Data Visualization with Python
By the end of this course, learners should be able to explain Lesson 1: Set up Python for data work
Unlock your potential with this carefully crafted course on Data Visualization with Python. We've distilled years of industry experience into actionable lessons that you can apply immediately.
This course covers both theoretical foundations and practical applications. Each module builds on the previous one, ensuring a smooth learning journey.
Perfect for self-learners and professionals seeking to expand their skillset in the growing African tech ecosystem.
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 1: Data science project workflow
- By the end of this course, learners should be able to explain Lesson 2: Visualize insights with charts
Skills you will develop
Data storytelling · Data visualisation · 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 Data Visualization with Python
- Quiz: Lesson 1: Set up Python for data work check (pass 70%)
- Final assessment: Data Visualization with Python — 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%
- Data Visualization with Python — 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 22min
- Lesson 2: Jupyter notebooks and workflows Preview 19min
- Lesson 3: NumPy arrays and vectorization 23min
Week 2: Core Concepts
- Lesson 1: Data science project workflow 10min
- Lesson 2: Visualize insights with charts 14min
- Lesson 3: Feature engineering basics 18min
Week 3: Hands-On Practice
- Lesson 1: Data science project workflow 12min
- Lesson 2: Visualize insights with charts 24min
- Lesson 3: Feature engineering basics 23min
- Lesson 4: Train a simple predictive model 10min
- Lesson 5: Evaluate model quality 17min
- Lesson 6: Communicate findings clearly 16min
Week 4: Intermediate Techniques
- Lesson 1: Data science project workflow 12min
- Lesson 2: Visualize insights with charts 11min
- Lesson 3: Feature engineering basics 21min
Free to enroll — no purchase required.
Instructor: Africoders
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