Facebook Professional Diploma in Data Science - Products - Lithan Skip to Content

Product Product

Professional Diploma in Data Science

NICF - Diploma In Business Analytics Data Science

  • PDF
    Brochures
  • PDF
    Videos
Share This

Course Outcome

Acquire Basic R programming & Azure Machine Learning Skills
Learn data science fundamentals, statistical modelling, key data science tools, and R programming
Training

Get Trained

Understand Data Science concepts and learn how to create data models and visualize data using Excel; Create and validate machine learning models with Azure Machine Learning; Build machine learning models using R.
Training

Get Experiential

With the guidance of industry practitioners, Apply data science techniques to common scenarios and Implement a machine learning solution for a given data problem
Training

Get Hired

Get placed as a Data Scientist in an Enterprise during or after the course
Training

Get Certified

Upon successful course completion, the learner will receive the following:

  • 7 NICF Statements of Attainment (SOA)
  • NICF Diploma in Business Analytics 
  • Verified Certificate for all MS courses on EdX

How You Learn

How You Learn?

Learning & Course Duration
When you enrol, you will commit to the following learning hours.

Learning Duration (GLH*) - 309 Hours

On Campus Hours
79 Hours
Off Campus Hours
230 hours

Course Duration with Study Options

Full Time

3 Months

5 hrs a day (5 days/week) 

Part Time 

9 Months 

3 hrs a day(3 days/week)

*GLH: Guided Learning Hours

Course Structure

Course Structure

NICF - Diploma In Business Analytics

Accelerate your career in data science!

 

Learn data science fundamentals, key data science tools, and widely-used programming languages from industry and academic experts in this unique program created by Microsoft.

 

Built in collaboration with leading universities and employers, the Microsoft Professional Program Certificate in Data Science will develop the analytical and programming skills you need to take advantage of the 1.5 million career opportunities available now in data science. By the end of the course the learner can:

  • 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

 

This course consists of following modules:

  • Data Queries & Visualization Basics
  • Statistical Thinking for Data Science & Analytics
  • Basic R Programming
  • Data Science Essentials
  • Principles of Machine Learning
  • Spark on Azure HDInsights

Course Modules :

Outline Outline Schedule Schedule

DQVB - NICF - Data Queries and Visualization Basics

Outline
Schedule
Module Name

NICF - Data Queries and Visualization Basics

Session Outline Activities

Session 1 : Orientation & Lecture - Live (3 Hours)

The student has to attend a Live Orientation Session conducted at LA campus or go through a recording of the Orientation Session.

After the Orientation, the Lecturer will introduce the module by conducting the lectures on:

  • IU1: The Data Science Curriculum 
  • IU2: Data Science Fundamentals
  • IU3: A Basic Introduction to Statistics
  • IU4: Data Analysis using excel

Before session: The Students will engage in E- Learning to learn IU1, IU2 , IU3 & IU4 

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 2: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU1 – IU4

Session 3: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU5 - IU7:

  • IU5: Queries & Data Model using excel
  • IU6 : DAX function and advanced query function in excel
  • IU7 : Visualisations using excel

Before session:The Student will engage in e-learning to LEARN IU5 - IU7  (off campus - 2 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 4: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU7 & IU8:

  • IU8:Introduction to Transact-SQL
  • IU9: Querying Tables with SELECT

Before session:The Student will engage in e-learning to LEARN IU8 & IU9 (off campus - 2 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 5: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU9 & IU10:

  • IU10: Querying Multiple Tables with Joins 
  • IU11: Using Set Operators
  • IU12:Using functions and Aggregating Data

Before session:The Student will engage in e-learning to LEARN IU10 - IU12 (off campus - 3 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 6: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU11 & IU12:

  • IU13: Using Subqueries and Apply
  • IU14: Using Table Expressions
  • IU15: Grouping Sets and Piviting Data

Before session:The Student will engage in e-learning to LEARN IU13 -IU15 (off campus - 1.5 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 7: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU13 & IU14:

