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NICF - Diploma In Business Analytics Data Analytics

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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 - Data Queries and Visualization Basics (CRS-Q-0037588-ICT)

Outline
Schedule
Module Name

Data Queries and Visualization Basics (CRS-Q-0037588-ICT)

Session Outline Activities

E-learning 1: Intro to Data Science  

E-learning 2: Intro to  T SQL  

Session Description

Assignment 1 on TSQL

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

E- Learning 3 - Different concepts in TSQL 

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

Assignment 2 on Joins, Functions  

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

E- Learning 4 - Advanced TSQL concepts

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

Assignment 5 –Modifying data and Error handling 

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

E-Learning 5 - Analytics in Excel

  • 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

Assignment 5 - Excel Analytics

  • 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

Project Mentoring - 1

  • 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

Project Mentoring - 2 

Assessor will review and provide feedback

Project Mentoring - 3

Session Description

Module Name

Data Queries and Visualization Basics (CRS-Q-0037588-ICT)

Competency Unit

ICT-DIT-4006-1.1 : Design data displays to present trends and finding, incorporating new and advanced visualisation techniques and analytics capabilities

ICT-DES-4001-1.1 : Design data models and data flow diagrams and mechanisms to optimise the flow, maintenance, storage and retrieval of data (

 

  • 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
  • Learn how to perform visual analysis to gather insights
  • Understand different types of data visualisation techniques using Power BI
  • Learn how to perform visual analysis to gather insights
  • Understand Data visualisation tools like Power BI
  • Understand the anatomy of a data visualisation
  • Understand Visualisation design methodology and process with Power BI

  • Identify key factors that may affect the success of data visualisation
  • Assess the data to be visualised based on the volume, velocity and variety
  • Gather insights/stories using the relevant Power BI visualisation techniques
  • Develop a data visualisation model that conveys the insights to the audience
  • 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, velocity and variety

  • Funding Validity Period:  30/7/2021
  • Course Developer : Lithan Academy
  • Qualification Course Code:  CRS-Q-0038611-ICT

 

NICF-STDSA - NICF - Statistical Thinking for Data Science and Analytics(CRS-Q-0030197-IT

Outline
Schedule
Module Name

NICF - Statistical Thinking for Data Science and Analytics(CRS-Q-0030197-IT

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(CRS-Q-0030197-IT

Competency Unit

BRP - Basic R Programming (CRS-Q-0037583-ICT)

Outline
Schedule
Module Name

Basic R Programming (CRS-Q-0037583-ICT)

Session Outline Activities

E Learning 1

Introduction to R-programming  

Vectors, Matrices and Factors

Session Description

Flipped Class 1

Introduction to R-programming  

Vectors, Matrices and Factors

Session Description

Assignment 1

Vectors, Matrices and Factors

Session Description

E Learning 2

Lists, Data Frames, Basic Graphics, 

Session Description

Flipped Class 2

Lists, Data Frames, Basic Graphics, 

Session Description

Assigment 2

Lists, Data Frames, Basic Graphics, 

Session Description

E Learning 3

Forecasting  

Session Description

E Learning 4

Azure Machine Learning 

Session Description

Flipped Class 3

Forecasting  and Azure Machine Learning 

Session Description

E Learning 3

Forecasting  and Machine Learning 

Session Description

E Learning 5 

Project 

 

Session Description

Flipped Class 4

Project Discussion

Session Description

Assignment 4 

Azure Machine Learning 

Session Description

Project Implementation Support 1

Session Description

Project Implementation Support 2

Session Description

Project Implementation Support 3

Session Description

Summative Assessment 

Session Description

Module Name

Basic R Programming (CRS-Q-0037583-ICT)

Competency Unit

ICT-SNA-4009-1.1 : Develop data management structures and recommend policies, processes and tools for effective data storage, handling and utilisation

  • Introductory R language fundamentals and basic syntax
  • Basics of R and how it’s used to perform data analysis
  • Creating Matrices and Data frames
  • Work with data in R
  • Introduction to Azure Machine Learning  
  • Introduction to Forecasting and Time Series 

  • Define analytics architecture requirements to deploy the predictive model
  • Design and develop predictions in Azure Machine Learning(AML) studio 
  • Create R scripts and integrate in AML
  • Create Time series forecasting model
  • Monitor and tune the deployed model to ensure that it delivers the expected outcome and minimize the error predictions

