beginner Level
Self-Paced Track
Certificate Included
Foundations of Data Science
Master the complete mathematical and computational foundations of Data Science. From CRISP-DM engineering, descriptive statistics, Python, NumPy, and Pandas to probability, the Central Limit Theorem, and Chi-Square tests.
32h 52m total content
71 lessons & projects
Instructor: Atul Jha
Course Overview
Why This Course Matters
Modern Artificial Intelligence and Machine Learning algorithms cannot function without solid data foundations. Knowing how to systematically collect, store, clean, summarize, model, and infer statistical guarantees from empirical data is what separates true data scientists from superficial model-fitters.
What You Will Master
- The Data Science Lifecycle & Systems: CRISP-DM methodology, engineering architectures, and problem framing.
- Descriptive Statistics: Centrality (mean, median, mode), spread (variance, standard deviation, IQR), outliers, and distribution shapes.
- Applied Python Stack: Deep dives into Python primitives, vectorized NumPy multidimensional arrays, and tabular manipulation with Pandas.
- Data Visualization: Constructing clear histograms, scatter plots, violin plots, pair grids, and compositional charts with Seaborn and Matplotlib.
- Probability & Counting: Combinatorics, sample spaces, conditional probability, Bayes’ Rule, and discrete distributions (Binomial, Geometric).
- Inferential Statistics: Continuous densities, Gaussian distributions, the Central Limit Theorem (CLT), and Chi-Square hypothesis testing.
Key Competencies You'll Build
Mathematical foundations written from pure first principles
Clean, reproducible PyTorch / Python code without black boxes
Production deployment and low-latency inference patterns
Hands-on capstone portfolio piece ready for GitHub
Course Curriculum
22 chapters • 71 total lessons & checkpoints