. In Module 6, “Machine Learning Applications,” learners will gain insights into real-world applications of machine learning, including supervised and unsupervised learning, deep learning, natural language processing, recommender systems, time series analysis, and reinforcement learning.
The final module, Module 7, “Data Science Modelling Project,” allows learners to apply their knowledge and skills to a comprehensive project. They will learn project planning and management, requirements elicitation, data preparation and analysis, model evaluation and selection, as well as deployment and production. Additionally, problem management, communication, and stakeholder management will be emphasized to ensure learners are equipped with essential project management skills.
Throughout this course, learners will engage in a hands-on learning experience, gaining practical skills through the instructional units within each module. By mastering Python programming, data modelling and visualization, statistical analysis, machine learning algorithms, and real-world applications, learners will acquire the necessary expertise to tackle complex data challenges.
The Professional Diploma in Data Science is designed to empower individuals with the knowledge and skills required to thrive in the data-driven landscape. With a strong foundation in Python, data modelling, statistics, machine learning algorithms, and practical application, learners will be well-prepared to enter the data science field and make meaningful contributions to organizations worldwide.
Knowledge, Skill, Ability Summary
At the end of the course, you will be able to acquire the following:
Knowledge
- Apply Python programming techniques to manipulate, analyze, and visualize complex datasets.
- Explain statistical concepts and apply appropriate techniques for data analysis and decision-making.
- Demonstrate a deep understanding of data science principles and methodologies.
- Identify and select appropriate machine learning algorithms and methods for various data-driven tasks.
- Recognize and evaluate the performance of machine learning models using appropriate metrics and techniques.
Skills
- Utilize Python programming to extract, transform, and load data for analysis and modeling.
- Perform statistical analysis to uncover patterns, trends, and relationships in data.
- Develop and implement data science workflows, including data preprocessing and feature engineering.
- Build, train, and evaluate machine learning models for prediction and classification tasks.
- Apply machine learning techniques to real-world applications, such as image recognition and natural language processing.
Ability
Effectively analyze complex data, build predictive models, and derive valuable insights to drive data-informed decision-making.Blended Learning Journey
(803.5 Hours)
E-Learning
(Async)
92 Hours
Flipped Class
(Sync)
72 Hours
Mentoring Support
(Sync)
180 Hours
Mentoring Support
(Async)
456 Hours
Assessment
(Sync)
3.5 Hours
Module Summary
Module Brief
The module “Python for Data Science” equips learners with essential knowledge and skills required to effectively use Python for data analysis and visualization. By mastering the instructional units (IU) listed below, learners will attain proficiency in Python programming, data manipulation, and visualization techniques.
The module covers fundamental concepts in Python, including Python basics, functions, conditionals, and file handling. Learners will gain expertise in utilizing Python packages for efficient data processing and analysis. They will also explore NumPy, a powerful library for numerical computations, and learn how to manipulate data using Pandas, a versatile data manipulation tool. Additionally, learners will delve into data visualization using Matplotlib and Seaborn libraries, enabling them to create insightful visual representations of data.
Through hands-on project tasks, learners will apply their knowledge of Python for data science to implement data visualization using the aforementioned Python libraries. By completing these tasks, learners will gain practical experience in creating visually appealing and informative visualizations to analyze and communicate data effectively.
By combining theoretical concepts, hands-on practice, and real-world applications, this module enables learners to harness the power of Python for data science, empowering them to extract insights, make data-driven decisions, and communicate findings through impactful visualizations.
Other Information
The module “Python for Data Science” equips learners with essential knowledge and skills required to effectively use Python for data analysis and visualization. By mastering the instructional units (IU) listed below, learners will attain proficiency in Python programming, data manipulation, and visualization techniques.
The module covers fundamental concepts in Python, including Python basics, functions, conditionals, and file handling. Learners will gain expertise in utilizing Python packages for efficient data processing and analysis. They will also explore NumPy, a powerful library for numerical computations, and learn how to manipulate data using Pandas, a versatile data manipulation tool. Additionally, learners will delve into data visualization using Matplotlib and Seaborn libraries, enabling them to create insightful visual representations of data.
