Most industry analysis starts with exploratory data analysis and a thorough study of this will help you to perform data health checks and provide initial business insights. You will gain a sound understanding of Python and R programming, descriptive statistics, data management and data visualisation. You will also learn SQL for big data pre- processing and prepare data for big data analytics. The module serves as an essential foundation for advanced analytics taught later in the course.

Statistical inference is the process of drawing inferences or conclusions from data using statistical techniques. This is at the core of data science, and a strong understanding of statistics from the beginning is the prime ingredient for a competent data scientist. In this module, you will cover sampling, statistical distribution, hypothesis testing, and variance analysis and use R code to carry out various statistical tests and draw inferences from their output.

Power BI and Excel are fundamental parts of the data analytics toolkit. A strong understanding in these also provides a basis for more advanced data analytics with other techniques and technologies. In this module, you’ll carry out data analytics using Excel and create data visualisations and dashboards using Power BI.

Solutions to many business problems are related to successfully predicting future outcomes. This module introduces predictive modelling and provides a foundation for more advanced methods and machine learning. You’ll gain an understanding of the general approach to predictive modelling and then build simple and multiple linear regression models in Python and R and apply these in a range of contexts.

This module provides learners with an opportunity to apply knowledge through project work. They will be able to select a project from a specific domain and carry out various data management, exploratory data analysis, data visualisation and predictive modelling tasks to produce analysis, insights and recommendations.