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10 Best Online Python Courses For Data Science On Udemy

Data science ranks first on the ‘top 50 best jobs’ list in America, according to Glassdoor.

The ranking puts the job satisfaction at 4.3 out of five stars.

The opportunities and job openings grew by 56 percent, year on year, according to LinkedIn data.

Python, on the other hand, is the most wanted skill for data scientists this year. The statistics show that 72 percent of all data scientists use the language.

It is also beginner-friendly and powerful. In fact, it features as the third most popular language in the world, according to Tiobe Index.

In essence, mastering the art of data science is not only profitable, but also quite rewarding.

Maybe you’ve been interested but haven’t found the right material to use.

Well, in this article, we’ll show you ten quality Udemy tutorials to take you from beginner to advanced data scientist fast.

1. Python for Data Science and Machine Learning Bootcamp

This course sits pretty amongst the elite courses in data science and machine learning.

It also takes the Bootcamp approach, but assumes a bit of programming experience on the part of the student.

It currently has over 53,000 reviews and a cumulative 4.5-star rating from students.

The instructor, Jose Portilla, has a ranking of 4.5 stars and has nearly 250,000 students enrolled presently in the course.

The course focuses strongly on using Python and its libraries.

It starts with a crash course on Python, explaining core concepts like functions and conditionals.

You’ll also learn about Python data structures and core libraries.

Once you finish, you’ll be working with the Jupyter tool and learn how to work with Matplotlib and NumPy.

You’ll focus quite some time on data extraction and analysis from Excel using Pandas.

Next, you’ll focus on data visualization using some Python data visualization libraries.

The course introduces the concept of machine learning much later.

You’ll learn about several algorithms and techniques such as linear regression, decision trees, and big data analysis in Python.

Finally, you’ll get introduced to machine learning and natural language processing.

Where This Course Shines

  • The course does an excellent job of simplifying complicated concepts in data science into easily digestible chunks.
  • Another excellent feature is the breadth of the course. Very few courses cover as many topics and areas as this course does.
  • You’ll learn plenty of tricks along the way and get familiar with lots of Python data science libraries.

Where There’s a Need for Improvement

  • The course doesn’t explain the core mathematical concepts that form the basis of data science, i.e. statistics and probability.
  • It focuses too strongly on using Python libraries, leaving the student heavily dependent on them.

2. Python A-Z™: Python For Data Science With Real Exercises!

This course focuses on building a strong foundation in Python, while preparing the student for data science.

It assumes no knowledge or programming experience on the part of the student.

It currently has cumulative reviews of 4.5-star from over 9,000 students.

The instructors also have a 4.5-star rating. Over 50,000 students have enrolled in this course.

The course starts on the fundamentals of programming; teaching the student the concept of variables and data types along with their implications.

It progresses further to introduce the concept of conditionals such as the while, for, if, and do-while statements.

Next, it begins on data structures in Python.

Students get introduced to lists, dictionaries, tuples, and finally, data manipulation using these data structures.

The course then delves into Python libraries for data science with a strong focus on NumPy.

After these sections, you begin the real inquiry into external data manipulation and data visualization using Python libraries such as Seaborn.

Where This Course Shines

  • This course invests a commendable length of time in simplifying the concept of programming in Python. It only introduces new libraries when the student is familiar with the language.
  • Its data visualization segment is quite in-depth and well-paced to help even beginners catch up with the difficult concept.

Where There’s a Need For Improvement

  • Compared to other courses, the number of libraries introduced in this course is quite small. It compensates, however, with the in-depth Python coding experience.
  • For students who already have a bit of programming experience, this course may feel too slow.

3. Machine Learning, Data Science and Deep Learning with Python

This course bridges the world of data science and machine learning for a more rounded experience.

It is a beginner-friendly course but assumes some prior coding experience on the part of the student.

It currently has the Udemy bestseller tag and a 4.5-star rating with over 15,000 reviews. The instructors are also 4.5-star level teachers on the Udemy platform.

This course focuses on getting you started fast. It begins with a Python tutorial session.

Here, you’ll learn all the basics of Python. Next up is a refresher on mathematics and probability.

You’ll learn data distributions and visualizations using Matplotlib and Seaborn libraries, respectively.

The course delves into the deep end after that. Concepts like predictive models, data mining, machine learning, and recommender systems get introduced.

You don’t have to worry, though, the instructors take each concept slowly and explain them in easy-to-understand language.

It sums up with neural networks and a final project to help you practice all the lessons you have learned.

Where This Course Shines

  • The course covers a lot of mathematical and statistical information.
  • The hands-on approach forces students to practice what they learn after each lesson. This helps to retain interest and build upon previous knowledge with ease.
  • The course introduces the student to several industry-standard tools for machine learning and data science.

