Data Science Deep Dive

Go beyond the basics — master statistics, data engineering, visualization, big data tools, and end-to-end analytics workflows used in real industry roles.

Roadmap

  1. Statistics & Probability

    Descriptive & inferential statistics, hypothesis testing, confidence intervals, and probability distributions.

  2. Data Wrangling & Visualization

    Pandas, NumPy for data cleaning, and visualization with Matplotlib, Seaborn, and Plotly.

  3. SQL for Analytics

    Advanced SQL — window functions, CTEs, subqueries, and query optimization for analytics workloads.

  4. Big Data Tools

    Introduction to Apache Spark, the Hadoop ecosystem, and distributed data processing concepts.

  5. Business Intelligence

    Building dashboards with Power BI / Tableau for storytelling with data and stakeholder reporting.

  6. Capstone Projects

    End-to-end analytics projects — from raw data ingestion to insights, visualization, and presentation.

Courses

Statistics for Data Science

Probability, distributions, hypothesis testing, and confidence intervals explained with practical examples from real datasets.

Foundation

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Data Visualization with Python

Create insightful charts and interactive dashboards using Matplotlib, Seaborn, and Plotly for storytelling.

Intermediate

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Advanced SQL for Analytics

Window functions, common table expressions, joins, and query optimization techniques for large datasets.

Intermediate

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Introduction to Apache Spark

Distributed data processing fundamentals with PySpark, including RDDs, DataFrames, and Spark SQL.

Advanced

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Power BI / Tableau Dashboards

Build interactive dashboards for business intelligence, KPI tracking, and executive reporting.

Intermediate

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A/B Testing & Experimentation

Design and analyze experiments to drive data-informed product decisions with statistical rigor.

Advanced

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Capstone Project Ideas

Sales Analytics Dashboard

Clean and analyze multi-year sales data, then build an interactive dashboard tracking revenue, growth, and seasonality.

Customer Churn Analysis

Combine SQL, statistics, and visualization to identify churn drivers and present actionable recommendations.

Big Data Pipeline with Spark

Process a large dataset with PySpark, perform transformations, and generate summary reports at scale.

Market Basket Analysis

Use association rule mining to discover purchasing patterns and inform product placement strategy.