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Express Data Science

Express Data Science Data Science

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Course Outcome

Acquire Basic R programming & Azure Machine Learning Skills
Learn how to implement Data Analytic Solution on Azure ML

Get Trained

Learn how to explore and visualize data using Microsoft Excel, Learn R syntax and how to handle data structures such as vectors, matrices, factors, data frames and lists; Learn how to build visualizations using the graphical capabilities of R; Learn how to operationalize and improve the machine learning models on Azure

Get Experiential

While completing these 3 modular courses, learner with Plan and implement a Data Analytics Solution and plot ggplot using R on Azure ML environment, with the guidance of experienced tutors.

Get Certified

Upon successful course completion, learners will receive the following NICF Statement of Attainments (SOA):

  • IT-BDA-402S-1 Apply Data Visualization
  • IT-BDA-301S-1 Apply data science and big data analytics knowledge
  • IT-BDA-403S-1 Operationalise Analytical Models   

How You Learn

How You Learn?

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

Learning Duration (GLH) 161.5 Hours

On Campus Hours
Up to 41.5 hours
Off Campus Hours
Up to 120 hours

Course Duration with Study Options

Full Time

6 Weeks

(4 days a week

7 hrs a day)

Part time

12 Weeks

(4 days a week

3.5 hrs a day)

*Guided Learning Hours

Course Structure

Course Structure

Express Data Science

Lithan’s Express Data Scientist course provides the knowledge and skills to learner in exploring data using a variety of visualization, analytical, and statistical techniques. Learners will be able to import data from different sources, creating mashups between data sources, and prepare data for analysis. This course will impart data science knowledge and skills to learner requirements to build simple data analytics solutions on Azure ML.

 

This course is aligned with Singapore’s SSG WSQ NICF Framework. The learner shall perform e-learning and lab exercises for each of the Instructional Units with online support from the Tutors. Subsequently, the learner shall perform a modular project for each of the 3 modules, with the guidance of an Industry expert who play the role of Project Tutor.

 

This course consists of 3 Course Modules:

  • Data Queries and Visualization Basics
  • Basic R Programming
  • Data Science Essentials

Course Modules :

Outline Schedule

DQVB - NICF - Data Queries and Visualization Basics

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

BRP - NICF - Basic R Programming

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

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

Who Should Attend

Ideal Candidates

Entry requirements

Educational Qualification: 3 GCE A Level passes or its equivalent and minimum 1 year experience in Statistics or Programming

 

Language Proficiency: IELTS 5.0 or its equivalent

Who Should Attend

  • Higher learning students who want to learn how to implement Data Analytic Solution on Azure ML

  • Higher learning students who want to learn how to implement Data Analytic Solution on Azure ML

Advance Your Career

Advance Your Career

Programme Options

Train only Program (CAT B funding)

Acquire R Programming skills & Data Analytics solution implementation skills by enrolling to 3 modular courses, availing SSG CAT B funding. After the completion of these modular courses, explore Data Science jobs and simultaneously enroll to Lithan’s NICF Diploma in Business Analytics Programme and get exemption for some of the units.

Grad Requirements

Graduation Requirements

To graduate from each of the modular course, learner should meet the following criteria:

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

Certificates

Certificates

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

  1. Statement of Attainment by SSG, Singapore: IT-BDA-402S-1 Apply Data Visualization
  2. Statement of Attainment by SSG, Singapore: IT-BDA-403S-1 Operationalise Analytical Models
  3. Statement of Attainment by SSG, Singapore: IT-BDA-301S-1 Apply data science and big data analytics knowledge

Course Fees & Funding

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

For Self-sponsored Individuals

Category of Individuals
Type Singapore Citizen & Singapore PR Age >= 21 Singapore Citizen Age >= 40 Singapore Citizen Age >= 35 & earning <= 2000.00/month
Course Fee 6000.0 6000.0 6000.0
SSG Grant 1290.0 4300.0 5700.0
Nett Fee Payable 4710.0 1700.0 300.0
GST Payable 420.0 420.0 420.0
Total Nett Course Fee 5130.0 2120.0 720.0
Fee Protection Scheme (inclusive of GST)* 0.0 0.0 0.0
Total Fee Payable to Lithan 5130.0 2120.0 720.0

For Company sponsored

Type of Companies
Type Non-SME & Singapore Citizen & Singapore PR Age >= 21 SME & Singapore Citizen & Singapore PR Age >= 21 SME & Singapore Citizen Age >= 35 & earning <= 2000.00/month
Course Fee 6000.0 6000.0 6000.0
SSG Grant 1290.0 4300.0 5700.0
Nett Fee Payable 4710.0 1700.0 300.0
GST Payable 420.0 420.0 420.0
Total Nett Course Fee 5130.0 2120.0 720.0
Fee Protection Scheme (inclusive of GST)* 0.0 0.0 0.0
Total Fee Payable to Lithan 5130.0 2120.0 720.0

For Self-sponsored Individuals

Category of Individuals
Singapore Citizen Age >= 40
Course Fee 6000.0
SSG Grant 4300.0
Nett Fee Payable 1700.0
GST Payable 420.0
Total Nett Course Fee 2120.0
Fee Protection Scheme (inclusive of GST)* 0.0
Total Fee Payable to Lithan 2120.0
Singapore Citizen Age >= 35 & earning <= 2000.00/month
Course Fee 6000.0
SSG Grant 5700.0
Nett Fee Payable 300.0
GST Payable 420.0
Total Nett Course Fee 720.0
Fee Protection Scheme (inclusive of GST)* 0.0
Total Fee Payable to Lithan 720.0
Singapore Citizen & Singapore PR Age >= 21
Course Fee 6000.0
SSG Grant 1290.0
Nett Fee Payable 4710.0
GST Payable 420.0
Total Nett Course Fee 5130.0
Fee Protection Scheme (inclusive of GST)* 0.0
Total Fee Payable to Lithan 5130.0

For Company sponsored

Type of Companies
SME & Singapore Citizen & Singapore PR Age >= 21
Course Fee 6000.0
SSG Grant 4300.0
Nett Fee Payable 1700.0
GST Payable 420.0
Total Nett Course Fee 2120.0
Fee Protection Scheme (inclusive of GST)* 0.0
Total Fee Payable to Lithan 2120.0
SME & Singapore Citizen Age >= 35 & earning <= 2000.00/month
Course Fee 6000.0
SSG Grant 5700.0
Nett Fee Payable 300.0
GST Payable 420.0
Total Nett Course Fee 720.0
Fee Protection Scheme (inclusive of GST)* 0.0
Total Fee Payable to Lithan 720.0
Non-SME & Singapore Citizen & Singapore PR Age >= 21
Course Fee 6000.0
SSG Grant 1290.0
Nett Fee Payable 4710.0
GST Payable 420.0
Total Nett Course Fee 5130.0
Fee Protection Scheme (inclusive of GST)* 0.0
Total Fee Payable to Lithan 5130.0

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