Through hands-on project tasks, learners will apply their knowledge of Python for data science to implement data visualization using the aforementioned Python libraries. By completing these tasks, learners will gain practical experience in creating visually appealing and informative visualizations to analyze and communicate data effectively.
By combining theoretical concepts, hands-on practice, and real-world applications, this module enables learners to harness the power of Python for data science, empowering them to extract insights, make data-driven decisions, and communicate findings through impactful visualizations.
Other Information
- SSG Module Reference No: NA
- Module Validity Date:Â 31 Jan 2025
Module Session Plan
Module Brief
The module “Data Modelling & Visualization” provides learners with the knowledge and skills necessary to perform effective data modelling and visualization using Power BI. By engaging with the instructional units (IU) listed below, learners will gain a comprehensive understanding of data analytics and the capabilities of Power BI.
The module begins with an introduction to data analytics, equipping learners with a foundational understanding of key concepts and techniques used in data analysis. Subsequently, learners will explore Power BI Transformation, where they will acquire skills to clean and prepare data for analysis, ensuring data quality and reliability. The Power BI Data Modelling unit focuses on creating data models that establish relationships and hierarchies, enabling efficient data exploration and analysis.
Moving forward, learners will delve into Power BI Reports, learning how to design and develop visually appealing and insightful reports to communicate data findings effectively. In the final IU, Power BI Dashboards, learners will discover techniques to create interactive dashboards, allowing users to interact with data, gain insights, and make data-driven decisions.
Through project tasks, learners will implement data modelling and visualization using Power BI. By completing these tasks, learners will gain practical experience in transforming and modelling data, generating informative reports, and developing interactive dashboards. This hands-on approach enables learners to apply their knowledge in real-world scenarios, enhancing their skills in data modelling and visualization techniques using Power BI.
By mastering the concepts and techniques covered in this module, learners will be well-prepared to leverage the power of Power BI in their data analytics and visualization tasks, enabling them to extract valuable insights and drive data-informed decision-making within organizations.
Other Information
The module “Data Modelling & Visualization” provides learners with the knowledge and skills necessary to perform effective data modelling and visualization using Power BI. By engaging with the instructional units (IU) listed below, learners will gain a comprehensive understanding of data analytics and the capabilities of Power BI.
The module begins with an introduction to data analytics, equipping learners with a foundational understanding of key concepts and techniques used in data analysis. Subsequently, learners will explore Power BI Transformation, where they will acquire skills to clean and prepare data for analysis, ensuring data quality and reliability. The Power BI Data Modelling unit focuses on creating data models that establish relationships and hierarchies, enabling efficient data exploration and analysis.
Moving forward, learners will delve into Power BI Reports, learning how to design and develop visually appealing and insightful reports to communicate data findings effectively. In the final IU, Power BI Dashboards, learners will discover techniques to create interactive dashboards, allowing users to interact with data, gain insights, and make data-driven decisions.
Through project tasks, learners will implement data modelling and visualization using Power BI. By completing these tasks, learners will gain practical experience in transforming and modelling data, generating informative reports, and developing interactive dashboards. This hands-on approach enables learners to apply their knowledge in real-world scenarios, enhancing their skills in data modelling and visualization techniques using Power BI.
By mastering the concepts and techniques covered in this module, learners will be well-prepared to leverage the power of Power BI in their data analytics and visualization tasks, enabling them to extract valuable insights and drive data-informed decision-making within organizations.
Other Information
- SSG Module Reference No: NA
- Module Validity Date:Â 31 Jan 2025
Module Session Plan
Module Brief
The module “Statistics for Data Science” equips learners with the essential knowledge and skills required to perform statistical analysis and inference in data science. By engaging with the instructional units (IU) listed below, learners will attain a solid foundation in descriptive statistics, probability theory, random variables, sampling, confidence intervals, and hypothesis testing.