  • Funding Validity Period: 30/7/2021
  • Course Developer : Lithan Academy
  • Qualification Course Code: CRS-Q-0038611-ICT

DSE - Data Science Essentials (CRS-Q-0037586-ICT)

Outline
Schedule
Module Name

Data Science Essentials (CRS-Q-0037586-ICT)

Session Outline Activities

E Learning 1

1. Introduction to Data Science  

2. Probability and Statistics 

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

E Learning 2

Simulation and Hypothesis testing          

Data Exploration and Visualization 

The Student will PERFORM activities related to IU1 &  IU2

Flipped Class 1  

1. Introduction to Data Science   

2. Probability and Statistics 

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

Assignment 1 

1. Introduction to Data Science   

2. Probability and Statistics 

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

Flipped Class 2 

Simulation and Hypothesis testing           

Data Exploration and Visualization 

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

 Assignment  2

Simulation and Hypothesis testing           

Data Exploration and Visualization 

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

  E- Learning 3 

Introduction to R-Programming for Data science 

Functions , Control flow and loops 

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

Flipped Class 3 

Introduction to R-Programming for Data science 

Functions , Control flow and loops 

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

 Assignment  3

Functions , Control flow and loops 

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

E- Learning 4 

11.Reading in data and Writing data
12. Reading from SQL Server

 

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

Flipped Class 4 

Reading in data and Writing data
Reading from SQL Server

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

Assignment 4 

Reading in data and Writing data
Reading from SQL Server

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

E Learning 5 and 6 

Simulation 
Linear Model
Graphics in R

 

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

Flipped Class 5 

Simulation 
Linear Model
Graphics in R

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

Assignment 5 

Simulation 
Linear Model
Graphics in R

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

Assignment 6

Working with Data and Manipulating Data

Session Description

Flipped Class 5  

Project Discussion 

Session Description

Project Implementation Support 1

Session Description

Project Implementation Support 2

Session Description

Project Implementation Support 3

Session Description

Project Implementation Support 4

Session Description

Summative Assessment 

Session Description

Module Name

Data Science Essentials (CRS-Q-0037586-ICT)

Competency Unit

ICT-DIT-4005-1.1 : Data Engineering : Translate business requirements into data structures and processes to standardise data, verify data reliability and validity, store, extract, transform, load and integrate data

  • Understand the research process and practices  of Data Exploration and Visualization 
  • Probability and statistics in Data Science 
  • Simulation and hypothesis testing using R 
  • Data Ingestion, cleansing and transformation processes 
  • Various research claims 
  • Survey design and measurement, Reliability and Validity
  • Correlation and Experimental design

  • Working with probability and statistics; Simulation and hypothesis testing
  • Create and customize visualizations using ggplot2
  • Design the process of predictive analysis to transform extracted dataset into models using R
  • Consolidating data from multiple datasets and Visualization with Azure Machine Learning and R on Azure stack
  • K-means clustering with Azure Machine Learning
  • Design Correlation and Regression Experiments
  • Develop data integration procedures using Webservice modelling from Azure Machine Learning 

  • Funding Validity Period: 30/7/2021
  • Course Developer : Lithan Academy
  • Qualification Course Code: CRS-Q-0038611-ICT

 

PML - Principles of Machine Learning (CRS-Q-0037585-ICT)

Outline
Schedule
Module Name

Principles of Machine Learning (CRS-Q-0037585-ICT)

Session Outline Activities

E - Learning 1 

Classification 

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

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

Flipped Class 1 

Classification 

Session Description

Assignment 1 

Classification 

Session Description

E Learning 2

Regression and Improving Machine Learning Models

Session Description

Flipped Class 2

Regression and Improving Machine Learning Models

Session Description

Assignment 2

Regression and Improving Machine Learning Models

Session Description

E Learning 3

Decision Trees , Neural Networks and Support Vector Machines  , Clustering

Session Description

Flipped Class 3

Decision Trees , Neural Networks and Support Vector Machines  , Clustering

Session Description

Assignment 3

Decision Trees , Neural Networks and Support Vector Machines  , Clustering

Session Description

E- Learning 4 

Text Analytics 1

Session Description

E- Learning 5

Text Analytics 2

Session Description

Flipped Class -4 

Text Analytics

Session Description

Assignment -4 

Text Analytics

Session Description

Assignment -5 

Text Analytics 2

Session Description

Project Implementation Support 1

Session Description

Project Implementation Support 2

Session Description

Project Implementation Support 3

Session Description

Summative Assessment 

Session Description

Module Name

Principles of Machine Learning (CRS-Q-0037585-ICT)

