Python for Data Analysis
By the end of this course, learners should be able to explain Lesson 1: Set up Python for data work
Master Python for Data Analysis with this in-depth training program designed for the African tech community. Whether you're just starting out or looking to level up your skills, this course provides everything you need.
You'll work on practical exercises that mirror real-world challenges faced by developers and tech professionals. Our step-by-step approach ensures you understand not just the 'how' but also the 'why'.
Join thousands of students who have transformed their careers through our courses.
What you will learn
- By the end of this course, learners should be able to explain Lesson 1: Set up Python for data work
- By the end of this course, learners should be able to explain Lesson 2: Jupyter notebooks and workflows
- By the end of this course, learners should be able to explain Lesson 3: NumPy arrays and vectorization
- By the end of this course, learners should be able to explain Lesson 4: Load datasets with Pandas
- By the end of this course, learners should be able to explain Lesson 1: Data science project workflow
Skills you will develop
Analytical thinking · Data cleaning and validation · Python for analytics basics
Skills you’ll develop
Course completion demonstrates these skills. Verification requires assessment or reviewed challenge work.
What you will build and prove
- Practice: Practice: Lesson 1: Set up Python for data work
- Project: Apply Python for Data Analysis
- Quiz: Lesson 1: Set up Python for data work check (pass 70%)
- Final assessment: Python for Data Analysis — course assessment (pass 70%)
Course journey
How this free course is structured — watch, practise, and progress module by module.
- Introduction & Setup
- Core Concepts
- Hands-On Practice
- Intermediate Techniques
Practice
1 optional practice exercise — apply what you learned. Practice is not required to complete the course.
Assessment
- Lesson 1: Set up Python for data work check · pass mark 70%
- Python for Data Analysis — course assessment · pass mark 70%
Prove
Course completion means finishing required lessons , and the course assessment . That grants a certificate of completion — not a professional certification. Optional practice does not block completion.
Curriculum
Week 1: Introduction & Setup
- Lesson 1: Set up Python for data work Preview Practice 12min
- Lesson 2: Jupyter notebooks and workflows Preview 24min
- Lesson 3: NumPy arrays and vectorization 11min
- Lesson 4: Load datasets with Pandas 23min
Week 2: Core Concepts
- Lesson 1: Data science project workflow 16min
- Lesson 2: Visualize insights with charts 17min
- Lesson 3: Feature engineering basics 14min
- Lesson 4: Train a simple predictive model 20min
- Lesson 5: Evaluate model quality 24min
Week 3: Hands-On Practice
- Lesson 1: Data science project workflow 12min
- Lesson 2: Visualize insights with charts 20min
- Lesson 3: Feature engineering basics 17min
- Lesson 4: Train a simple predictive model 13min
- Lesson 5: Evaluate model quality 15min
Week 4: Intermediate Techniques
- Lesson 1: Data science project workflow 25min
- Lesson 2: Visualize insights with charts 19min
- Lesson 3: Feature engineering basics 15min
- Lesson 4: Train a simple predictive model 15min
- Lesson 5: Evaluate model quality 9min
Free to enroll — no purchase required.
Instructor: Africoders
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