Professional Diploma in Data Science (Up to 90% funding and job placement available)

Lithan Academy Pte Ltd


Course introduction

This programme will train and place you in a Data Analyst job to kick-start your data science career. You could receive up to 90% funding* on your training and job placement assistance to ensure a smooth transition to your new data science job role.  *Terms and conditions apply.



Course Benefits


Acquire Data Science Skills
Learn R programming, statistical modelling and machine learning models in R studio, Azure Machine Learning studio and Spark HDInsight to clean, transform, analyse, visualize and perform predictive analysis on both structured and unstructured data to prepare yourself for a Data Scientist role.

Mentor-Led Blended Learning Delivery
We deliver blended learning through a combination of self-paced e-learning, instructor-led flipped classes and personalised mentoring with industry practitioners to greatly increase your efficiency and effectiveness in acquiring knowledge and skills.

Job Placement Assistance
We will assist you in getting hired with more than 50 hiring employers working with us.

Up to 90% Funding
Singapore Citizens / Permanent Residents can receive up to 90% funding* from SkillsFuture Singapore and further subsidies using your SkillsFuture credit and Post-Secondary Education Account.
*Terms and conditions apply.



Course Pre-Requisites


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



Target Audience


Individuals who are interested in a Data Science career



Course Outline


Data Queries and Visualization Basics

  • 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
Basic R Programming
  • 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
Data Science Essentials
  • Demonstrate an understanding of data analytics lifecycle and its activities
  • Demonstrate an understanding of different analytical techniques and tools to perform analytics project
  • Demonstrate an 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
Statistical Thinking for Data Science and Analytics
  • 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 the relevant parties
  • Use Bayesian modelling and inference for forecasting and studying public opinion
  • Use Data to create compelling graphics
Principles of Machine Learning
  • 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 artefacts with 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 artefacts
  • 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
Spark on Azure HDInsight
  • 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



Available Course Sessions


Please click here to stay updated on upcoming sessions.



Trainer Profile


ACTA Certified and Industry Practitioner

All our trainers are WSQ Advanced Certificate in Training and Assessment (ACTA) certified and industry practitioner who can impart real-world experience through actual work examples or case studies.



 

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