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Professional Diploma in Data Science

SCTP-NICF-Diploma in Infocomm Technology (Data) (Synchronous and Asynchronous E-Learning)

9 months Part time / 4 months Full Time (Bootcamp) Instructor-led Live & Mentor-led Blended Learning



Be a Data Scientist skilled at R programming , Statistics and Azure Machine learning.

What do I Get?

Acquire Data Science Skills

Learn core principles of data science, supervised and unsupervised machine learning techniques that you can use to Visualize, Analyse and Predict data with hands-on experience in Microsoft Azure Machine Learning to build Data Science solutions and prepare yourself for a Data Scientist role 

Acquire R programming Skills

Learn R programming to import, Clean, prepare Visualize, Analyse and Predict both structured and unstructured data , perform statistical analysis in R studio and integrate deployment in Azure Machine Learning 

Explore Visualisation skills

Explore how data visualisation transforms complicated Information into useful data driven insights to grow your business with interactive reports and dashboards using Power BI . 

Attain Statistical knowledge and skills

Learn essential concepts of descriptive statistics, basic probability, random variables, sampling and hypothesis testing and how to apply using R programming 

Audience and Certificates

Target Audience

  • Individuals who are interested in a Data Science career

Prerequisite

Minimum Age: Min. 21 years

Academic Level & Work Experience: 1 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-DIT-4001-1.1 Analytics and Computational Modelling

  • 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

(643 Hours)

E-Learning

90 hours

Projects / Assignments

180 hours

Flipped Class/Mentoring

90 hours

Additional Practice – for Bootcamp only

280 hours

Assessment

3 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, you will gain following knowledge:

  • Learn the Syntax of Transact-SQL, work with data types,& tables, and manipulate data using T-SQL
  • Understand how to programme 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
  • Work 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:

  • 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

Other Information

Funding Validity Period: Until 31-Dec-2022

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

By the end of this module, the Learner should be able to gain the following 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  

Skills

By the end of this module, the Learner should be able to apply the following 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
  • Monitor and tune the deployed model to ensure that it delivers the expected outcome 

Other Information

Funding Validity Period: Until 31-Dec-2022

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

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

  • Understand the process and practices  of Data Exploration and Visualization 
  • Principles of Data Science
  • Data Ingestion, cleansing and transformation processes 
  • Various Machine Learning process in Azure 
  • Publish the machine learning model

Skills

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

  • 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
  • Develop data integration procedures using Webservice modelling from Azure Machine Learning 

Other Information

Funding Validity Period: Until 31-Dec-2022

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

NICF-Statistical Thinking for Data Science and Analytics (SF) (Synchronous and Asynchronous E-Learning)

You will learn descriptive statistics, basic probability, random variables, sampling and confidence intervals and hypothesis testing using R

Session Plan

More Details

Learning Outcome

Knowledge

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

 

  • Range of statistical and advanced computational modelling techniques
  • Advanced mathematical models and theories
  • Elements of various Statistics and probability 
  • Develop understanding of Hypoethesis testing

Skills

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

  • Sampling analysis using jupyter notebooks  
  • Descriptive statistics 
  • Conduct probability analysis
  • Apply the IRAC framework to real-world cases

  • Analyse Recidivism data set and context.

Other Information

Funding Validity Period: Until 31-Dec-2022

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 data analytics from machine learning models using R and Azure Machine Learning.

Session Plan

More Details

Learning Outcome

Knowledge

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

  • 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

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 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-2022

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 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 Azure
  • Implement a predictive solution in Azure
  • Build real-time machine learning solutions in Azure
  • 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

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 Azure
  • 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-2022

Course Developer: Lithan Academy

Qualification Course Code: TGS-2019503390

Pricing and Funding

SGD 18000.00

Pricing

Fee Description

Detailed Breakdown

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