Data Science & Analytics Mastery
Comprehensive academic and industrial curriculum covering mathematical foundations, exploratory data analysis (EDA), NumPy tensor operations, Pandas data wrangling, data visualization, inferential statistics, hypothesis testing, SQL for analytics, and applied machine learning models.
Introduction to Data Science & Analytics
Foundations of data science, comparison with machine learning and analytics, structured, semi-structured, and unstructured data taxonomy, the 4 tiers of analytics maturity, the 6-stage lifecycle, modern data roles, and industry case studies.
Data Fundamentals
Measurement scales (Nominal, Ordinal, Interval, Ratio), qualitative vs quantitative, discrete vs continuous, primary vs secondary data, file formats (CSV, Excel, JSON, XML, SQL), 6 dimensions of data quality, missing data, duplicates, outliers (IQR/Z-score), and data privacy/ethics.
Microsoft Excel for Analytics
Calculation engine mechanics, relative/absolute cell referencing, text and date functions, logical branching (IF, AND, OR, IFS), lookup architectures (VLOOKUP, INDEX-MATCH, XLOOKUP), Pivot Tables, interactive dashboards, Power Query ETL, and Power Pivot DAX modeling.
SQL for Data Analysis
Relational architecture, primary/foreign keys, DDL/DML, logical query execution pipeline, filtering, aggregation (GROUP BY, HAVING), multi-table joins (Inner, Left, Right, Full), subqueries, CTEs, analytical window functions, views, B-Tree indexes, and data cleansing.
Statistics and Probability
Measures of central tendency and dispersion (variance, SD, Bessel's correction), distribution shapes (skewness, kurtosis), probability rules, Normal and Binomial distributions, Central Limit Theorem, confidence intervals, hypothesis testing (p-values, Type I/II errors), T-tests, Chi-square, ANOVA, correlation, and linear regression.
Python for Data Science
CPython runtime architecture and PVM bytecode execution, Jupyter Notebook client-kernel IPC, Anaconda environments, dynamic typing and heap object references, operators, control flow, functional scoping, data structures (Lists, Tuples, Sets, Dictionaries), text normalization, context managers, and pipeline exception handling.
NumPy & Pandas
N-dimensional array architecture (ndarray), contiguous memory buffers, strided indexing, vectorized ufuncs, broadcasting algebra, random number generators, Pandas Series and DataFrame BlockManager internals, multi-modal file ingestion, data cleaning, split-apply-combine aggregations, relational joins, pivot tables, and time-series resampling.
Data Cleaning & Preparation
Systematic data profiling, the 6 data quality dimensions, Rubin's missing data mechanisms (MCAR, MAR, MNAR), univariate and multivariate KNN imputation, exact and fuzzy deduplication, text normalization, Tukey's IQR and Z-score outlier mitigation, categorical encodings, feature scaling theorems, domain transformations, and preventing data leakage via Scikit-Learn pipelines.
Exploratory Data Analysis
John Tukey's investigative philosophy, Anscombe's Quartet, structural dataset profiling, five-number summary statistics, higher-order distribution moments (skewness and kurtosis), univariate-bivariate-multivariate analytical pipelines, correlation algebra, Simpson's Paradox, pattern recognition, and executive insight generation.
Data Visualization with Python
Cleveland-McGill perceptual hierarchies, Edward Tufte's Data-Ink ratio, chart typology and selection taxonomies, Matplotlib's Object-Oriented Artist architecture, high-level statistical plotting with Seaborn, interactive declarative computing with Plotly and Bokeh, and executive dashboard design frameworks.
Business Intelligence Tools
Ralph Kimball's dimensional modeling, star schemas, Power BI ecosystem, Power Query M-language ETL with query folding, VertiPaq in-memory columnar database mechanics, DAX row context vs. filter context transition, dynamic measures, Tableau VizQL query translation, blue vs. green pill algebra, Level of Detail (LOD) expressions, and corporate analytical governance.
Introduction to Machine Learning
Tom Mitchell's formal learning framework, AI vs. ML vs. Deep Learning taxonomy, the three core paradigms (Supervised, Unsupervised, Reinforcement), feature-label design matrices, 3-way dataset partitioning, gradient descent optimization, the Bias-Variance tradeoff, decision threshold tuning, and comprehensive classification/regression evaluation metrics.
Supervised & Unsupervised Learning
Ordinary Least Squares (OLS) regression assumptions, polynomial expansions, Logistic Regression log-odds geometry, K-Nearest Neighbors, Naive Bayes conditional probability, Support Vector Machines (SVM) maximum-margin hyperplanes, CART Decision Trees, Random Forest ensembles, K-Means and Hierarchical clustering, PCA eigendecomposition, and Market Basket Association Rule mining.
Model Improvement & Optimization
Empirical learning curve diagnostics, K-fold and stratified cross-validation architectures, mathematical regularization (L1 Lasso, L2 Ridge, ElasticNet), automated feature selection (filter, wrapper, embedded), hyperparameter optimization (Grid, Random, Bayesian), severe class imbalance mitigation (SMOTE, class weighting), and production deployment engineering with FastAPI and Docker.
Big Data & Cloud Analytics
The 5 V's taxonomy, Brewer's CAP Theorem in distributed databases, Apache Hadoop HDFS block storage and MapReduce execution, Apache Spark in-memory architecture (Catalyst optimizer, Project Tungsten), modern storage topographies (Warehouses, Lakes, and ACID Lakehouses with Delta Lake and Iceberg), cloud analytics stacks across AWS, GCP, and Azure, and production ELT pipeline orchestration via Apache Airflow DAGs.
End-to-End Analytics Capstone
Production-grade end-to-end analytics and machine learning capstone: data ingestion, automated profiling, feature engineering pipelines, cross-validated model selection, executive BI dashboarding, and containerized REST deployment.