• Natalia Angarita

Best Resources to Learn Python for Data Science

A lot more Python for Data Science courses have been released since our first blog was published three years ago, so we decided to revise all these newly available resources and update the list of our top recommendations to include more courses and material.


In addition to the top general course picks, a separate section for more specific data science interests, like Deep Learning, and other relevant topics has been included. These extra picks are good for supplementing before, after, and during the main courses.


Some courses we have tried, others were selected based on their customer reviews, popularity, and breadth of material, reviews from aggregators and from forums. Remember we focused on finding online material that is widely accessible and most of the time free.


We hope that you feel confident that the courses below are truly worth your time and effort. We present you with options of various difficulty levels, lengths and specialisation, so you can find one that meets your particular needs.



Learn Python for Data Science


Why should you learn Python if you want to be a successful data scientist?


In short, understanding Python is one of the valuable skills needed for a data science career. Though it hasn’t always been the case, python is now the programming language choice by data analysts, data scientists and software developers alike. During the past years, Python has overtaken other popular programming languages such as R as the tool of choice for analytics professionals and as a platform for data science competitions (such as Kaggle).


Big companies such as Google, Netflix, Facebook, Spotify, YouTube, among many others use Python as part of their ecosystem. The beauty of Python sits in that it is a general-purpose and versatile programming language, which can be used in a variety of applications, including software development, scientific research and analysis, machine learning and website construction. Python is also flexible, allows for rapid development, is scalable and has good performance.


Today, the massive open-source community that has grown around Python drives it forward: more and more packages are developed for Python to ease the implementation of machine learning algorithms as well as several aspects of the data processing pipeline. This has helped to expand its adoption in a variety of industries and as a teaching tool in universities.


If you are to succeed as a data scientist, you will be much better off if you have good Python programming skills.



What exactly do I need to learn?


This first step is to know how to setup your Python environment and know the fundamentals on how to execute Python code, and how to make operations with the basic data structures such as arrays, lists, dictionaries, tuples and dataframes. Perhaps the most convenient way to go about this is to download the free Anaconda package distribution, as it contains the core Python language, as well as all of the essential libraries. One of the important tools you should start using early in your journey is Jupyter Notebooks, an interactive programming environment that allows for python coding, data exploration, and debugging in a web browser.


Next, you need to become familiar with the installation and use of additional Python libraries. Python is a general purpose language. However, what makes Python extremely powerful for working with data, are all the libraries for data manipulation and cleaning, data visualisation, and machine learning, among others, that have sprung up in the latest years. These libraries allow users to easily and quickly implement multiple machine learning, statistical and other data science techniques.


Below are the major Python libraries that are used for working with data and that you should try to familiarise with:





Finally, we also recommend that you join a community (for example a local Meetup group or an online community) or follow / join a Kaggle competition. You’ll put yourself around like-minded people, learn how python is used to solve a variety of problems in different industries and why not, increase your opportunities for employment.



How can you learn Python for use in Data Science?


Python is highly beginner-friendly as it’s expressive, concise, and readable. This makes it much easier to start coding quickly, and the community supporting the language will provide enough resources to solve problems whenever they come up.


Additionally, there are plenty of resources online that you can leverage to improve your Python programming skills. Which resource to choose depends on how comfortable you are with programming in general, and whether you prefer to enrol in a course, or self-learn by reading a book.


How comfortable are you with programming? Have you used Python or any other programming language before? If you haven't, don't worry, there are plenty of online material that will teach you Python from the very basics. And if you have, there are also resources that will help you take your skills to the next level. Without further say, let’s explore the resources…


Disclaimer: Opinions stated here are our ownand we do not become financial compensation from any of the links included in this article. The article does not contain affiliate links.


Contents


Short and Free Online Courses

  1. Introduction to Python (DataCamp)

  2. Python Programming for Absolute Beginners (Udemy)

  3. Introduction to Python: Absolute Beginner (edX)

  4. Python for Data Science: Fundamentals & Intermediate (Dataquest)

Comprehensive Online Courses

  1. Programming for Everybody (Getting Started with Python) (Coursera)

  2. Complete Python Bootcamp (Udemy)

  3. Learning Python for Data Analysis and Visualisation (Udemy)

  4. CS109 Data Science (Harvard)

  5. Applied Data Science with Python Specialization (Coursera)

Python courses for specific needs or data science interests

  1. Introduction to Computer Science and Programming Using Python (edX)

  2. Deep Learning Specialization (Coursera)

  3. Python Django Dev To Deployment (Udemy)

Books for Python programming for Data Science

  1. Learning IPython for Interactive Computing and Data Visualization, Cyrille Rossant, Packt

  2. Python Crash Course, Eric Matthes

Wrapping Up




Short and Free Online Courses


There are several short and free python programming courses in the web that will teach you the very basics of python programming. These short courses take 2-4 hrs to complete, plus some additional time to do the exercises. A good thing about these courses is that they will give you a quick sense of whether this is something you enjoy. Keep in mind though that these are very basic courses, with minimal applications to data

science, and you will certainly need a follow up course to master the language.