The module begins with Descriptive Statistics, where learners will acquire the skills to summarize and describe datasets using measures such as mean, median, and standard deviation. Basic Probability introduces learners to the fundamental concepts of probability theory, enabling them to analyze uncertain events and calculate probabilities.
Moving forward, learners will explore Random Variables – I and Random Variables – II, where they will delve into the principles of probability distributions and their applications in data analysis. In Sampling and Confidence Intervals, learners will understand the importance of sampling techniques and learn to estimate population parameters with confidence intervals.
The module concludes with Hypothesis Testing – I, where learners will learn how to formulate and test hypotheses using statistical tests, making informed decisions based on data analysis. Through hands-on project tasks, learners will implement statistical analysis tasks, applying descriptive statistics, probability, and hypothesis testing techniques to real-world datasets.
By mastering the concepts and techniques covered in this module, learners will be equipped with the necessary statistical skills to effectively analyze and interpret data in various data science applications. These skills are essential for making data-driven decisions and drawing meaningful insights from data.
Other Information
The module “Statistics for Data Science” equips learners with the essential knowledge and skills required to perform statistical analysis and inference in data science. By engaging with the instructional units (IU) listed below, learners will attain a solid foundation in descriptive statistics, probability theory, random variables, sampling, confidence intervals, and hypothesis testing.
The module begins with Descriptive Statistics, where learners will acquire the skills to summarize and describe datasets using measures such as mean, median, and standard deviation. Basic Probability introduces learners to the fundamental concepts of probability theory, enabling them to analyze uncertain events and calculate probabilities.
Moving forward, learners will explore Random Variables – I and Random Variables – II, where they will delve into the principles of probability distributions and their applications in data analysis. In Sampling and Confidence Intervals, learners will understand the importance of sampling techniques and learn to estimate population parameters with confidence intervals.
The module concludes with Hypothesis Testing – I, where learners will learn how to formulate and test hypotheses using statistical tests, making informed decisions based on data analysis. Through hands-on project tasks, learners will implement statistical analysis tasks, applying descriptive statistics, probability, and hypothesis testing techniques to real-world datasets.
By mastering the concepts and techniques covered in this module, learners will be equipped with the necessary statistical skills to effectively analyze and interpret data in various data science applications. These skills are essential for making data-driven decisions and drawing meaningful insights from data.
Other Information
- SSG Module Reference No: NA
- Module Validity Date:Â 31 Jan 2025
Module Session Plan
Module Brief
The module “Data Science Principles” equips learners with the essential knowledge and skills required to understand and apply key principles in the field of data science. By engaging with the instructional units (IU) listed below, learners will attain proficiency in data validation, data transformation, data modelling, data integration on Microsoft Azure, and the deployment and management of data science solutions.
The module begins with an Introduction to Data Science, where learners will gain a comprehensive understanding of the fundamental concepts, methodologies, and applications in data science. Data validation and transformation are then covered, providing learners with the skills to assess data quality, identify and handle missing or inconsistent data, and transform data into a suitable format for analysis.
Moving forward, learners will explore data transfer and modelling, where they will learn techniques for efficient data transfer and modelling processes. Data integration on Microsoft Azure introduces learners to the principles and practices of integrating diverse data sources and leveraging Azure services for seamless data workflows.
The module concludes with a focus on the deployment and management of data science solutions. Learners will acquire knowledge and skills in deploying and managing data science models and solutions, ensuring efficient operations and scalability.
Through project tasks, learners will implement Data Science Principles, applying data validation, modelling, integration, and deployment techniques to real-world scenarios. By completing these tasks, learners will gain practical experience in assessing and transforming data, creating robust data models, integrating data on Microsoft Azure, and deploying data science solutions effectively.
By mastering the concepts and techniques covered in this module, learners will be well-equipped to apply Data Science Principles effectively in various domains. These skills are crucial for extracting insights, making data-driven decisions, and contributing to the success of data-driven organizations.
Other Information
The module “Data Science Principles” equips learners with the essential knowledge and skills required to understand and apply key principles in the field of data science. By engaging with the instructional units (IU) listed below, learners will attain proficiency in data validation, data transformation, data modelling, data integration on Microsoft Azure, and the deployment and management of data science solutions.
