Data Science & Big Data Engineering
Master the complete enterprise data lifecycle. Build end-to-end predictive machine learning pipelines, execute distributed computing with Apache Spark, design Snowflake cloud data lakes, and stream real-time telemetry with Apache Kafka.
Core Competencies You Will Build
Engineered to bridge statistical data modeling with high-throughput distributed big data architectures.
Master hypothesis testing, exploratory data analysis (EDA), NumPy, Pandas vectorization, and Scikit-Learn workflows.
Process terabyte-scale distributed dataframes, Spark SQL, and RDD transformations using PySpark on AWS EMR.
Design Kimball dimensional models, star schemas, Snowflake data warehouses, and Delta Lake Lakehouse architectures.
Construct automated DAG scheduling pipelines with Apache Airflow, dbt transformations, and Apache Kafka message queues.
Curriculum & Technical Roadmap
Structured modular progression from foundational statistical coding to enterprise big data streaming.
- Advanced SQL: Window functions, CTEs, self-joins, query optimization, and indexing
- Pandas & NumPy: Memory optimization, vectorization, handling missingness, and outlier mitigation
- Inferential Statistics: Central Limit Theorem, confidence intervals, A/B testing, and ANOVA
- Exploratory Data Analysis (EDA) and interactive visualization with Seaborn & Plotly
- Supervised Regression & Classification: Ridge, Lasso, Logistic Regression, and Decision Trees
- Ensemble Modeling: Gradient Boosting, XGBoost, LightGBM, and Random Forests
- Unsupervised Learning: K-Means clustering, hierarchical clustering, and PCA dimensionality reduction
- Model Evaluation: ROC-AUC, precision-recall curve, cross-validation, and Optuna hyperparameter sweeps
- Hadoop ecosystem architecture: HDFS, YARN, and MapReduce paradigms
- PySpark fundamentals: Resilient Distributed Datasets (RDDs) and Spark DataFrames
- Spark SQL, Catalyst Optimizer, execution plans, partition tuning, and shuffle management
- Building scalable distributed ML pipelines with Spark MLlib
- Snowflake multi-cluster shared data architecture, virtual warehouses, and time travel
- Dimensional data modeling: Fact vs. Dimension tables, SCD Type 1/2, and Star/Snowflake schemas
- Data transformation and lineage management using dbt (data build tool)
- Delta Lake format: ACID transactions, schema enforcement, and time-travel querying on S3
- Event streaming architecture with Apache Kafka: Topics, partitions, producers, and consumer groups
- Spark Structured Streaming: Processing real-time event feeds with sliding/tumbling windows
- Workflow orchestration: Writing production DAGs, task sensors, and backfills in Apache Airflow
- CI/CD data pipeline testing, Great Expectations data quality checks, and production monitoring
Terabyte-Scale Capstone Projects
Deploy real-world data pipelines and predictive models to highlight rigorous engineering depth.
E-Commerce Real-Time Clickstream Pipeline
Ingest 1M+ live events/sec via Apache Kafka, process aggregations in Spark Streaming, and sink data into a Snowflake warehouse.
Predictive Customer Churn & LTV System
Construct an automated ML pipeline with XGBoost and Optuna that scores subscription cancellation risk across 500k+ customer accounts.
Automated Financial Lakehouse with dbt & Airflow
Orchestrate daily automated banking ETL DAGs in Apache Airflow, applying dbt transformations and Great Expectations quality suites.
Log Analytics & Anomaly Detection Engine
Deploy a PySpark cluster on AWS EMR to parse 500GB+ server access logs, detect security intrusions, and visualize spikes on interactive dashboards.
Tools & Frameworks Covered
Industry-standard big data, analytics, and distributed cloud computing technologies.