“Python AZ™: 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 reallife 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 AZ™: 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 toocomplicated 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 Highlights  Details 

Registration Link  Apply Now! 
Price  INR 449 ( 
Duration  11 Hours 
Rating  4.6/5 
Student Enrollment  149,184 students 
Instructor  Kirill Eremenko https://www.linkedin.com/in/kirilleremenko 
Topics Covered 

Course Level  Intermediate 
Total Student Reviews  25,203 
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  
Slicing  
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 
Matrices  
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  
Matrices  
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  
Histograms  
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)  
THANK YOU Video  
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 indepth course for learning both Python and Data Science
Pros
 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.
Cons
 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 stepbystep tutorial in the field of data science by fusing his realworld 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
Parameters  Python AZ™: Python For Data Science With Real Exercises!  Data Science AZ™: RealLife Data Science Exercises Included  R Programming AZ™: R For Data Science With Real Exercises! 

Offers  INR 449 (  INR 449 (  INR 449 ( 
Duration  11 hours  21 hours  10.5 hours 
Rating  4.6 /5  4.5 /5  4.7 /5 
Student Enrollments  149,179  208,256  244,647 
Instructors  Kirill Eremenko  Kirill Eremenko  Kirill Eremenko 
Register Here  Apply Now!  Apply Now!  Apply Now! 
Python AZ™: 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 floatingpoint 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 wellliked 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 noncoder 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 nonIT 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 realworld 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 nonscience 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 entrylevel 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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