Competency Unit

ICT-SNA-4011-1.1(DS) : Emerging Technology Synthesis : Evaluate new and emerging technology and trends against the organisational needs and processes

  • Text analytics solutions
  • Text analytics process and artifacts
  • Text mining techniques and how to apply them
  • Operation of Classifiers and how to use Logistic Regression as a Classifier
  • Metrics used to evaluate classifiers and regression models
  • Operation of Regression models and how to use Linear regression for prediction and forecasting
  • Problems of over-parameterization and dimensionality
  • How and when to use common supervised machine learning models
  • Compare different Multi Class models to analyse the best model

  • 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 artifactswith 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

  • Funding Validity Period: 30/7/2021
  • Course Developer : Lithan Academy
  • Qualification Course Code: CRS-Q-0038611-ICT

SAHDI - Spark on Azure HDInsight(CRS-Q-0037584-ICT)

Outline
Schedule
Module Name

Spark on Azure HDInsight(CRS-Q-0037584-ICT)

Session Outline Activities

E- learning 1 

Introduction to Data Science with SPARK

Session Description

Flipped Class 1 

Introduction to Data Science with SPARK

Session Description

Assignment 1 

Introduction to Data Science with SPARK

Session Description

E- learning 2 

Getting started with Machine Learning 

Session Description

Flipped Class 2 

Getting started with Machine Learning 

Session Description

Assignment 2 

Getting started with Machine Learning 

Session Description

E- learning 3 

Evaluating and Optimizing Machine learning models

Session Description

Flipped Class 3 

Evaluating and Optimizing Machine learning models

Session Description

Assignment  3 

Evaluating and Optimizing Machine learning models

Session Description

E- learning 4

Recommenders  

Session Description

E- learning 5

Unsupervised models 

Session Description

Flipped Class  4

Recommenders and Unsupervised models 

Session Description

Assignment  4

Recommenders and Unsupervised models 

Session Description

Project  Mentoring 1

Session Description

Project  Implementation Support 1

Session Description

Project  Implementation Support 2

Session Description

Summative Assessment 

Session Description

Module Name

Spark on Azure HDInsight(CRS-Q-0037584-ICT)

Competency Unit

ICT-PMT-4001-1.1(DS) : Business Needs Analysis : Investigate existing business processes, evaluate requirements and define the scope for recommended solutions and programmes

ICT-OUS-3011-1.1(DS) : Problem Management : Handle specific problems from diagnosis and prioritisation to the identification and implementation of solutions

 

  • Programming language and tools for big data analytics and how they integrate with big data technologies
  • Emerging trends in the business domain
  • Concepts of computing used in big data analytics
  • Machine Learning Support in Spark Clusters
  • Implement a predictive solution using Spark
  • Build real-time machine learning solutions with Spark.
  • Use R to work with data and build models by leveraging Hadoop in HDInsight.

 

  • Software development methodologies, with emphasis on requirement gathering for data science projects
  • Role of stakeholders and their level of involvement in data science projects
  • Information gathering methods for data science projects
  • Functional and non-functional requirements of Data Science projects and document them
  • Principles of reactive and proactive problem management
  • Documentation requirements and protocols in problem management
  • Usage of documentation tools, systems and records to log relevant information throughout the problem's lifecycle
     

  • 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 
  • Implement solutions to address the problem through appropriate control procedures
  • Propose solutions to prevent future occurrences of similar problems
  • Document information about problems and the appropriate workarounds and resolutions

  • Funding Validity Period: 30/7/2021
  • Course Developer : Lithan Academy
  • Qualification Course Code: CRS-Q-0038611-ICT

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. NICF - Diploma in Business Analytics
  2. Statement of Attainment by SSG, Singapore: ICT-DES-4001-1.1 Data Design
  3. Statement of Attainment by SSG, Singapore: ICT-DIT-4006-1.1: Data Visualisation
  4. Statement of Attainment by SSG, Singapore: IT-STM-401S-1 Develop Statistical Model
  5. Statement of Attainment by SSG, Singapore: ICT-SNA-4009-1.1:Data Strategy)
  6. Statement of Attainment by SSG, Singapore: Data Engineering : ICT-DIT-4005-1.1
  7. Statement of Attainment by SSG, Singapore: Emerging Technology Synthesis : ICT-SNA-4011-1.1
  8. Statement of Attainment by SSG, Singapore: Problem Management : ICT-OUS-3011-1.1
  9. Statement of Attainment by SSG, Singapore: Project Management : ICT-PMT-4001-1.1