  • IU16:Modifying Data
  • IU17:Programming with Transact - SQL
  • IU18:Error Handling and Transactions

Before session:The Student will engage in e-learning to LEARN IU13 & IU14 (off campus - 3 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 8: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 1and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 2  of the project
  • Tutor will provide necessary feedback & guidance

Before session:

  • Student will be given a project brief  which comprises few tasks in Cloud Dev Environment
  • Student should complete the tasks up to the milestone 1 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 9: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 2 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 3  of the project
  • Tutor will provide necessary feedback & guidance

Before session:

  • Student should complete the task up to the milestone 2 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 10: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 3 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 3  of the project
  • Tutor will provide necessary feedback & guidance

Before session:

  • Student should complete the task up to the milestone 3 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 11: Summative Assessment (30 mins)

Assessor will review and provide feedback

During session: The Student should summarise the project output and answer questions posed by the Assessor

Module Name

NICF - Data Queries and Visualization Basics

Competency Unit

IT-BDA-402S-1 : Apply Data Visualization (3 Credit Values)

By the end of this module, the learner should be able to gain the following knowledge:

  • Foundational statistics that can be used to analyze data
  • Syntax of Transact-SQL, working with data types, tables and manipulating data using T-SQL
  • How to program using Transact-SQL
  • Understand different types of data visualisation techniques and the types of data suitable for using the techniques
  • Learn how to perform visual analysis to gather insights
  • Understand Data visualisation tools like Excel
  • Understand the anatomy of a data visualisation
  • Understand Visualisation design methodology and process with Excel

By the end of this module, the learner should be able 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

STDSA - NICF - Statistical Thinking for Data Science and Analytics

Outline
Schedule
Module Name

NICF - Statistical Thinking for Data Science and Analytics

Session Outline Activities

Session 1: Orientation & Lecture - Live (3 hrs)

The student has to attend a Live Orientation Session conducted at LA campus or go through a recording of the Orientation Session.

 

After the Orientation, the Lecturer will introduce the module by conducting the lecture on:

  • IU1:  Introduction to Data Science 

After session: The Student will engage in e-learning to LEARN IU1.

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 2: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU1

Session 3: Assignment–Off campus (3 hrs)

The Student will PERFORM activities related to IU2:

  • IU2: Statistics and Probability - 1

Before session:The Student will engage in e-learning to LEARN IU2 (off campus - 3 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 4: Assignment–Off campus (3 hrs)

The Student will PERFORM activities related to IU3:

  • IU3: Statistics and Probability - 2

Before session:The Student will engage in e-learning to LEARN IU3 (off campus - 3 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 5: Assignment–Off campus (3 hrs)

The Student will PERFORM activities related to IU4:

  • IU4: Exploratory Data Analysis and Visualization 

Before session:The Student will engage in e-learning to LEARN IU4 (off campus - 3 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 6: Assignment–Off campus (3 hrs)

The Student will PERFORM activities related to IU5:

  • IU5:Introduction to Bayesian Modeling

Before session:The Student will engage in e-learning to LEARN IU5  (off campus - 2 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 7: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 1 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 2  of the project
  • Tutor will provide necessary feedback & guidance

Before session:

  • Student will be given a project brief  which comprises few tasks in Cloud Dev Environment
  • Student should complete the tasks up to the milestone 1 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 8: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 2 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 3  of the project
  • Tutor will provide necessary feedback & guidance

Before session:

  • Student should complete the task up to the milestone 2 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 9: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 3 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 3  of the project
  • Tutor will provide necessary feedback & guidance

Before session:

  • Student should complete the task up to the milestone 3 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 10: Summative Assessment (30 mins)

Assessor will review and provide feedback

During session: The Student should summarise the project output and answer questions posed by the Assessor

Module Name

NICF - Statistical Thinking for Data Science and Analytics

Competency Unit

IT-STM-401S-1 : Develop Statistical Model (4 Credit Values)