Where There’s a Need For Improvement

  • Some sections are quite advanced for pure beginners who may have to consult other more straightforward materials to grasp them.
  • The course also doesn’t teach advanced topics in the Python programming language before delving into the data science aspects

4. Complete Data Science Training with Python for Data Analysis

This course aims to strike a balance between the complex nature of data science and the beginner friendliness of Python. You don’t need to know programming to begin the course.

The course currently has a rating of 4.3 stars with a bestseller tag. Over 5,000 students are taking the course.

The course starts with an introduction to Python and the concept of data science.

After this, it delves straight into the Python libraries needed for data science and analysis. Some of these libraries and tools include NumPy and Python Pandas.

You can expect to learn complex data science topics such as data visualization, data wrangling, statistical analysis, and supervised learning.

It progresses further to explain artificial neural networks and deep learning processes.

Where This Course Shines

  • The course is completely beginner-friendly, and the instructor follows an empathetic path to teaching the complex sections.
  • Introduction to mathematical concepts makes the course easier to follow. This is true, especially for the complex modules in the course.
  • The course introduces the student to plenty of techniques for data manipulation and analysis. Despite this, it still manages to stay simple enough for the beginner.

Where There’s a Need For Improvement

  • The introductory course on Python is not in-depth enough. Many times you need to research to understand what the instructor is trying to achieve with the language.

5. Data Science & Machine Learning with Python

This course is one of the most in-depth courses available in the field of data science on the Udemy platform.

The course, however, assumes a bit of knowledge about programming and some basic mathematics.

It currently has a rating of 4.5 stars, with over 6,000 students enrolled. The instructor is a 4.3-star rated Udemy course teacher and has over 23,000 students in all his courses.

This data science course starts with a respectable Python crash course.

Here, you can expect to pick up the basics of Python syntax and programming techniques. The course also explains variables, conditionals, and other programming principles for beginners.

Soon after, the course moves into Python libraries for data science like NumPy, Matplotlib, and other data visualization tools.

The instructor introduces these concepts with easy-to-understand real-life data scenarios.

Furthermore, the course walks you through several data science concepts like big data, k-nearest neighbor, and web scraping techniques. It rounds off with data science in the cloud.

Where This Course Shines

  • The direct approach appeals to students who are interested in getting their hands dirty with some data processing fast.
  • The course covers most of the essential tools and libraries used in the data science industry today.
  • The real-life scenario approach makes the course more relevant. Students can quickly see how to apply their skills in the real world and the relevance of data science in the broader scope.

Where There’s a Need for Improvement

  • The instructor relies too often on libraries and in some cases, assumes the student is already familiar with certain concepts.
  • The fast pace may discourage complete beginners.

Other Useful Resources:

6. Complete Data Analysis Course with Pandas & NumPy: Python

This data science course focuses on the use of two popular libraries to conduct in-depth analysis and manipulation of data.

The course assumes a basic knowledge of programming in any language.

It is a 4.5-star bestseller course with over 7,000 enrolled students. The instructor is rated 4.3-stars with 23,000 students under his tutelage.

The course begins with an impressive crash course on Python, explaining the core concepts of programming and program flow.

It progresses further to introduce data manipulation using Python’s native data structures, such as lists, tuples, and dictionary.

Next, it delves into NumPy and how to use the built-in functions for complex data manipulation purposes. It continues into Pandas and introduces the core features of the library.

You’ll also learn how to import, extract, and manipulate data using Pandas.

You’ll also learn how to create stunning visualizations and improve data quality using data cleaning techniques.

Where This Course Shines

  • The introductory section covers a lot of the crucial concepts in Python programming. This is sufficient to get the student up to speed with the rest of the course.
  • The course stays on the mark with the two libraries without straying into other tools helping students master them in detail.

Where There’s a Need For Improvement

  • Students who are already familiar with this tool may not get very much out of this course.
  • There is a keen focus on only data manipulation without any section dedicated to newer concepts like machine learning.

7. Data Science and Machine Learning using Python – A Bootcamp

This course aims to be a one-stop-shop for all the foundational lessons in data science using Python.

Since it follows the Bootcamp approach, it doesn’t require any experience from the student.

It is rated as a bestseller course with a 4.5-star rating. The instructor is also a 4.5-star level instructor with over 6,000 students enrolled in his courses.

This Bootcamp begins with Python essentials, where you learn the basics of the language.

It progresses further to teach Python syntax, control flow, and data structure for data manipulation with the Python core libraries.

Once the students are up a respectable level in Python programming, the course introduces the libraries. Some of them include NumPy, Seaborn, Matplotlib, and Pandas.

You’ll learn how to visualize, extract, clean, and manipulate data using these tools.

