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

Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!

4.60 (34580 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Successfully perform all steps in a complex Data Science project

Create Basic Tableau Visualisations

Perform Data Mining in Tableau

Understand how to apply the Chi-Squared statistical test

Apply Ordinary Least Squares method to Create Linear Regressions

Assess R-Squared for all types of models

Assess the Adjusted R-Squared for all types of models

Create a Simple Linear Regression (SLR)

Create a Multiple Linear Regression (MLR)

Create Dummy Variables

Interpret coefficients of an MLR

Read statistical software output for created models

Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models

Create a Logistic Regression

Intuitively understand a Logistic Regression

Operate with False Positives and False Negatives and know the difference

Read a Confusion Matrix

Create a Robust Geodemographic Segmentation Model

Transform independent variables for modelling purposes

Derive new independent variables for modelling purposes

Check for multicollinearity using VIF and the correlation matrix

Understand the intuition of multicollinearity

Apply the Cumulative Accuracy Profile (CAP) to assess models

Build the CAP curve in Excel

Use Training and Test data to build robust models

Derive insights from the CAP curve

Understand the Odds Ratio

Derive business insights from the coefficients of a logistic regression

Understand what model deterioration actually looks like

Apply three levels of model maintenance to prevent model deterioration

Install and navigate SQL Server

Install and navigate Microsoft Visual Studio Shell

Clean data and look for anomalies

Use SQL Server Integration Services (SSIS) to upload data into a database

Create Conditional Splits in SSIS

Deal with Text Qualifier errors in RAW data

Create Scripts in SQL

Apply SQL to Data Science projects

Create stored procedures in SQL

Present Data Science projects to stakeholders

Course Gallery

Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025] – Screenshot 1 — Screenshot 1 – Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025]

Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025] – Screenshot 2 — Screenshot 2 – Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025]

Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025] – Screenshot 3 — Screenshot 3 – Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025]

Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025] – Screenshot 4 — Screenshot 4 – Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025]

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Comidoc Review

Our Verdict

Offering a rich learning experience complete with real-analytics examples, this Data Science A-Z course stands out with hands-on exercises that cover essential topics like Data Mining, Modeling, and Tableau Visualization. However, the lack of homeworks based on actual job scenarios limits practical application and assessment, while minor inconsistencies in video instructions may demand extra learner diligence. While awaiting an update for outdated software demonstrations, you can rest assured that Kirill's enthusiastic explanations and TAs' prompt assistance will make your Data Science learning journey enjoyable and enriching.

What We Liked

In-depth coverage of Data Science concepts with real-analytics examples
Comprehensive exercises on Data Mining, Modeling, and Tableau Visualization
Knowledgeable guidance on Chi-Squared test, OLS method, and Linear Regression
Clearly explained Logistic Regression and model maintenance strategies

Potential Drawbacks

Some instructions have become outdated, making it challenging to follow on occasion
Lack of real-job-scenario homework assignments limits practical application and assessment
Minor inconsistency in video regarding label formatting requires learner vigilance
Integration between Tableau and machine learning streams could be improved