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!

4.57 (34456 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

Why take this course?

🚀 Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2024] 📊

Course Headline:

Dive Deep into Data Science - Master Analytics with Real-World Examples!

Course Description:

Extremely Hands-On... Incredibly Practical... Unbelievably Real! 🌊

This isn't your typical, smooth-sailing data science course. Data Science A-Z is a rollercoaster ride into the heart of what it means to be a Data Scientist. You will face challenges that mirror the real issues professionals encounter daily. From corrupt data and anomalies to irregularities - you'll see it all! 🚂✨

What You'll Learn:

Data Cleaning & Preparation: Tame your unruly datasets and make them analysis-ready.
Basic Data Visualization: Turn numbers into compelling visual stories with Tableau.
Modeling Your Data: Uncover the patterns and predictive power within your data.
Curve-Fitting: Perfect your model to fit your data like a glove.
Presenting Your Findings: Communicate your insights in a way that will leave your audience in awe.

Why You Should Choose This Course:

Practical Exercises: You'll have so many hands-on exercises, real-world scenarios will feel like child's play by the time you graduate. 👨‍💼🎓
Thought-Provoking Homeworks: These challenges are designed to push your limits and make you a resilient problem-solver.
Tools Mastery: Gain a solid understanding of SQL, SSIS, Tableau, and Gretl - the tools that every Data Scientist needs.

Course Navigation: This course is structured to offer flexibility. You can tailor your learning journey using pre-planned pathways that focus on the skills you want to develop. Or embark on the full adventure and prepare yourself for an exciting career in Data Science. The choice is yours! 🗺️🚀

Who is Teaching This? Your guide through this data odyssey is Kirill Eremenko - a seasoned Data Scientist and course instructor with a passion for making complex concepts accessible and fun. His teaching style is engaging, his experience is vast, and his dedication to your learning journey is unwavering. 🧑‍🏫✨

Join the Class Today! Embark on an educational adventure that will transform the way you think about data. Data Science A-Z awaits - sign up now and let's make those data dreams a reality! 🎉

What's Inside the Course?

Real-World Data Challenges: Test your skills with exercises that mimic real-world scenarios.
Expert-Led Instruction: Learn from Kirill Eremenko, a professional with years of industry experience.
Interactive Learning: Engage with the material through interactive coding and data analysis tasks.
Community Support: Join a community of like-minded learners who are all on their journey to becoming Data Science experts.

Bonus:

ChatGPT Prize: Stand a chance to win exclusive prizes when you complete the course, including ChatGPT interactions to further your learning experience! 🏆

Enroll now and let Kirill Eremenko guide you through the fascinating world of Data Science. Your data adventure starts here! 🌟✨

Screenshots

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Data Science A-Z: Hands-On Exercises & ChatGPT Prize [2025] - Screenshot_04

Our review

📚 Overall Course Rating: 4.57/5

Review Summary

The course has been highly praised for its comprehensive approach to Data Science, with a particular emphasis on practical skills and real-world applications. It is well-received among learners who are passionate about data science and are looking to enhance their abilities. The course structure is appreciated for its intuitive and easy-to-follow format, with complex theories being gradually introduced and explained.

Pros:

Engaging Content: Learners find the content engaging and the pacing of the course to be just right, allowing for absorption of new concepts.
Hands-On Learning: Practical examples and hands-on experience are highlighted as key strengths of this course, providing learners with valuable skills that can be applied directly in their work.
Clear Explanations: Kirill Eremenko's teaching style is commended for its clarity and ability to make complex topics understandable.
Well-Structured Modules: The course is praised for its clear progression from basic to advanced concepts, making it suitable for beginners as well as those with prior knowledge.
Diverse Learning Materials: Learners appreciate the variety of materials provided, including SQL, Tableau, and machine learning insights.
Supportive Community: The course community, along with teacher assistants, is recognized for providing helpful support throughout the learning journey.

Cons:

Outdated Applications: Some learners encountered challenges due to outdated applications used in the course, which affected their experience.
Language and Subtitles: Non-native English speakers find the course difficult due to language barriers and suggest that subtitles in English or their native language would be beneficial.
Caption Accuracy: A few learners pointed out inaccuracies in the captions, which could lead to confusion.
More Homework Desired: Some users feel that additional homework and assessments would further solidify their understanding of the course material.
Lack of Integration: There is a mention of a disconnect between Tableau and machine learning streams within the course, with some disappointment expressed regarding the lack of integration between these tools and model results.
User Experience Improvements: A small number of users found the course initially confusing and felt that there could be more guidance and background information in certain sections.

Additional Feedback:

Accessibility: Learners request written transcripts for better understanding, especially for non-native English users.
Cost and Value: Some learners express their satisfaction with the value they are getting from the course and anticipate deeper understanding as they progress.
Expectations Management: It's important to set realistic expectations, as some learners mentioned feeling disappointed not by the course content but by external tools like Tableau that did not integrate as expected.

Conclusion

The course is overwhelmingly positive, with a strong focus on practical application and learner satisfaction. The majority of users find the course beneficial, well-structured, and engaging. However, there are areas for improvement, including language accessibility, potential integration between tools like Tableau and machine learning models, and a desire for additional hands-on assignments to reinforce learning. Overall, this course is a valuable resource for individuals looking to dive into Data Science with a combination of theoretical knowledge and practical skills.