By the end of this module, the learner should be able to gain the following knowledge:

  • Understand the metrics to be analysed and objectives of the metrics
  • Statistical modelling techniques
  • Programming language and tools for big data analytics and how they integrate with big data technologies
  • Current and emerging trends in the business domain
  • Concepts of computing used in big data analytics
  • Understand the meaning of the data in different data sources
  • Understand Data collection, analysis and inference processes
  • Understand how to classify data to identify key traits and customers
  • Understand how to judge the probability of an event, based on certain conditions
  • Understand basics of Linear Regression

By the end of this module, the learner should be able 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 modeling and inference for forecasting and studying public opinion
  • Use Data to create compelling graphics

BRP - NICF - Basic R Programming

Outline
Schedule
Module Name

NICF - Basic R Programming

Session Outline Activities

Session 1: Orientation & Lecture - Live (3 hrs)

The student has to attend a Live Orientation Session conducted at LA campus or go through a recording of the Orientation Session.

After the Orientation, the Lecturer will introduce the module by conducting the lectures on:

  • IU 1:Analytics Enviornment on MS AzureMachine Learning
  • IU 2: Introduction to R-programming

After session: The Student will engage in e-learning to LEARN IU1 & IU 2 (off campus - 3 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 2: Assignment–Off campus (3 hrs)

The Student will PERFORM activities related to IU1 & IU2

Session 3: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU3 & IU4

  • IU3: Vectors
  • IU 4: Matrices

Before session:The Student will engage in e-learning to LEARN IU3 & IU 4 (off campus - 4.5 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 4: Assignment–Off campus (3 hrs)

The Student will PERFORM activities related to IU5 & IU6:

  • IU5: Factors
  • IU 6: Data Frames

Before session:The Student will engage in e-learning to LEARN IU5 & IU6 (off campus - 3 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 5: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU 7:

  • IU 7: Lists

Before session:The Student will engage in e-learning to LEARN IU 7(off campus -1.5 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 6: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU8:

  • IU8: Basic Graphics

Before session:The Student will engage in e-learning to LEARN IU8  (off campus - 3 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 7: Tutoring - Live (1.5 hrs)

The lecturer will conduct lectures on topics related to IU9:

  • IU9: Operationalizing and improving models using R

After session: The Student will PERFORM activities related to IU9 (off campus - 1.5 hrs)

Session 8: Tutoring - Live (2.5 hrs)

Tutor  will conduct a project orientation and explain the project brief, guidelines and expectations. 

After session:

  • Student will be given a project brief  which comprises few tasks in Cloud Dev Environment
  • Student should complete the tasks up to the milestone 1 (on / off campus - 2.5 hrs)
  • Student can seek online support if needed

Session 9: Tutoring - Live (2.5 hrs)

  • Student should present the project up to milestone 1 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 2  of the project
  • Tutor will provide necessary feedback & guidance

After session:

  • Student should complete the task up to the milestone 2 (on / off campus - 2.5 hrs)
  • Student can seek online support if needed

Session 10: Tutoring - Live (2.5 hrs)

  • Student should present the project up to milestone 2 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 3  of the project
  • Tutor will provide necessary feedback & guidance

After session:

  • Student should complete the task up to the milestone 3 (on / off campus - 2.5 hrs)
  • Student can seek online support if needed

Session 11: Summative Assessment (30 mins)

Assessor will review and provide feedback

Module Name

NICF - Basic R Programming

Competency Unit

IT-BDA-403S-1 : Operationalise Analytical Models (4 Credit Values)

By the end of this module, the learner should be able to gain the following knowledge:

  • Analytics architecture
  • Introductory R language fundamentals and basic syntax
  • Basics of R and how it’s used to perform data analysis
  • Introductory R language fundamentals and basic syntax and data structures
  • Creating functions and using control flow.
  • Work with data in R

By the end of this module, the learner should be able 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

DSE - NICF - Data Science Essentials

Outline
Schedule
Module Name

NICF - Data Science Essentials

Session Outline Activities

Session 1: Orientation & Lecture - Live (3 hrs)

The student has to attend a Live Orientation Session conducted at LA campus or go through a recording of the Orientation Session.

