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Course Outline
Day 1
- Data Science: An overview
- Practical session: Getting started with Python – Basic language features
- The Data Science lifecycle – Part 1
- Practical session: Working with structured data – The Pandas library
Day 2
- The Data Science lifecycle – Part 2
- Practical session: Working with real-world data
- Data visualisation
- Practical session: The Matplotlib library
Day 3
- SQL – Part 1
- Practical session: Creating a MySQL database with tables, inserting data, and executing simple queries
- SQL – Part 2
- Practical session: Integrating MySQL and Python
Day 4
- Supervised learning – Part 1
- Practical session: Regression
- Supervised learning – Part 2
- Practical session: Classification
Day 5
- Supervised learning – Part 3
- Practical session: Building a spam filter
- Unsupervised learning
- Practical session: Clustering images using k-means
Requirements
- A foundational understanding of mathematics and statistics.
- Some programming experience, preferably in Python.
Audience
- Professionals considering a career change
- Individuals curious about Data Science and Data Analytics
35 Hours
Testimonials (1)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
