“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 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 |
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| 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 in-depth 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 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
| Parameters | Python A-Z™: Python For Data Science With Real Exercises! | Data Science A-Z™: Real-Life Data Science Exercises Included | R Programming A-Z™: 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 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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