Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025]

Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025]

Overview

Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025] is a practical, project-driven introduction to the complete data science lifecycle. Instead of focusing only on theory, the course walks learners through real analytical workflows, from raw data preparation to modeling, evaluation, and business interpretation.

The course emphasizes how data science is actually applied in real-world scenarios—including messy data, imperfect assumptions, and decision-making under uncertainty. Learners gain exposure to statistics, regression modeling, SQL, and Tableau visualization, all within a single, structured learning path.

It is designed to build intuition first, then reinforce it with hands-on exercises and realistic case studies.

Course Snapshot

• Instructors: Kirill Eremenko, Ligency Team
• Students enrolled: 220,000+
• Content length: ~21 hours
• Difficulty level: Beginner to Intermediate
• Language: English (auto captions available)
• Certification: Certificate of completion
• Access: Lifetime (mobile & TV supported)

What This Course Actually Covers

This course is structured around the end-to-end data science process, rather than isolated tools. Learners follow the same sequence used in professional analytics projects:

• Understanding business problems through data
• Cleaning and preparing real datasets
• Applying statistical and regression techniques
• Building and validating predictive models
• Visualizing insights for stakeholders
• Presenting results in a clear, decision-focused way

The focus remains practical, with frequent exercises designed to mirror real analytics tasks.

Skills & Concepts You’ll Work With

Data Preparation & Exploration

• Cleaning raw datasets and handling anomalies
• Identifying missing values and inconsistencies
• Transforming and deriving new variables
• Preparing datasets for modeling and analysis

Statistics & Regression Modeling

• Applying simple and multiple linear regression
• Understanding coefficients and statistical significance
• Using R-squared and adjusted R-squared for evaluation
• Performing logistic regression for classification problems
• Interpreting odds ratios and confusion matrices

Model Evaluation & Optimization

• Training vs test data splits
• Preventing model deterioration
• Understanding false positives and false negatives
• Applying CAP curves for model assessment
• Using variable selection techniques (forward, backward, bidirectional)

SQL for Data Science

• Installing and navigating SQL Server
• Writing SQL queries for analytical use cases
• Creating stored procedures
• Using SQL Server Integration Services (SSIS)
• Uploading, cleaning, and transforming data via SQL

Data Visualization & Communication

• Creating Tableau visualizations
• Performing basic data mining in Tableau
• Building geodemographic segmentation models
• Translating analytical results into business insights
• Presenting findings to non-technical stakeholders
  
  

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Who This Course Is Best Suited For

• Beginners starting their journey in data science
• Students learning analytics, statistics, or business intelligence
• Professionals transitioning into data-driven roles
• Learners preparing for more advanced machine learning courses
• Anyone who wants practical exposure, not just theory

Common Questions Learners Ask

Do I need prior data science experience?
No. The course starts from fundamentals and builds gradually.

Is this course more theoretical or practical?
It is strongly practical, with real datasets and hands-on exercises.

Does it include SQL and visualization?
Yes. SQL Server and Tableau are both integrated into the learning flow.

Is this a machine learning course?
It focuses on statistical modeling and applied analytics. Advanced ML is not the main goal.

Can this help with real-world projects or interviews?
Yes. The workflow mirrors how data science problems are approached in practice.

Practical Value

What makes this course valuable is its realistic approach. Learners don’t work with perfectly clean data or ideal assumptions. Instead, they experience common challenges faced by data scientists—data quality issues, model limitations, and the need to justify results to stakeholders.

This builds not just technical skills, but analytical thinking and decision-making confidence.

Final Thoughts

If you want a hands-on introduction to data science that reflects real analytical work, Data Science A-Z provides a strong foundation. It’s especially suitable for learners who want to understand the full journey—from raw data to actionable insights—before moving into advanced machine learning or specialization tracks.