The module begins with an Introduction to Data Science, where learners will gain a comprehensive understanding of the fundamental concepts, methodologies, and applications in data science. Data validation and transformation are then covered, providing learners with the skills to assess data quality, identify and handle missing or inconsistent data, and transform data into a suitable format for analysis.
Moving forward, learners will explore data transfer and modelling, where they will learn techniques for efficient data transfer and modelling processes. Data integration on Microsoft Azure introduces learners to the principles and practices of integrating diverse data sources and leveraging Azure services for seamless data workflows.
The module concludes with a focus on the deployment and management of data science solutions. Learners will acquire knowledge and skills in deploying and managing data science models and solutions, ensuring efficient operations and scalability.
Through project tasks, learners will implement Data Science Principles, applying data validation, modelling, integration, and deployment techniques to real-world scenarios. By completing these tasks, learners will gain practical experience in assessing and transforming data, creating robust data models, integrating data on Microsoft Azure, and deploying data science solutions effectively.
By mastering the concepts and techniques covered in this module, learners will be well-equipped to apply Data Science Principles effectively in various domains. These skills are crucial for extracting insights, making data-driven decisions, and contributing to the success of data-driven organizations.
Other Information
- SSG Module Reference No: NA
- Module Validity Date:Â 31 Jan 2025
Module Session Plan
Module Brief
The module “Machine Learning Algorithms and Methods” equips learners with the knowledge and skills required to understand and apply various machine learning techniques. By engaging with the instructional units (IU) listed below, learners will attain proficiency in classification, regression, improving machine learning models, tree and ensemble methods, and optimization-based methods.
The module begins with an exploration of classification techniques, where learners will gain an understanding of algorithms used to predict categorical outcomes. Regression techniques are then covered, providing learners with the skills to predict continuous variables. Learners will also delve into improving machine learning models by learning methods for tuning hyperparameters and optimizing model performance.
Moving forward, learners will explore tree and ensemble methods, which enable more accurate predictions and enhance model robustness through combining multiple models. Optimization-based methods will be introduced, allowing learners to tackle complex machine learning problems by leveraging optimization algorithms.
Through project tasks, learners will implement machine learning algorithms and methods covered in the module. They will apply classification techniques to predict categorical outcomes, utilize regression techniques to predict continuous variables, improve machine learning models by tuning hyperparameters, and employ tree and ensemble methods for enhanced predictions. Additionally, learners will explore optimization-based methods to solve complex machine learning problems effectively.
By mastering the concepts and techniques covered in this module, learners will be well-equipped to apply machine learning algorithms and methods effectively in various domains. These skills are vital for developing accurate predictive models, making data-driven decisions, and contributing to the advancement of machine learning applications.
Other Information
The module “Machine Learning Algorithms and Methods” equips learners with the knowledge and skills required to understand and apply various machine learning techniques. By engaging with the instructional units (IU) listed below, learners will attain proficiency in classification, regression, improving machine learning models, tree and ensemble methods, and optimization-based methods.
The module begins with an exploration of classification techniques, where learners will gain an understanding of algorithms used to predict categorical outcomes. Regression techniques are then covered, providing learners with the skills to predict continuous variables. Learners will also delve into improving machine learning models by learning methods for tuning hyperparameters and optimizing model performance.
Moving forward, learners will explore tree and ensemble methods, which enable more accurate predictions and enhance model robustness through combining multiple models. Optimization-based methods will be introduced, allowing learners to tackle complex machine learning problems by leveraging optimization algorithms.
Through project tasks, learners will implement machine learning algorithms and methods covered in the module. They will apply classification techniques to predict categorical outcomes, utilize regression techniques to predict continuous variables, improve machine learning models by tuning hyperparameters, and employ tree and ensemble methods for enhanced predictions. Additionally, learners will explore optimization-based methods to solve complex machine learning problems effectively.