The course moves on to introduce Scikit – a machine learning and data science toolkit. You’ll learn some concepts like k-nearest neighbor, linear regression models, logistic regression models, and decision trees, among others.

It rounds up with an introduction to natural language processing techniques.

Where This Course Shines

  • The teaching model is very beginner-friendly; the instructor takes his time to explain difficult concepts in simple language.
  • The libraries and tools introduced are industry-standard hence students taking the course can expect to handle small scale real-life projects easily

Where There’s a Need For Improvement

  • The course only scratches the surface of these tools and doesn’t go in-depth in explaining how they work. This is to keep the students from feeling frustration and quitting midway.

8. Tensorflow Bootcamp For Data Science in Python

The TensorFlow Bootcamp course aims to simplify the concepts around TensorFlow for a beginner in data science and machine learning.

The course is a beginner to mid-level data science course.

It assumes a bit of data science and programming knowledge on the part of the student.

The course is rated 4.3 stars and tagged a bestseller with over 1,000 students enrolled. The instructor is also a 4.3-star level Udemy tutor with nearly 50,00o students enrolled for her courses.

The course begins with an introduction to TensorFlow and the essential concepts about its application in data science. It moves on to introduce NumPy and other associated Python libraries.

Next, the course shows you how to use TensorFlow and these libraries to create statistical models for data analysis.

You’ll begin using the tool for machine learning and develop models for supervised and unsupervised learning.

It ends with an introduction to neural networks and deep learning, including application to image analysis.

Where This Course Shines

  • This course does spectacularly well at introducing the TensorFlow tool and its capabilities. New students can grasp the foundational information the course provides and build upon them.
  • It introduces enough new concepts to keep the interest of the student without becoming overwhelming

Where There’s a Need For Improvement

  • While it focuses on TensorFlow, it introduces other Python libraries too. These libraries don’t get covered in sufficient depth, which can be confusing for new students.

9. The Complete Pandas Bootcamp: Master your Data in Python.

The Pandas Bootcamp aims to simplify everything about the Pandas library for students who hope to use it in their project.

Even though it focuses on a relatively advanced library, the course is exceptionally beginner-friendly.

Some knowledge of Excel is helpful, although not required.

The course is one of the highest-rated courses on Udemy at 4.6 stars.

The instructor is also a 4.7-star rated tutor on the Udemy platform, with over 14,000 students enrolled in his courses.

This Bootcamp starts with a tour of the Pandas library, including some of its features and functions.

It progresses into the manipulation of data and analysis using the tool. Next is an in-depth course on data visualization using Matplotlib.

You’ll learn other useful techniques, such as importing data from Excel for analysis in the latter part of the course. Also, there is a section for learning Python programming too.

Where This Course Shines

  • The course provides an excellent starting point for data scientists looking to leverage the power of Pandas.
  • It is very beginner-friendly.

Where There’s a Need For Improvement

  • The Python crash course is at the bottom of the course list.
  • This positioning could throw off many beginners who first see the Pandas tutorial at the beginning instead of the Python crash course.

10. The Data Science Course: Complete Data Science Bootcamp

This is one of the most in-depth courses on data science available on Udemy.

The course takes a Bootcamp approach towards data science, so you don’t need to know anything at all about the course.

The instructor for the program is rated 4.5-stars. They have over 33,000 reviews and an average of 4.5 stars for the course.

This course takes a gentle start by introducing the concept of data science and its associated keywords.

Next, you dive into the world of mathematics and statistics to clarify the techniques used in the course.

Once you get familiar with these mathematical concepts, the course veers towards Python.

Concepts in programming get introduced in Python, such as variables, functions, conditional statements, sequences, and iterations.

Soon after, you’ll get introduced to the powerful world of Python libraries.

Using these libraries, you’ll begin to apply statistical models to conduct statistical inquiries into massive complex datasets.

This course finally introduces new concepts such as machine learning, neural networks, and deep learning.

Where This Course Shines

  • The pace is gentle at the beginning and builds up gradually as the student gets comfortable with the course.
  • Mathematics and statistics form the bedrock of data science, hence are critical to the success of the student. The course does a brilliant job of covering these tough topics in an engaging style.
  • The Python programming section is quite robust enough for the program.

Where There’s a Need For Improvement

  • Python classes and object-oriented programming don’t get covered in the course.
  • Although this style of programming isn’t popular in data science, they are critical in building user-friendly data science solutions.


Data science, as a field, will keep growing in relevance as humanity keeps improving its tools.

The courses above are some of the best in the field of data science that leverage Python. The language has grown to become the most critical tool for data scientists both in academia and industry.

Make sure to look through the courses and select the one that best aligns with your interests.