Foundations of Data Science – Framework of data science functions and tools; applying Python and R in making sense of data and deriving information and intelligence for decision-making.

The Mathematics of Data Science – Applying important quantitative techniques and tools deployed in data analytics and model optimisation including Advanced Statistics, Linear Algebra, Probability and Calculus; Data shape and relationships.

Dealing With Data – Segregating and classifying data types; tackling data from varying sources; handling streaming and voluminous data; analysing, managing and harnessing massive data using tools and applications like Python and R.

Doing Data Analytics – Framework of computing using Hadoop; developing data models; capturing, processing, sorting, classifying, analysing, managing and harnessing big data using the Hadoop ecosystem; understanding the use of combination of tools like R, Python and MapReduce etc., in big data analytics.

Data Visualisation – Analysing data visualisation use-cases; choosing optimal data visualisation models using popular visualisation applications like Tableau to visualise and present information results.

Analytics in Machine Learning and AI – Scenarios and cases of applying big data analytics in machine learning and artificial intelligence; applying big data analytics models in ML and Ai systems; advanced R and Python deployment in ML and AI applications.

Business Implementation of Data Analytics – Planning, designing and implementing big data analytics solutions in organisations; deploying R and Python in business-level data analytics; going to production with R.

Store Streaming Data – Choosing storage tools for streaming massive data and data pipelines; applications of document-oriented NoSQL database in high volume data storage and processing using MongoDB; using messaging systems; deploying techniques of Elasticsearch enterprise big data search; and big data processing on cloud.