Kirill Eremenko - Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2026] (Download)

Data science positions continue to dominate hiring trends, yet most aspiring analysts hit the same wall—academic theory doesn’t prepare you for messy real-world data. Corrupt files, missing values, anomalies that break your models—this is where most learning stops and frustration begins.

This data science course takes the opposite approach. Instead of sanitized datasets, you’ll wrestle with the actual chaos data scientists face daily, building practical skills through hands-on problem-solving across the complete analytics workflow.

Course Structure and Instructors

Created by Kirill Eremenko, the SuperDataScience Team, and Ligency, this program offers flexible learning pathways. You can follow preset tracks targeting specific skills or complete the full curriculum for comprehensive data science mastery.

The course divides into four core parts: Visualization, Modeling, Data Preparation, and Communication—covering the entire data science lifecycle from raw data intake to executive presentations.

What You’ll Learn

Part 1: Visualization and Data Mining

Master Tableau for creating visualizations and conducting data mining. You’ll build calculated fields, apply statistical tests like Chi-Squared, identify anomalies in datasets, and validate findings through multiple analytical approaches.

Part 2: Statistical Modeling

Develop regression models from simple linear to complex logistic regressions. Learn to interpret coefficients, assess model quality using R-Squared and Adjusted R-Squared, handle dummy variables, and apply techniques like Backward Elimination and Forward Selection for model optimization.

Build a robust geodemographic segmentation model, understanding multicollinearity through VIF and correlation matrices. Master the Cumulative Accuracy Profile (CAP) for model assessment, work with training and test datasets to prevent overfitting, and derive business insights from odds ratios and coefficients.

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Part 3: Data Preparation and ETL

Install and navigate SQL Server and Microsoft Visual Studio Shell. Use SQL Server Integration Services (SSIS) to upload data, create conditional splits, handle text qualifier errors, and clean messy datasets. Write SQL scripts and stored procedures specifically for data science applications.

Learn the complete ETL (Extract, Transform, Load) process through data wrangling before loading, step-by-step SSIS implementation, and post-load data preparation using RAW, WRK, and DRV table structures.

Part 4: Communication Skills

Discover how to present findings effectively to stakeholders and executives. Navigate cross-departmental collaboration, set realistic expectations, and deliver compelling presentations that translate technical results into business value.

Who This Course Serves

Perfect for anyone wanting practical data mining, statistical modeling, or data preparation skills. Whether you’re starting fresh or improving existing capabilities, the challenging homework and real-world scenarios prepare you for actual industry demands rather than idealized textbook scenarios.

The course provides all datasets, templates, and hands-on exercises designed to push your problem-solving abilities while building confidence with industry-standard tools.

Ready to handle data science challenges the real world throws at you? Start building production-ready skills today.

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Kirill Eremenko – Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2026] (Download)

Course Structure and Instructors