After the Orientation, the Lecturer will introduce the module by conducting the lectures on:

  • IU1: Introduction to Data Science
  • IU2: Probability and Statistics

 

After session: The Student will engage in e-learning to LEARN IU1, IU2 (off campus - 1 hour)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 2: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU1 &  IU2

Session 3: Tutoring - Live (3 Hours)

Tutor  will conduct a tutoring session on IU3 & IU4:

  • IU 3: Simulation and Hypothesis Testing 
  • IU 4: Data Exploration and Visualisation

After session: The Student will engage in e-learning to LEARN IU3 &  IU4 (off campus - 2 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 4 : Assignment Off Campus (2 Hours)

The student will perform activities related to IU 3 & IU 4

Session 5: Tutoring - Live (1 Hour)

Tutor  will conduct a tutoring session on IU5 & IU6:

  • IU 5:Data Cleaning and transformation
  • IU 6: Intro to Machine learning 

After session: The Student will engage in e-learning to LEARN IU5 &  IU6 (off campus - 2 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 6 : Assignment - Off campus (2 Hours)

The Student will perform activities related to IU 5 & IU 6

Session 7 : Assignment - Off campus (2 Hours)

The student will perform activities related to IU 7:

  • IU 7: Introduction to R-Programming for Data science 

Before session: The Student will engage in e-learning to LEARN IU7 (off campus - 1 hr)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 8: Assignment - Off Campus (2 Hours)

The student will perform activities related to IU 8:

  • IU 8: Functions

Before session: The Student will engage in e-learning to LEARN IU8 (off campus - 1 hr)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 9: Assignment - Off Campus (3 Hours)

The student will perform activities related to IU 9:

  • IU 9: Control Flow and Loops

Before session: The Student will engage in e-learning to LEARN IU 9 (off campus - 1 hr)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 10 : Assignment - Off Campus (2 Hours)

The student will perform activities related to IU 10:

  • IU 10: Working with vectors and matrices 

Before session: The Student will engage in e-learning to LEARN IU 10 (off campus - 1 hr)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 11: Assignment - Off campus (3 Hours)

The student will perform activities related to IU 11 & IU 12:

  • IU 11: Reading in Data and writing Data
  • IU 12: Reading from SQL server

Before session: The Student will engage in e-learning to LEARN IU 11 & IU 12 (off campus - 2 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 12: Assignment - Off Campus (2 Hours)

The student will perform activities related to IU 13:

  • IU 13: Working with Data and Manipulating Data

Before session: The Student will engage in e-learning to LEARN IU 13 (off campus - 1 hour)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 13: Assignment - Off Campus (2 Hours)

The student will perform activities related to IU 14 :

  • IU 14: Simulation

Before session: The Student will engage in e-learning to LEARN IU 14 (off campus - 1 hour)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 14: Assignment - Off Campus (2 Hours)

The student will perform activities related to IU 15:

  • IU 15: Linear Model

Before session: The Student will engage in e-learning to LEARN IU 15 (off campus - 1 hour)

The Student should complete self-assessment

Student can approach Online Support for any queries or guidance

Session 15: Tutoring - Live (3 hrs)

Tutor  will conduct a project orientation and explain the project brief, guidelines and expectations. 

