Big Data Processing
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 Big Data Processing. 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
What you will build and prove
- Practice: Practice: Lesson 1: Set up Python for data work
- Project: Apply Big Data Processing
- Quiz: Lesson 1: Set up Python for data work check (pass 70%)
- Final assessment: Big Data Processing — 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
- Advanced Topics
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%
- Big Data Processing — 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 23min
- Lesson 2: Jupyter notebooks and workflows Preview 11min
- Lesson 3: NumPy arrays and vectorization 17min
Week 2: Core Concepts
- Lesson 1: Data science project workflow 23min
- Lesson 2: Visualize insights with charts 20min
- Lesson 3: Feature engineering basics 9min
- Lesson 4: Train a simple predictive model 13min
- Lesson 5: Evaluate model quality 12min
- Lesson 6: Communicate findings clearly 19min
Week 3: Hands-On Practice
- Lesson 1: Data science project workflow 15min
- Lesson 2: Visualize insights with charts 21min
- Lesson 3: Feature engineering basics 22min
- Lesson 4: Train a simple predictive model 21min
- Lesson 5: Evaluate model quality 19min
Week 4: Intermediate Techniques
- Lesson 1: Data science project workflow 14min
- Lesson 2: Visualize insights with charts 24min
- Lesson 3: Feature engineering basics 11min
- Lesson 4: Train a simple predictive model 9min
- Lesson 5: Evaluate model quality 13min
- Lesson 6: Communicate findings clearly 9min
Week 5: Advanced Topics
- Lesson 1: Data science project workflow 17min
- Lesson 2: Visualize insights with charts 22min
- Lesson 3: Feature engineering basics 15min
Free to enroll — no purchase required.
Instructor: Africoders
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