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Curriculum
- 14 Sections
- 55 Lessons
- 10 Weeks
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- Course Introductions4
- NumPy Basics10
- 2.1Introduction to NumPy and its importance
- 2.2Creating arrays and basic array operations
- 2.3Indexing and slicing in NumPy arrays
- 2.4Reshaping and manipulating arrays
- 2.5Statistical operations with NumPy
- 2.6Broadcasting and advanced indexing
- 2.7Working with random numbers in NumPy
- 2.8Mathematical functions in NumPy
- 2.9Linear algebra with NumPy
- 2.10NumPy project: Data manipulation and analysis
- Pandas Basics4
- Evaluation & Presentation (Mid-course)1
- Advanced Pandas6
- Matplotlib Basics & Visualization5
- Data Science Workflow & EDA4
- Evaluation & Presentation (Mid-course)1
- Seaborn & Advanced Visualization4
- Statistics & Probability7
- 10.1Mini Project → Statistical analysis on dataset
- 10.2Linear regression (concepts only)
- 10.3Probability basics & distributions (Normal, Binomial)
- 10.4Descriptive statistics (mean, median, variance, std)
- 10.5Correlation & Covariance
- 10.6Practice – visualize distributions with Seaborn
- 10.7Mini Project → Statistical analysis on dataset
- Introduction to Machine Learning4
- Version Control & Career Guidance2
- Wrap-up & Final Assessment2
- Completion Formalities1