After session: 

  • The Student will engage in e-learning to LEARN IU 15 (off campus - 1 hour)
  • Student will be given a project brief  which comprises few tasks in Cloud Dev Environment
  • Student should complete the tasks up to the milestone 1 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 16: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 1 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 2  of the project
  • Tutor will provide necessary feedback & guidance

After session:

  • Student should complete the task up to the milestone 2 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 17: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 2 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 3  of the project
  • Tutor will provide necessary feedback & guidance

After session:

  • Student should complete the task up to the milestone 3 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 18: Summative Assessment (30 mins)

Assessor will review and provide feedback

Module Name

NICF - Data Science Essentials

Competency Unit

IT-BDA-301S-1 : Apply data science and big data analytics knowledge (3 Credit Values)

By the end of this module, the learner should be able to gain the following knowledge:

  • Understand Big data analytics roles and responsibilities
  • Understand Data analytics lifecycle / Data Science process
  • Understand Analytical technique and tools
  • Understand High level ecosystem of technologies and tools for big data analytics
  • Understand Data Exploration and Visualization
  • Understand Data Ingestion, cleansing and transformation processes
  • Understand how to perform predictive analysis with R programming

By the end of this module, the learner should be able 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

PML - NICF - Principles of Machine Learning

Outline
Schedule
Module Name

NICF - Principles of Machine Learning

Session Outline Activities

Session 1: Orientation & Lecture - Live (3 hrs)

The student has to attend a Live Orientation Session conducted at LA campus or go through a recording of the Orientation Session.

After the Orientation, the Lecturer will introduce the module by conducting the lectures on:

  • IU1: Introduction to Machine Learning & Text analytics
  • IU2: Introduction to Classification

 

Before session:The Student will engage in e-learning to LEARN IU1 & IU2 (off campus - 2.5 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 2: Assignment–Off campus (1 hr)

The Student will PERFORM activities related to IU1 &  IU2

Session 3: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU3 &  IU4:

  • IU 3: Building Classification Models
  • IU 4: Introduction to Regression

 

Before session:The Student will engage in e-learning to LEARN IU 3 & IU 4(off campus - 2 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 4: E - Learning (3 hrs)

The student will engage in e- learning to learn IU 5 & IU 6 :

  • IU 5: Creating Regression Models
  • IU 6: Principles of Model Improvement

Session 5: Assignment–Off campus (3 hrs)

The Student will PERFORM activities related to IU 7:

  • IU 7: Techniques for Model Imrovement

Before session:The Student will engage in e-learning to LEARN IU7  (off campus - 1.5 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 6: Assignment (3 hrs)

The Student will PERFORM activities related to IU 8:

  • IU 8:Trees and Ensemble methods

Before session:The Student will engage in e-learning to LEARN IU8  (off campus - 1.5 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 7: Assignment - Off campus (3 hrs)

The Student will PERFORM activities related to IU 9::

  • IU 9: Neural Networks and Support Vector Machines 

Before session:The Student will engage in e-learning to LEARN IU9  (off campus - 1.5 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 8: Assignment - Off campus (3 hrs)

The Student will PERFORM activities related to IU10 & 11:

  • IU 10: Clustering
  • IU 11: Recommenders

Before session:The Student will engage in e-learning to LEARN IU10 & IU 11 (off campus - 2 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 9: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 1 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 2  of the project
  • Tutor will provide necessary feedback & guidance

Before session:

  • Student will be given a project brief  which comprises few tasks 
  • Student should complete the tasks up to the milestone 1 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 10: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 2 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 3  of the project
  • Tutor will provide necessary feedback & guidance

Before session:

  • Student should complete the task up to the milestone 2 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 11: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 3 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 3  of the project
  • Tutor will provide necessary feedback & guidance

Before session:

  • Student should complete the task up to the milestone 3 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 12: Summative Assessment (30 mins)

Assessor will review and provide feedback

During session: The Student should summarise the project output and answer questions posed by the Assessor

Module Name

NICF - Principles of Machine Learning

Competency Unit

IT-TA-401S-1 : Develop text analytics process (4 Credit Values)

By the end of this module, the learner should be able to gain the following knowledge:

  • Understand different text analytics solutions
  • Understand text analytics process and artifacts
  • Understand text mining techniques and how to apply them
  • Understand the operation of classifiers and how to use logistic regression as a classifier
  • Understand the metrics used to evaluate classifiers and regression models
  • Understand the operation of regression models and how to use linear regression for prediction and forecasting
  • Understand the problems of over-parameterization and dimensionality
  • Understand how and when to use common supervised machine learning models

By the end of this module, the learner should be able 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

SAHDI - NICF - Spark on Azure HDInsight

Outline
Schedule
Module Name

NICF - Spark on Azure HDInsight

Session Outline Activities

Session 1: Orientation & Lecture - Live (3 hrs)

The student has to attend a Live Orientation Session conducted at LA campus or go through a recording of the Orientation Session.