1. Introduction to Python (DataCamp)


Introduction to Python on DataCamp is an introductory course to Python, ideal for complete beginners, with no programming experience. It teaches how to handle basic data structures, with an emphasis on data analysis and data science. The course takes 4 hours and includes videos and exercises. This basic course is for free, however more advanced courses in Datacamp require payment.



2. Python Programming for Absolute Beginners (Udemy)


Python Programming for Absolute Beginners on Udemy, is a great practical python course for beginners. It teaches python from scratch starting on how to setup a Python IDE moving to fundamental concepts on handling data structures. The course includes Practice Problems, with solutions that can also be downloaded. There are 3.5 hours of content for free with over 100,000 students enrolled and a 4.4/5 star review by almost a thousand students.



3. Introduction to Python: Absolute Beginner (edX)


As it names indicates, Introduction to Python: Absolute Beginner is an introductory course to Python fundamentals (provided free by Microsoft). It teaches the basics of Python through Jupyter Notebooks hosted on Azure. This course provides a good overview of Python and its capabilities, and gives more in depth knowledge on how to build reusable functions. This course claims to be a bit more time demanding, with a recommended completion time between 12-20 hrs over the course of 4 weeks.


After you complete this course, you can take the next level course from Microsoft called Introduction to Python: Fundamentals either for free or you can choose to pay a fee if you wish a certification.



4. Python for Data Science: Fundamentals & Intermediate (Dataquest)


Python for Data Science: Fundamentals & Intermediate are a couple of introductory courses that give a good overview of Python as a programming language and how to use Python for data science. The courses are part of a more comprehensive collection, setup by Dataquest, under a particular approach to online learning based on the use of projects and teaching through interactive textbooks. Both the Fundaments & Intermediate courses are free and aside from the Python fundamentals, you can learn how to use a Jupyter Notebook, cleaning techniques for text data, the basics of object-oriented programming and how to work with date and time data.

More advanced topics such as machine learning, can be accessed on Dataquest by paying a monthly membership.



Comprehensive Online Courses


There are very good, and potentially more useful, comprehensive online courses to learn Python programming and some aspects of Data Science. These courses contain an extended curriculum and run across several weeks, or include several hrs of video tutorials. These comprehensive online courses cover many aspects of Data Science, such as data gathering, data cleaning, data analysis and visualisation and building of machine learning models, all of this, using python as programming language.



✔️ Best Choice for complete beginners


1. Programming for Everybody (Getting Started with Python) (Coursera)


The course Programming for Everybody (Getting Started with Python) on Coursera makes a good starting point for complete beginners. This is the first of a 5 course specialisation taught by Charles Severance, Associate Professor at the University of Michigan (check the Python for everybody Specialization for more details).

Professor Severance has made a fun and easy-to-follow course full of insight into the ideas behind programming. This course will teach you basic things like what a function is, what a loop is and why we use them. And you will also learn about Python data structures, and basic Python operations with these structures. There is a book that accompanies the lessons, which is freely available. You can audit Programming for Everybody (Getting Started with Python) on Coursera for free, or choose to pay a fee if you wish a certification.



✔️ Top recommendation


2. Complete Python Bootcamp (Udemy)


The Complete Python Bootcamp, by Jose Portilla, is well suited for both complete beginners and students with some programming experience either in Python or in other programming languages. It covers the very basics of Python programming, as well as more complex topics like object oriented programming, decorators and GUIs. The Complete Python Bootcamp will leave you in an excellent place to take your Python skills into any direction you want, be it numerical computing, web development or any other area. The course has clear explanations on Python syntax and coding style, more than 500 thousands students enrolled and 4.5/5 stars rating by more than 150 thousand students. This course is certainly a resource to check out!


This course is not available for free, however Udemy releases discounted vouchers regularly.



