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Course Outline
Day 1
- Data Science: An Overview
- Practical Session: Getting Started with Python - Core Language Features
- The Data Science Lifecycle - Part 1
- Practical Session: Manipulating Structured Data with the Pandas Library
Day 2
- The Data Science Lifecycle - Part 2
- Practical Session: Working with Real-World Data
- Data Visualization
- Practical Session: Utilizing the Matplotlib Library
Day 3
- SQL - Part 1
- Practical Session: Establishing a MySQL Database, Creating Tables, Inserting Data, and Executing Basic Queries
- SQL - Part 2
- Practical Session: Integrating MySQL with Python
Day 4
- Supervised Learning - Part 1
- Practical Session: Regression Analysis
- Supervised Learning - Part 2
- Practical Session: Classification Techniques
Day 5
- Supervised Learning - Part 3
- Practical Session: Building a Spam Filter
- Unsupervised Learning
- Practical Session: Clustering Images Using K-Means
Requirements
- A foundational knowledge of mathematics and statistics.
- Some prior programming experience, with preference given to Python.
Target Audience
- Professionals seeking a career transition.
- Individuals curious about exploring 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.
