📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Data Science and Analytics Using Python, from foundations to a certified capstone project.
Toolkit
• pandas for loading, cleaning and reshaping tabular data
• NumPy vectorisation and avoiding slow row-wise loops
• Notebook discipline: execution order, hidden state and reproducibility
Exploration
• Profiling, missingness patterns and outlier investigation
• Exploratory visualisation with matplotlib and seaborn
• Forming hypotheses from exploration without confirming them on the same data
Statistics
• Sampling, confidence intervals and bootstrap methods
• Hypothesis testing, multiple comparisons and practical significance
• Correlation, confounding and the limits of observational analysis
Modelling
• Pipelines, encoding and preventing leakage across the split
• Regression and classification with honest validation
• Feature importance and its frequent misinterpretation
Delivery
• Dashboards and reports that answer a stated question
• Presenting uncertainty to decision-makers
• Packaging an analysis for handover and rerun
Earn government-registered certification in Data Science and Analytics Using Python
e-Certificate and e-Marksheet issued on successful completion.