After the Orientation, the Lecturer will introduce the module by conducting the lecture on:

  • IU1: Introduction to Spark 

 

After session: The Student will engage in e-learning to LEARN IU1 (off campus - 3 hours)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 2: Assignment – Off campus (1 hr.)

The Student will PERFORM activities related to IU1

Session 3: Assignment–Off campus (2 hrs)

The Student will PERFORM activities related to IU2

  • IU2: Exploring Data with Spark

Before session:The Student will engage in e-learning to LEARN IU2 (off campus - 3 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 4: Assignment–Off campus (3 hrs)

The Student will PERFORM activities related to IU3:

  • IU3: Machine Learning in Spark & Data Preparation

Before session:The Student will engage in e-learning to LEARN IU3 (off campus - 1.5 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 5: Assignment–Off campus (3 hrs)

The Student will PERFORM activities related to IU4 & IU5:

  • IU4: Machine Learning Models
  • IU5: Build Machine Learning Solutions in Spark

Before session:The Student will engage in e-learning to LEARN IU4 & IU5 (off campus - 4 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 6: Assignment–Off campus (3 hrs)

The Student will PERFORM activities related to IU6 & IU7:

  • IU6: R Server on Spark 
  • IU7: Machine Learning with R-Server on Spark 

Before session:The Student will engage in e-learning to LEARN IU6 & IU7  (off campus - 3.5 hrs)

The Student should complete self-assessments

Student can approach Online Support for any queries or guidance

Session 7: Tutoring - Live (3 hrs)

Tutor  will conduct a project orientation and explain the project brief, guidelines and expectations. 

After session: 

  • Student will be given a project brief  which comprises few tasks in Cloud Dev Environment
  • Student should complete the tasks up to the milestone 1 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 8: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 1 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 2  of the project
  • Tutor will provide necessary feedback & guidance

After session:

  • Student should complete the task up to the milestone 2 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 9: Tutoring - Live (3 hrs)

  • Student should present the project up to milestone 2 and seek tutor’s feedback.
  • Student can seek support & guidance for the milestone 3  of the project
  • Tutor will provide necessary feedback & guidance

After session:

  • Student should complete the task up to the milestone 3 (on / off campus - 3 hrs)
  • Student can seek online support if needed

Session 10: Summative Assessment (30 mins)

Assessor will review and provide feedback

During session: The Student should summarise the project output and answer questions posed by the Assessor

Module Name

NICF - Spark on Azure HDInsight

Competency Unit

IT-BDA-401S-1 : Analyse data and identify  business insights (4 Credit Values)

IT-RG-402S-1 (SAHDI) : Gather data to identify business requirements (2 Credit Values)

By the end of this module, the learner should be able to gain the following knowledge:

  • Statistical modelling techniques
  • Programming language and tools for big data analytics and how they integrate with big data technologies
  • Current and emerging trends in the business domain
  • Concepts of computing used in big data analytics
  • Understanding the meaning of the data in different data sources
  • Understand Machine Learning Support in Spark Clusters
  • Learn how to implement a predictive solution using Spark
  • Learn how to build real-time machine learning solutions with Spark.
  • Learn how to use R to work with data and build models by leveraging Hadoop in HDInsight.
  • Understand Software development methodologies, with emphasis on requirement gathering for data science projects
  • Understand the role of stakeholders and their level of involvement in data science projects
  • Understand the Information gathering methods for data science projects
  • Understand Functional and non-functional requirements of Data Science projects and document them

