Most industry analysis starts with exploratory data analysis and a thorough study of this will help learners to perform data health checks and provide initial business insights.
The module will help the learner to understand and perform descriptive statistics and present the data using appropriate graphs/diagrams and serves as a foundation for advanced analytics.
This module also introduces the basics of programming in R and Python, the most commonly used languages used for data science.
The module culminates in practices related to data management, which is essential for both exploratory data analysis and advanced analytics. In particular, the module focuses on SQL as a highly practical language for data preprocessing, and addresses ways to connect SQL with R and Python tools, as well as learning the skills required to prepare data for machine learning and efficient data modelling.
R for Data Science: Import, Tidy, Transform, Visualise, and Model DataPaperback – 25 July 2016
by Garrett Grolemund (Author), Hadley Wickham (Author)
Hands-On Exploratory Data Analysis with Python: Perform EDA techniques to understand, summarise, and investigate your data Paperback – 27 Mar. 2020
by Suresh Kumar Mukhiya (Author), Usman Ahmed (Author)
Exploratory Data Analysis with R
Radhika Datar, Harish Garg
Publisher: Packt Publishing (31 May 2019)
Candidates who apply for this course must have a recognised undergraduate degree or equivalent. Candidates without a degree but with other relevant qualifications and/or work experience can also be considered.
English language competency at an IELTS 6.5 (or equivalent) is required of all applicants whose first language is not English. Where students can demonstrate previous substantial studies or work experience in English, this requirement can be waived.