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Data Science Implementation


5 weeks part time / 2 weeks full time per module Online Synchronous Sessions by the Instructor & Mentor



Learn to analyse the data using R programming, Spark on Azure HDInsights and Azure Machine learning

What do I Get?

Acquire Power BI and SQL Skills

Learn how to  visualize, analyse and report information using Microsoft Power BI along with basic concepts of Data Modelling, SQL Transformations and the various forms of Data Analysis

Acquire R programming Skills

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 using Azure Machine Learning

Acquire Data Science Skills

Learn the essential skills and hands-on experience with the science and research aspects of Data Science using R and Azure Machine learning from setting up a proper  supervised machine learning predictive model to making valid claims and inferences from data

Acquire Statistical Skills

Learn  how to apply the essential concepts of probability , descriptive statistics, random variables, sampling ,confidence intervals, and hypothesis testing and how to apply to run simulations of a model . Learn how to use for solving simple mathematical equations and plotting simple histograms, read and import data and setup real world models on Azure ML

Acquire Machine Learning Skills

Learn how to  improve Machine Learning Models on Azure ML, how to create and evaluate a classifier in Azure Machine Learning and manage imbalanced data using R; Learn how to apply text classification in  categorizing newspaper articles and contents into topics

Acquire Data Analytics Implementation Skills

Execute data analytics Implementation project using Azure machine learning tool by identifying various data sources, extracting , cleaning and transforming data and create reports and dashboards as per the business requirement

Audience and Certificates

Target Audience

  • Entrepreneurs and relevant stakeholders responsible to analyse the data and generate reports using R programming, Azure Machine learning and other statistical methods
  • Leadership and management teams, mid-level managers of small and medium enterprises seeking data analytics solution to the organisation

Prerequisite

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

Graduation Requirements

  • 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

Certificate(s)

  • Statement of Attainment by SSG, Singapore: ICT-DES-4001-1.1 Data Design

  • Statement of Attainment by SSG, Singapore: ICT-DIT-4006-1.1 Data Visualisation

  • Statement of Attainment by SSG, Singapore: ICT-SNA-4009-1.1 Data Strategy

  • Statement of Attainment by SSG, Singapore: ICT-DIT-4005-1.1 Data Engineering

  • Statement of Attainment by SSG, Singapore: ICT-SNA-4011-1.1 Emerging Technology Synthesis

  • Statement of Attainment by SSG, Singapore: ICT-OUS-3011-1.1 Problem Management

  • Statement of Attainment by SSG, Singapore: ICT-PMT-4001-1.1 Project Management

Blended Learning Journey

(302.5 Hours)

E-Learning

72 hours

Projects / Assignments

150 hours

Flipped Class/Mentoring

78 hours

Assessment

2.5 hours

Modules

NICF-Data Queries and Visualization Basics (SF) (Synchronous and Asynchronous E-Learning)

You will get started on your Data Science journey by learning how to write queries and modify data using Transact-SQL as well as visualise data using Power BI.

Session Plan

More Details

Learning Outcome

Knowledge

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
  • Learn Syntax of Transact-SQL, working with data types, tables and manipulating data using T-SQL
  • Understand how to program using Transact-SQL
  • Learn how to perform visual analysis to gather insights using Power BI
  • Learn different types of data visualisation techniques using Power BI
  • Working with different types of data sources connecting Power BI

Skills

By the end of this module, the Learner should be able to apply the following skills:

  • Assess the data to be visualised based on the volume, velocity and variety
  • Create reports using the relevant Power BI visualisation techniques
  • Develop a data visualisation model that conveys the insights to the audience
  • Develop PowerBI dashboards  Write programs using T-SQL
  • Implement error handling and transactions using T-SQL

Other Information

Funding Validity Period: Until 31-Dec-2021

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

NICF-Basic R Programming (SF) (Synchronous and Asynchronous E-Learning)

You will learn the basics of R programming, how to handle data structures such as vectors, matrices, lists and data frames and create your own stunning data visualisations.

Session Plan

More Details

Learning Outcome

Knowledge

  • 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 

Skills

  • 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

Other Information

Funding Validity Period: Until 31-Dec-2021

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

NICF-Data Science Essentials (SF) (Synchronous and Asynchronous E-Learning)

You will explore Advanced R programming and perform predictive analytics and create visualisations using the popular ggplot2 package.

Session Plan

More Details

Learning Outcome

Knowledge

  • 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

Skills

  • 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 

Other Information

Funding Validity Period: Until 31-Dec-2021

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

NICF-Principles of Machine Learning (SF) (Synchronous and Asynchronous E-Learning)

You will get hands-on experience building and deriving insights of text analytics from machine learning models using R and Azure Machine Learning.

Session Plan

More Details

Learning Outcome

Knowledge

  • 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

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 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

Other Information

Funding Validity Period: Until 31-Dec-2021

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

NICF-Spark on Azure HDInsight (SF) (Synchronous and Asynchronous E-Learning)

You will learn how to use Spark in Microsoft Azure HDInsight to create predictive analytics and machine learning solutions.

Session Plan

More Details

Learning Outcome

Knowledge

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

  • 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
     

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 
  • 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

Other Information

Funding Validity Period: Until 31-Dec-2021

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

Pricing and Funding

SGD 15000.00

Pricing

Fee Description

Detailed Breakdown

Funding

SME can claim additional 20% of full gross fee under Enhanced Training Support Subsidy (ETSS) where applicable

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