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 >= 35 & earning <= 2000.00/month Singapore Citizen Age >= 40
Course Fee 18,000.00 18,000.00 18,000.00
Less: SkillsFuture Funding 12,600.00 12,600.00 12,600.00
Course Fee after SkillsFuture Funding 5,400.00 5,400.00 5,400.00
Add: GST (7%) 378.00 378.00 378.00
Course Fee after GST 5,778.00 5,778.00 5,778.00
Add: Fee Protection Scheme (inclusive of GST) 0.00 0.00 0.00
Less: Workfare Training Scheme (WTS) 0.00 4,500.00 0.00
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.00 0.00 3,600.00
Total Fee Payable to Lithan 5,778.00 1,278.00 2,178.00

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 18,000.00 18,000.00 18,000.00
Less: SkillsFuture Funding 12,600.00 12,600.00 12,600.00
Course Fee after SkillsFuture Funding 5,400.00 5,400.00 5,400.00
Add: GST (7%) 378.00 378.00 378.00
Course Fee after GST 5,778.00 5,778.00 5,778.00
Add: Fee Protection Scheme (inclusive of GST) 0.00 0.00 0.00
Less: Workfare Training Scheme (WTS) 0.00 0.00 4,500.00
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.00 0.00 0.00
Total Fee Payable to Lithan 5,778.00 5,778.00 1,278.00
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 18,000.00
Less: SkillsFuture Funding 12,600.00
Course Fee after SkillsFuture Funding 5,400.00
Add: GST (7%) 378.00
Course Fee after GST 5,778.00
Add: Fee Protection Scheme (inclusive of GST) 0.00
Less: Workfare Training Scheme (WTS) 4,500.00
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.00
Total Fee Payable to Lithan 1,278.00
Singapore Citizen Age >= 40
Course Fee 18,000.00
Less: SkillsFuture Funding 12,600.00
Course Fee after SkillsFuture Funding 5,400.00
Add: GST (7%) 378.00
Course Fee after GST 5,778.00
Add: Fee Protection Scheme (inclusive of GST) 0.00
Less: Workfare Training Scheme (WTS) 0.00
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 3,600.00
Total Fee Payable to Lithan 2,178.00
Singapore PR & Singapore Citizen Age >= 21
Course Fee 18,000.00
Less: SkillsFuture Funding 12,600.00
Course Fee after SkillsFuture Funding 5,400.00
Add: GST (7%) 378.00
Course Fee after GST 5,778.00
Add: Fee Protection Scheme (inclusive of GST) 0.00
Less: Workfare Training Scheme (WTS) 0.00
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.00
Total Fee Payable to Lithan 5,778.00

For Company sponsored

Type of Companies
SME & Singapore Citizen Age >= 35 & earning <= 2000.00/month
Course Fee 18,000.00
Less: SkillsFuture Funding 12,600.00
Course Fee after SkillsFuture Funding 5,400.00
Add: GST (7%) 378.00
Course Fee after GST 5,778.00
Add: Fee Protection Scheme (inclusive of GST) 0.00
Less: Workfare Training Scheme (WTS) 4,500.00
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.00
Total Fee Payable to Lithan 1,278.00
SME & Singapore PR & Singapore Citizen Age >= 21
Course Fee 18,000.00
Less: SkillsFuture Funding 12,600.00
Course Fee after SkillsFuture Funding 5,400.00
Add: GST (7%) 378.00
Course Fee after GST 5,778.00
Add: Fee Protection Scheme (inclusive of GST) 0.00
Less: Workfare Training Scheme (WTS) 0.00
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.00
Total Fee Payable to Lithan 5,778.00
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 18,000.00
Less: SkillsFuture Funding 12,600.00
Course Fee after SkillsFuture Funding 5,400.00
Add: GST (7%) 378.00
Course Fee after GST 5,778.00
Add: Fee Protection Scheme (inclusive of GST) 0.00
Less: Workfare Training Scheme (WTS) 0.00
Less: SkillsFuture Mid-Career Enhanced Subsidy (SFMCES) 0.00
Total Fee Payable to Lithan 5,778.00

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)