By mastering the concepts and techniques covered in this module, learners will be well-equipped to apply machine learning algorithms and methods effectively in various domains. These skills are vital for developing accurate predictive models, making data-driven decisions, and contributing to the advancement of machine learning applications.
Other Information
- SSG Module Reference No: NA
- Module Validity Date:Â 31 Jan 2025
Module Session Plan
Module Brief
The “Machine Learning Applications” module equips learners with essential knowledge and skills in a wide range of machine learning techniques and their practical applications. Through the instructional units (IU) covered in this module, learners will gain proficiency in various areas of machine learning.
The module begins with an introduction to machine learning applications, providing learners with a solid foundation and understanding of the fundamental concepts and principles. They will then delve into supervised learning, where they will learn how to train models to make predictions and classify data based on labelled examples. Next, learners will explore unsupervised learning, discovering techniques to identify patterns and structures in unlabelled data.
Deep learning, a subfield of machine learning, is a key focus of this module. Learners will acquire knowledge of neural networks and learn how to build deep learning models capable of handling complex pattern recognition tasks and extracting meaningful features from data. Additionally, learners will delve into natural language processing, exploring techniques to analyze and process text data, enabling them to develop applications that can understand and generate human language.
The module also covers recommender systems, where learners will learn how to build personalized recommendation engines that provide relevant suggestions based on user preferences and behaviours. Time series analysis, essential for analyzing and forecasting time-dependent data, is another crucial area covered in this module. Lastly, learners will be introduced to reinforcement learning, exploring techniques to build intelligent agents capable of making optimal decisions based on feedback from their environment.
By the end of this module, learners will have acquired the skills to implement various machine learning applications. They will be able to apply supervised and unsupervised learning algorithms, build and train deep learning models, process and analyze natural language data, develop recommendation systems, perform time series analysis, and utilize reinforcement learning techniques. These skills will enable learners to address real-world challenges and contribute to the growing field of machine learning applications.
Other Information
The “Machine Learning Applications” module equips learners with essential knowledge and skills in a wide range of machine learning techniques and their practical applications. Through the instructional units (IU) covered in this module, learners will gain proficiency in various areas of machine learning.
The module begins with an introduction to machine learning applications, providing learners with a solid foundation and understanding of the fundamental concepts and principles. They will then delve into supervised learning, where they will learn how to train models to make predictions and classify data based on labelled examples. Next, learners will explore unsupervised learning, discovering techniques to identify patterns and structures in unlabelled data.
Deep learning, a subfield of machine learning, is a key focus of this module. Learners will acquire knowledge of neural networks and learn how to build deep learning models capable of handling complex pattern recognition tasks and extracting meaningful features from data. Additionally, learners will delve into natural language processing, exploring techniques to analyze and process text data, enabling them to develop applications that can understand and generate human language.
The module also covers recommender systems, where learners will learn how to build personalized recommendation engines that provide relevant suggestions based on user preferences and behaviours. Time series analysis, essential for analyzing and forecasting time-dependent data, is another crucial area covered in this module. Lastly, learners will be introduced to reinforcement learning, exploring techniques to build intelligent agents capable of making optimal decisions based on feedback from their environment.
By the end of this module, learners will have acquired the skills to implement various machine learning applications. They will be able to apply supervised and unsupervised learning algorithms, build and train deep learning models, process and analyze natural language data, develop recommendation systems, perform time series analysis, and utilize reinforcement learning techniques. These skills will enable learners to address real-world challenges and contribute to the growing field of machine learning applications.
Other Information
- SSG Module Reference No: NA
- Module Validity Date: 31 Jan 2025
Module Session Plan
Module Brief
The “Data Science Modelling Project” module enables learners to attain knowledge and skills in various areas of data science project implementation. The instructional units covered in this module include project planning and management, requirements elicitation, data preparation and analysis, model evaluation and selection, deployment and production, problem management, and communication and stakeholder management.
