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