By the end of this module, the learner should be able 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

Who Should Attend

Ideal Candidates

Entry requirements

Minimum Age: 21 Years

 

Academic Level: 3 GCE A Level passes or its equivalent and minimum 1-year experience in statistics or programming

 

Language Proficiency: IELTS 6.5 or its equivalent

Who Should Attend

  • Persona

    IT and Non-IT Professionals who are interested in a career in Data Science

  • Persona

    IT and Non-IT Professionals who are interested in a career in Data Science

Advance Your Career

Advance Your Career

SSG Funding Programme Options

Career

Train & Place Programme with CAT A funding

After qualifying through a pre-course assessment, you can join the course, with a course free grant from SSG. After completing the course, you will get opportunities to attend interviews with prospective employers for Data Analyst or Data Scientist job role

Grad Requirements

Graduation Requirements

To graduate from this course, the learner should meet the following criteria:

  • Minimum attendance of 75% for all Sessions in each of the modules of the qualification
  • Should be assessed Competent (C) in each of the modules of the qualification

Certificates

Certificates

Following certifications will be awarded to the student upon meeting graduation requirements

  1. Statement of Attainment by SSG, Singapore: IT-BDA-402S-1 Apply Data Visualization
  2. Statement of Attainment by SSG, Singapore: IT-STM-401S-1 Develop Statistical Model
  3. Statement of Attainment by SSG, Singapore: IT-BDA-403S-1 Operationalise Analytical Models
  4. Statement of Attainment by SSG, Singapore: IT-BDA-301S-1 Apply data science and big data analytics knowledge
  5. Statement of Attainment by SSG, Singapore: IT-TA-401S-1 Develop text analytics process
  6. Statement of Attainment by SSG, Singapore: IT-BDA-401S-1 - Analyse data and identify business insights
  7. Statement of Attainment by SSG, Singapore: IT-RG-402S-1 Gather data to identify business requirements
  8. NICF - Diploma in Business Analytics

Course Fees & Funding

Course Fees & Funding
(All fees stated below, are in SGD)

For Self-sponsored Individuals

Category of Individuals
Type Singapore PR & Singapore Citizen Age >= 21 Singapore Citizen Age >= 40 Singapore Citizen Age >= 35 & earning <= 2000.00/month
Course Fee 18000.0 18000.0 18000.0
Less: SkillsFuture Funding 12600.0 12600.0 12600.0
Course Fee after SkillsFuture Funding 5400.0 5400.0 5400.0
Add: GST (7%) 378.00000000000006 378.00000000000006 378.00000000000006
Course Fee after GST 5778.0 5778.0 5778.0
Add: Fee Protection Scheme (inclusive of GST) 0.0 0.0 0.0
Less: Workfare Training Scheme (WTS) 0.0 0.0 4500.0
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.0 3600.0 0.0
Total Fee Payable to Lithan 5778.0 2178.0 1278.0

For Company sponsored

Type of Companies
Type Non-SME & Singapore PR & Singapore Citizen Age >= 21 SME & Singapore PR & Singapore Citizen Age >= 21 SME & Singapore Citizen Age >= 35 & earning <= 2000.00/month
Course Fee 18000.0 18000.0 18000.0
Less: SkillsFuture Funding 12600.0 12600.0 12600.0
Course Fee after SkillsFuture Funding 5400.0 5400.0 5400.0
Add: GST (7%) 378.00000000000006 378.00000000000006 378.00000000000006
Course Fee after GST 5778.0 5778.0 5778.0
Add: Fee Protection Scheme (inclusive of GST) 0.0 0.0 0.0
Less: Workfare Training Scheme (WTS) 0.0 0.0 4500.0
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.0 0.0 0.0
Total Fee Payable to Lithan 5778.0 5778.0 1278.0
Note : SME can claim additional 20% of full gross fee under Enhanced Training Support Subsidy (ETSS) where applicable.
Fee Description

