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Data Analysis Basics

Exploratory data analysis, NumPy vectorization, Pandas wrangling, split-apply-combine aggregations, statistical profiling, and Matplotlib data visualization.

1NumPy Vectorization & N-Dimensional Array Operations

Contiguous memory layouts, strides calculation, zero-copy views, broadcasting rules, and hardware SIMD vectorization.

NumPy Vectorization & N-Dimensional Array Operations

Examine the ndarray C struct, memory stride offsets, broadcasting rules, and AVX SIMD vector acceleration.

NumPy Vectorization & N-Dimensional Array Operations

Deconstruct NumPy ndarray memory layouts, calculate byte strides, simulate zero-copy broadcasting rules, and analyze hardware SIMD parallelism.

2Pandas DataFrames, Wrangling & Aggregation

Columnar BlockManager architecture, Split-Apply-Combine groupby algorithms, in-memory hash joins, and null imputation.

Pandas DataFrames, Wrangling & Aggregation

Explore columnar memory layouts, Split-Apply-Combine group reductions, hash join relational mechanics, and missing data imputation.

Pandas DataFrames, Wrangling & Aggregation

Master Pandas columnar DataFrame architecture, implement hash joins, execute Split-Apply-Combine GroupBy reductions, and evaluate missing data imputation.

3Exploratory Data Analysis (EDA) & Summary Statistics

Central tendency, Bessel-corrected variance, Tukey 1.5 IQR outlier detection, Pearson linear correlation, and Anscombe's quartet.

Exploratory Data Analysis (EDA) & Summary Statistics

Analyze parametric vs robust summary statistics, Tukey outlier fences, Pearson vs Spearman correlation, and distributional shape.

Exploratory Data Analysis (EDA) & Summary Statistics

Apply rigorous exploratory data analysis: compute central tendency, standard deviation, Tukey 1.5 IQR outlier fences, and bivariate correlation coefficients.

4Data Visualization with Matplotlib & Seaborn

Leland Wilkinson's Grammar of Graphics, Matplotlib Figure/Axes object hierarchy, Gaussian Kernel Density Estimation, and accessible colormaps.

Data Visualization with Matplotlib & Seaborn

Master Grammar of Graphics layers, explicit Matplotlib Figure/Axes APIs, Gaussian KDE mathematics, and perceptually uniform colormaps.

Data Visualization with Matplotlib & Seaborn

Architect production visualization pipelines: implement Grammar of Graphics layers, manage Matplotlib Figure/Axes hierarchies, compute Gaussian KDE distributions, and deploy accessible colormaps.