The complete 13-module curriculum: from NumPy vector computing, Pandas data wrangling, and cleaning to statistical inference, hypothesis testing, memory optimization, and end-to-end capstone EDA.
Complete all 13 milestones to unlock and download your verified certificate (13 remaining).
Foundations of data analytics, the four types of analytics (descriptive, diagnostic, predictive, prescriptive), and the analytics lifecycle.
Memory layout of ndarrays, vectorized arithmetic, broadcasting rules, indexing/slicing, and linear algebra routines.
Series and DataFrame structures, index mechanics, label-based (loc) vs position-based (iloc) selection, and boolean filtering.
Vectorization vs apply bottlenecks, groupby Split-Apply-Combine mechanics, aggregations, window transforms, and filtrations.
Missing data handling (MCAR/MAR/MNAR), deduplication, string parsing, and IQR/z-score outlier detection and mitigation.
Wide vs long formats, pivot tables, melt un-pivoting, hierarchical multi-index stacking/unstacking, and Tidy Data principles.
Central tendency (mean, median, mode), variance and standard deviation, skewness, kurtosis, and probability distributions.
Univariate distribution analysis, bivariate relationships, multivariate interactions, and Simpson's Paradox.
Matplotlib Figure/Axes architectures, Seaborn statistical plots, distribution KDEs, box/violin plots, and heatmaps.
Sample covariance, Pearson linear correlation, Spearman rank correlation, Kendall tau, and confounding variable pitfalls.
Central Limit Theorem, confidence intervals, null hypothesis testing, p-values, t-tests, ANOVA, and Chi-Square tests.
Memory profiling, numeric dtype downcasting, Categorical compression, chunked streaming, and Parquet columnar formats.
Complete analytics capstone: business question translation, data hygiene audit, rigorous EDA, and executive insight storytelling.