Course Outline

Day 1 

  • Data Science: an overview
  • Practical part: Let’s get started with Python - Basic features of the language 
  • The data science life cycle - part 1
  • Practical part: Working with structured data - the Pandas library

Day 2 

  • The data science life cycle - part 2
  • Practical part: dealing with real data
  • Data visualisation
  • Practical part: the Matplotlib library

Day 3

  • SQL - part 1
  • Practical part: Creating a MySql database with tables, inserting data and performing simple queries 
  • SQL part 2
  • Practical part: Integrating MySql and Python 

Day 4

  • Supervised learning part 1
  • Practical part: regression
  • Supervised learning part 2
  • Practical part: classification

Day 5

  • Supervised learning part 3
  • Practical part: building a spam filter
  • Unsupervised learning
  • Practical part: Clustering images with k-means


  • An understanding of mathematics and statistics.
  • Some programming experience, preferably in Python.


  • Professionals interested in making a career change 
  • People curious about Data Science and Data Analytics
  35 Hours


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