Miscellaneous Fees

Miscellaneous Fee (applicable to all individuals)
Medical Insurance (option)* 96.00 - Payable upon approval of request
Deferment Admin Fee* 160.50 - Payable upon approval of request
Re-module Fee* 0.00 - Refer to Modular course fee which is available on Lithan’s website
Re-sit Exam Fee* 107.00 - Payable upon approval of request
Change of Assessment Date Fee* 64.20 - Payable upon approval of request
Course StructureNote : where applicable (inclusive of GST)

For Self-sponsored Individuals

Category of Individuals
Singapore Citizen Age >= 35 & earning <= 2000.00/month
Course Fee 18000.0
Less: SkillsFuture Funding 12600.0
Course Fee after SkillsFuture Funding 5400.0
Add: GST (7%) 378.00000000000006
Course Fee after GST 5778.0
Add: Fee Protection Scheme (inclusive of GST) 0.0
Less: Workfare Training Scheme (WTS) 4500.0
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.0
Total Fee Payable to Lithan 1278.0
Singapore Citizen Age >= 40
Course Fee 18000.0
Less: SkillsFuture Funding 12600.0
Course Fee after SkillsFuture Funding 5400.0
Add: GST (7%) 378.00000000000006
Course Fee after GST 5778.0
Add: Fee Protection Scheme (inclusive of GST) 0.0
Less: Workfare Training Scheme (WTS) 0.0
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 3600.0
Total Fee Payable to Lithan 2178.0
Singapore PR & Singapore Citizen Age >= 21
Course Fee 18000.0
Less: SkillsFuture Funding 12600.0
Course Fee after SkillsFuture Funding 5400.0
Add: GST (7%) 378.00000000000006
Course Fee after GST 5778.0
Add: Fee Protection Scheme (inclusive of GST) 0.0
Less: Workfare Training Scheme (WTS) 0.0
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.0
Total Fee Payable to Lithan 5778.0

For Company sponsored

Type of Companies
SME & Singapore Citizen Age >= 35 & earning <= 2000.00/month
Course Fee 18000.0
Less: SkillsFuture Funding 12600.0
Course Fee after SkillsFuture Funding 5400.0
Add: GST (7%) 378.00000000000006
Course Fee after GST 5778.0
Add: Fee Protection Scheme (inclusive of GST) 0.0
Less: Workfare Training Scheme (WTS) 4500.0
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.0
Total Fee Payable to Lithan 1278.0
SME & Singapore PR & Singapore Citizen Age >= 21
Course Fee 18000.0
Less: SkillsFuture Funding 12600.0
Course Fee after SkillsFuture Funding 5400.0
Add: GST (7%) 378.00000000000006
Course Fee after GST 5778.0
Add: Fee Protection Scheme (inclusive of GST) 0.0
Less: Workfare Training Scheme (WTS) 0.0
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.0
Total Fee Payable to Lithan 5778.0
Note : SME can claim additional 20% of full gross fee under Enhanced Training Support Subsidy (ETSS) where applicable.
Non-SME & Singapore PR & Singapore Citizen Age >= 21
Course Fee 18000.0
Less: SkillsFuture Funding 12600.0
Course Fee after SkillsFuture Funding 5400.0
Add: GST (7%) 378.00000000000006
Course Fee after GST 5778.0
Add: Fee Protection Scheme (inclusive of GST) 0.0
Less: Workfare Training Scheme (WTS) 0.0
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.0
Total Fee Payable to Lithan 5778.0

Miscellaneous Fees

Miscellaneous Fee (applicable to all individuals)
Medical Insurance (option)* 96.00 - Payable upon approval of request
Deferment Admin Fee* 160.50 - Payable upon approval of request
Re-module Fee* 0.00 - Refer to Modular course fee which is available on Lithan’s website
Re-sit Exam Fee* 107.00 - Payable upon approval of request
Change of Assessment Date Fee* 64.20 - Payable upon approval of request
Note : where applicable (inclusive of GST)

Register to know more