Statistical Analysis with Python
By the end of this course, learners should be able to explain Lesson 1: Set up Python for data work
This comprehensive course will take you from beginner to confident practitioner in Statistical Analysis with Python. You'll learn industry-standard techniques and best practices used by professionals worldwide.
Through hands-on projects and real-world examples, you'll gain the skills needed to succeed in today's competitive tech landscape. Our curriculum is designed by industry experts with years of experience.
By the end of this course, you'll have a portfolio of projects to showcase your abilities and the confidence to apply your knowledge in professional settings.
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
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 Statistical Analysis with Python
- Quiz: Lesson 1: Set up Python for data work check (pass 70%)
- Final assessment: Statistical Analysis with Python — course assessment (pass 70%)
Course journey
How this free course is structured — watch, practise, and progress module by module.
- Week 1: Data Science Foundations
- Week 2: Data Wrangling
- Week 3: Visualization & Analysis
- Week 4: Final Analytics Project
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%
- Statistical Analysis 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: Week 1: Data Science Foundations
- Lesson 1: Set up Python for data work Preview Practice 16min
- Lesson 2: Jupyter notebooks and workflows Preview 35min
- Lesson 3: NumPy arrays and vectorization 35min
Week 2: Week 2: Data Wrangling
- Lesson 1: Data science project workflow 28min
- Lesson 2: Visualize insights with charts 45min
- Lesson 3: Feature engineering basics 19min
Week 3: Week 3: Visualization & Analysis
- Lesson 1: Data science project workflow 21min
- Lesson 2: Visualize insights with charts 24min
- Lesson 3: Feature engineering basics 43min
Week 4: Week 4: Final Analytics Project
- Lesson 1: Data science project workflow 45min
- Lesson 2: Visualize insights with charts 18min
- Lesson 3: Feature engineering basics 24min
Free to enroll — no purchase required.
Instructor: Africoders
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