“Python A-Z™: Python For Data Science With Real Exercises!” course will help the students to learn python programming, the core principles of programming, and learn to install packages in Python. This course offers training with real-life analytical challenges. This course has been designed in a manner that enables everyone to become skilled efficiently. Students who are not from a programming or statistical background can also pursue this course.

Students will learn about principles of programming, the creation of variables, creating Histograms KDE plots, and violin plots.  Currently, Udemy is offering Python A-Z™: Python For Data Science With Real Exercises course for up to 87 % off i.e. INR 449 (INR 3,499).  (5.5 USD)

Who all can opt for this course?

  • This course is for you if you want to learn how to program in Python
  • This course is for you if you’re sick of taking too-complicated Python courses.
  • This course is for you if you want to learn Python through practice
  • You should take this course if you enjoy engaging in challenges
  • In this course, you will have homework, so be prepared to complete it

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,49987 % off
Duration11 Hours
Student Enrollment149,184 students
InstructorKirill Eremenko https://www.linkedin.com/in/kirilleremenko
Topics Covered
  • Learn the core principles of programming
  • How to create histograms, KDE plots, violin plots, and style your charts to perfection
  • Learn about integer, float, logical, string, and other types in Python
Course LevelIntermediate
Total Student Reviews25,203

Learning Outcomes

  • Gain proficiency in Python programming
  • Utilize Jupiter Notebooks to learn how to code
  • Master the fundamentals of programming
  • Find out how to make variables
  • Learn about Python’s string, logical, float, integer, and other kinds
  • Learn how to make a Python while() and for() loop
  • Learn Python package installation
  • Know the Rule of Large Numbers

Course Content

S.No.Module (Duration)Topics
1.Welcome To The Course (09 minutes)Installing Python (Windows & MAC)
Get the Datasets here
Extra Resources
2.Core Programming Principles (01 hours 12 minutes)Types of variables
Using Variables
Boolean Variables and Operators
The “While” Loop
The “For” Loop
The “If” statement
Code indentation in Python
Section recap
HOMEWORK: Law of Large Numbers
Core Programming Principles
3.Fundamentals Of Python (01 hours 18 minutes)What is a List?
Let’s create some lists
Using the [] brackets
Tuples in Python
Functions in Python
Packages in Python
Numpy and Arrays in Python
Slicing Arrays
Section Recap
HOMEWORK: Financial Statement Analysis
Fundamentals of Python
4.Matrices (01 hours 58 minutes)Project Brief: Basketball Trends
Building Your First Matrix
Dictionaries in Python
Matrix Operations
Your first visualization
Expanded Visualization
Creating Your First Function
Advanced Function Design
Basketball Insights
Section Recap
HOMEWORK: Basketball free throws
5.Data Frames (01 hours 59 minutes)Importing data into Python
Exploring your dataset
Renaming Columns of a Dataframe
Subsetting data frames in Pandas
Basic operations with a Data Frame
Filtering a Data Frame
Using .at() and .iat() (advanced tutorial)
Introduction to Seaborn
Visualizing With Seaborn: Part 1
Keyword Arguments in Python (advanced tutorial)
Section Recap
HOMEWORK: World Trends
Data Frames
6.Advanced Visualization (02 hours 36 minutes)What is a Category data type?
Working with JointPlots
Stacked histograms in Python
Creating a KDE Plot
Working with Subplots
Violin plots vs Boxplots
Creating a Facet Grid
Coordinates and Diagonals
EXTRA: Building Dashboards in Python
EXTRA: Styling Tips
EXTRA: Finishing Touches
Section Recap
HOMEWORK: Movie Domestic % Gross
Advanced Visualization
7.Homework Solutions (01 hours 48 minutes)Homework Solution Section 2: Law Of Large Numbers
Homework Solution Section 3: Financial Statement Analysis (Part 1)
Homework Solution Section 3: Financial Statement Analysis (Part 2)
Homework Solution Section 4: Basketball Free Throws
Homework Solution Section 5: World Trends (Part 1)
Homework Solution Section 5: World Trends (Part 2)
Homework Solution Section 6: Movie Domestic % Gross (Part 1)
Homework Solution Section 6: Movie Domestic % Gross (Part 2)
8.Special Offer (02 minutes)***YOUR SPECIAL BONUS***

Resources Required

  • No prior expertise or knowledge is required
  • Only interest can propel you to achievement

Featured Review

Erik Whitelaw (5/5): This was an excellent in-depth course for learning both Python and Data Science


  • SHRUTHI SRIDHAR (5/5): “The best course” for anyone looking for learning python for analytics.
  • Prasad Ingare (5/5): One of the best courses to learn python for data science!!!
  • Balogun Soliu (5/5): I think this is the best course for learning data science.
  • Jesus A Rojas Zavarce (5/5): I believe the structure of the course and the language used by the instructor definitely make this an excellent course.