3. Learning Python for Data Analysis and Visualisation (Udemy)


Learning Python for Data Analysis and Visualisation, also by Jose Portilla, covers all the standard and essential Python libraries for numerical computation that every data scientist should know, including NumPy, pandas, Scikit-learn Matplotlib and seaborn. For this course, the instructor has put together lots of examples and exercises on a variety of topics, that will help you understand how to use Python for data analysis and visualisation. Although these and other courses on Udemy are not for free, Udemy constantly releases discount vouchers that allow you to get life-time access to the content for a very small fee.



4. CS109 Data Science (Harvard)


If what you are looking for is a well-rounded course that touches a wide variety of data science topics, without going deep into the details of the algorithms but providing a lot of practical knowledge to get you started, the CS109 Data Science from Harvard is for you.


CS109 Data Science offers a great mix of theory and application on data science topics such as data collection and preparation, Machine Learning (Regression, SVM, Bayesian, Clustering), network analysis and text analytics. A great feature of the course is the way it is organised: you can find a detailed syllabus with all the lecture slides and videos, as well as the practical exercises (videos and notebooks) on their website. This option lets you easily decide what lectures to watch or download. There is also a YouTube channel that has shorter videos dealing with the “nitty gritty” stuff on the course.


All the video lectures of this course can be found here and are all free.



5. Applied Data Science with Python Specialization (Coursera)


The Applied Data Science with Python Specialization is another excellent option from Coursera and the University of Michigan if you are looking for a course focused on the applied side of data science using Python as a programming language. The specialization comprises 5 courses, where you’ll get a strong introduction to commonly used data science Python libraries, like Matplotlib and pandas, going to the more specialised ones such as nltk, Scikit-learn, and networkx, and learn how to use them on real data.


The course is intended for people who have a basic programming background (not necessarily in Python) who are looking to apply machine learning, text analytics and social network analysis through python.

As with all Coursera courses you can audit the courses for free, or choose to pay a fee if you wish to get a certification.



Python courses for specific needs or data science interests


1. Introduction to Computer Science and Programming Using Python (edX)


Introduction to Computer Science and Programming Using Python is an excellent option for those who are new to programming and its concepts, or for those who find difficult to translate problems into computer programs. The course is part of a two-course sequence that will help people to think “computationally”. Introduction to Computer Science and Programming Using Python is an introductory course to Python fundamentals and basic programming syntax that will help you get familiar with the use of data structures, and it will also teach you to how to test and debug code. The course can be taken for free or you can choose to pay a fee to get a certification.



2. Deep Learning Specialisation (Coursera)


From the maker of the famouse Stanford Machine Learning course, Andrew Ng, the Deep Learning Specialization is one of the highest rated data science courses on the internet. The specialisation is for those interested in Deep Learning, in particular understanding and working with neural networks in Python. The beginning of the series of courses is catered for beginners but for those who have knowledge in this field there is plenty of advanced topics and exercises on the latter part of the specialisation.



3. Python Django Dev To Deployment (Udemy)


If you are looking to build websites and web apps and also want to learn Python, Python Django Dev To Deployment is the course for you. You’ll learn the Python fundaments whilst learning how to build mobile-friendly interactive websites with HTML and CSS using Python’s Django framework.



Books for Python programming for Data Science


1. Learning IPython for Interactive Computing and Data Visualization, Cyrille Rossant, Packt


One of the most helpful books to learn how to use Python for data science, are the Learning IPython for Interactive Computing and Data Visualization, in its Cookbook or full version form, by Cyrille Rossant from Packt. Both versions are clear and straight to the point, and include several coding examples to help you understand how to use Python for data analysis. An amazing alternative for those who prefer learning from books instead of courses.



2. Python Crash Course, Eric Matthes


The Python Crash Course by Eric Matthes gives a hands-on, project based introduction to programming in Python. The first half of the book is dedicated to cover the basic elements and data structures in Python and the more advanced concepts (functions, classes and file handling), as well as, code testing and debugging. The second half focuses on learning based on following three major challenging and entertaining projects.

There are of course many other books that teach different aspects of Python programming, some from O'Reilly, and the well-known book "Learn Python the Hard Way", among others. It all boils down to personal preference and what aspects of Python programming you want to learn more of. The resources highlighted here are however, those that will help you learn from scratch and leave you in a good position to start your career as a data scientist.



Wrapping Up


For a more exhaustive review of free books to learn Python programming for data science visit these blogs:


Thanks for reading and have fun learning!

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