Through this module, learners will engage in a hands-on data science project where they will apply their knowledge and skills to tackle real-world challenges. They will be involved in the entire project lifecycle, starting from planning and managing the project to effectively communicating with stakeholders. Learners will gain expertise in eliciting requirements, preparing and analyzing data, evaluating and selecting appropriate models, and deploying them in a production environment.
The module will also focus on problem management, equipping learners with strategies to address challenges that may arise during the project. Effective communication and stakeholder management skills will be developed to ensure clear and consistent collaboration throughout the project.
By implementing a data science modelling project, learners will enhance their practical experience and develop a strong understanding of how to apply data science principles in real-world scenarios. They will gain the ability to deliver valuable insights and solutions through the use of advanced data science techniques, making them well-equipped for data-driven decision-making processes.
Other Information
The “Data Science Modelling Project” module enables learners to attain knowledge and skills in various areas of data science project implementation. The instructional units covered in this module include project planning and management, requirements elicitation, data preparation and analysis, model evaluation and selection, deployment and production, problem management, and communication and stakeholder management.
Through this module, learners will engage in a hands-on data science project where they will apply their knowledge and skills to tackle real-world challenges. They will be involved in the entire project lifecycle, starting from planning and managing the project to effectively communicating with stakeholders. Learners will gain expertise in eliciting requirements, preparing and analyzing data, evaluating and selecting appropriate models, and deploying them in a production environment.
The module will also focus on problem management, equipping learners with strategies to address challenges that may arise during the project. Effective communication and stakeholder management skills will be developed to ensure clear and consistent collaboration throughout the project.
By implementing a data science modelling project, learners will enhance their practical experience and develop a strong understanding of how to apply data science principles in real-world scenarios. They will gain the ability to deliver valuable insights and solutions through the use of advanced data science techniques, making them well-equipped for data-driven decision-making processes.
Other Information
- SSG Module Reference No: NA
- Module Validity Date:Â 31 Jan 2025
Module Session Plan
Target Audience & Prerequisite
Target Audience
- Individuals who are aiming to acquire data science and data analytics skills specifically for the purpose of securing employment in the digital science/data analytics field.
Prerequisite
- Academic: Minimum one credit in O Level or its equivalent
- English Proficiency: Minimum IELTS 5.5 or its equivalent
- Age: Minimum 21 years
- Work Experience: Minimum 1 year experience in any business process
Graduation Requirements
Each learner must meet the following requirements to secure academic qualifications and eduCLaaS job role certification.
- Minimum 75% attendance in all sessions.
- Minimum pass grade in the summative assessment of each module
Certificates
Academic Qualification
WSQ Diploma in Infocomm Technology (Data) awarded by SSG
WSQ Diploma in Infocomm Technology (Data) awarded by SSG
Statement of Attainment
WSQ Statistics for Data Science (SF)
ICT-DIT-4001-1.1 Analytics and Computational Modelling
- WSQ Python for Data Science (SF)
- ICT-SNA-4009-1.1 Data strategy
- WSQ Data Modelling & Visualization (SF)
- ICT-DIT-4006-1.1 Data Visualization
- WSQ Data science principles (SF)
- ICT-DIT-4005-1.1 Data Engineering
- WSQ Machine Learning Algorithms and methods (SF)
- ICT-SNA-4011-1.1 Emerging Technology Synthesis
- WSQ Machine Learning Applications (SF)
- ICT-DES-4001-1.1 Data Design
- WSQ Data Science Modelling Project (SF)
- ICT-PMT-4001-1.1 Business Needs Analysis
- ICT-OUS-3011-1.1 Problem Management
Industry Skills Certification
- WSQ Data Modelling & Visualization (SF) Analyzing Data with Microsoft Power BI
- WSQ Data Science Principles (SF) Microsoft Certified: Azure Data Fundamentals
EduCLaaS Job Role Certification
Data Scientist / Data Analyst
Data Scientist / Data Analyst
Other Information
SSG Course Reference No: TGS-2019503390
Course Validity Date: 31 Jan 2025
Course Developer: Lithan Academy
Course Validity Date: 31 Jan 2025
Course Developer: Lithan Academy