  • Ric Jannon Flores (1/5): The answer is yes, you did, but what’s annoying is the homework.
  • John McGraw (2/5): That said, this course was very frustrating when it came to the Homework and putting what you learned to the test.
  • Kevin Hui (1/5): Poor explanations from instructors during the course, a lack of information regarding where to find course materials, and overall awful quality of the lectures.
  • Dylan Zubata (2/5): Dwyane Wade was spelled wrong in your dataset which confused me for some time.

About the Author

The instructor of this course is Kirill Eremenko who is a Data Scientist with a 4.5 Instructor Rating and 599,095 Reviews on Udemy. He offers 59 Courses and has taught 2,251,523 students so far.

  • Professionally, Kirill Eremenko is a data science consultant with experience in the retail, transportation, retail, and financial sectors
  • At Deloitte Australia, Kirill Eremenko received training from the top analytics mentors, and since he started teaching on Udemy, he has shared his experience with thousands of aspiring data scientists
  • Students will see from his courses how he gives a skilled step-by-step tutorial in the field of data science by fusing his real-world expertise and academic background in physics and mathematics
  • His emphasis on intuitive explanations is one of his teaching strengths, so you can be confident that he will fully comprehend even the most challenging subjects

Comparison Table

ParametersPython A-Z™: Python For Data Science With Real Exercises!Data Science A-Z™: Real-Life Data Science Exercises IncludedR Programming A-Z™: R For Data Science With Real Exercises!
OffersINR 449 (INR 3,499) 87% offINR 449 (INR 3,499) 87% offINR 449 (INR 3,499) 87% off
Duration11 hours21 hours10.5 hours
Rating4.6 /54.5 /54.7 /5
Student Enrollments149,179208,256244,647
InstructorsKirill EremenkoKirill EremenkoKirill Eremenko
Register HereApply Now!Apply Now!Apply Now!

Python A-Z™: Python For Data Science With Real Exercises: FAQs

Ques. What topics of Python are required for data science?

Ans. Numbers are one of the most fundamental ideas in data science. In Python, integers and floating-point numbers are discussed.

Ques. Should I learn R or Python first?

Ans. R might be a good fit for you if you’re passionate about the statistical computation and data visualization aspects of data analysis. Python might be a better choice if, on the other hand, you’re interested in working as a data scientist and utilizing big data, artificial intelligence, and deep learning methods.

Ques. What type of Python is used in data science?

Ans. Scipy is a well-liked Python library for scientific computing and data science. Scipy offers the excellent capability for computer programming and scientific mathematics.

Ques. Can I learn data science without programming?

Ans. Many data scientists did not have any prior coding training or expertise when they began their careers. The fundamental conditions for a non-coder to become a data scientist are as follows: comprehensive knowledge of statistics and probability. being passionate about handling numbers.

Ques. Is R easier than Python?

Ans. Both Python and R are regarded as being quite simple to learn. Python was created initially for the purpose of creating software. Python might be easier to learn than R if you have prior familiarity with Java or C++. R, though, can be a little simpler if you have a background in statistics.

Ques. Can a nonprogrammer learn data science?

Ans. The use of data science and machine learning tools doesn’t require any programming knowledge. This is very helpful for non-IT workers who aren’t familiar with Python, R, etc. programming. They offer a highly interactive GUI that is simple to use and pick up.

Ques. Can I learn data science without knowing Python?

Ans. Before studying Python, you should familiarise yourself with certain fundamental data science principles, but you can start tackling a lot of real-world issues without even touching a line of code! Starting to think about issues in terms of data, features, accuracy measurements, etc. is the most crucial aspect of data science.

Ques. Can a non-science student learn data science?

Ans. You can start a career in data science even if you don’t have programming experience or come from a computer science background. Many data scientists didn’t have any prior coding training or expertise when they began their employment.

Ques. How many months does it take to learn data science?

Ans. A person who has never coded before and/or has no mathematics training often needs to put in 7 to 12 months of intense study to become an entry-level data scientist. It’s crucial to remember that mastering merely the theoretical underpinnings of data science could not turn you into a true data scientist.

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