Python for Data Science: A Beginner’s Guide

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Python is a programmer darling for plenty of reasons: the language is easy to read and work with, relatively simple to learn, and popular enough that there’s a great community and plenty of resources available.

And if you needed one more reason to consider starting Python for beginners, it plays an important role in lucrative data careers as well! Learning Python for data science or data analysis will give you a variety of useful skills.

What exactly are those skills? In this special guest post, Quincy Smith from Springboard writes about why Python is used for data science and everything it allows you to do.

Here’s Quincy!

Disclosure: I’m a proud affiliate for some of the resources mentioned in this article. If you buy a product through my links on this page, I may get a small commission for referring you. Thanks!

Table of Contents

Getting Started With Python for Data Science

Python has been around since grunge music hit the mainstream and dominated the airways. Over the years, many programming languages (like Perl) have come and gone, but Python has been growing, evolving, and gaining new strengths.

Getting Started With Python for Data Science

In fact, it’s one of the fastest-growing programming languages in the world. As a high-level programming language, Python is widely used in mobile app development, web development, software development, and in the analysis and computing of numeric and scientific data.

For example, popular websites like Dropbox, Google, Instagram, Spotify, and YouTube were all built with this powerful programming language.

The massive open-source community that has grown around Python drives it forward with a number of tools that help coders work with it efficiently. In recent years, more tools have been developed specifically for data science, making it easier than ever to analyze data with Python.

Is Python good for data science? Absolutely! In the rest of this article, we’ll cover how Python is used in data science, how to learn Python for data science, and more.

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What Is Python?

The foundation for Python was laid in the late 1980s, but the code was only published in 1991. The primary aim here was to automate repetitive tasks, to rapidly prototype applications, and to implement them in other languages.

It’s a relatively simple programming language to learn and utilize because the code is clean and easy to comprehend. So it’s not surprising that most programmers are familiar with it.

The clean code, along with extensive documentation, also makes it easy to create and customize web assets. As alluded to above, Python is also highly versatile and supports multiple systems and platforms. Thus, it can be easily leveraged for a variety of purposes from scientific modeling to advanced gaming.

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Why Should You Learn Python for Data Science?

Since its early days as a utility language, Python has grown to become a major force in artificial intelligence (AI), machine learning (ML), and big data and analytics. However, while other programming languages like R and SQL are also highly efficient to use in the field of data science, Python has become the go-to language for data scientists.

If you learn Python for data science or another career, it can open a lot of doors for you and improve your career opportunities. Even if you don’t work in AI, ML, or data analytics, Python is still vital to web development and the development of graphical user interfaces (GUIs).

A major reason why Python is used for data science is the fact that it has proven time and again to be capable of solving complex problems efficiently. With the help of data-focused libraries (like NumPy and Pandas), anyone familiar with Python’s rules and syntax can quickly deploy it as a robust tool to process, manipulate, and visualize data.

Whenever you get stuck, it’s also relatively easy to solve Python-related problems because of the sheer amount of documentation that’s freely available. For example, the Python Data Science Handbook: Essential Tools for Working With Data is a popular book you can read for free online.

python programming

Python’s appeal has also extended beyond software engineering to those working in non-technical fields. It makes data analysis attainable for those coming from backgrounds like business and marketing.

Most data scientists won’t ever have to deal with things like cryptography or memory leaks, so as long as you can write clean, logical code in Python, you’ll be on your way to conducting some data analytics.

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

It also pays to become a Python developer. According to Glassdoor, Python developers command an average salary of $76,526 a year. Those with significant coding experience can earn as much as $107,000 annually.

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What Are Basic Data Structures?

We can’t talk about how to learn Python for data science without going over some of the underlying data structures that are available. These can be described as a method of organizing and storing data in a way that’s easily accessible and modifiable.

Some of the data structures that are already built in include:

  • Dictionaries
  • Lists
  • Sets
  • Strings
  • Tuples

Lists, strings, and tuples are ordered sequences of objects. Both lists and tuples are like arrays (in C++) and can contain any type of object, but strings can only contain characters. Lists are heterogenous containers for items, but lists are mutable and can be reduced or extended as needed.

coding screen

Tuples, like strings, are immutable, so that’s a significant difference when compared to lists. This means that you can delete or reassign an entire tuple, but you can’t make any changes to a single item or slice.

Tuples are also considerably faster and demand less memory. Sets, on the other hand, are mutable, unordered sequences of unique elements. In fact, a set is a lot like a mathematical set because it doesn’t hold duplicate values.

A dictionary in Python holds key-value pairs, but you’re not allowed to use an unhashable item as a key. The primary difference between a dictionary and a set is the fact that it holds key-value pairs instead of single values.

Dictionaries are enclosed in curly brackets: d = {"a":1, "b":2}

Lists are enclosed in brackets: l = [1, 2, "a"]

Sets are also enclosed in curly brackets: s = {1, 2, 3}

Tuples are enclosed in parentheses: t = (1, 2, "a")

(Source: Thomas Cokelaer)

All of the above have their own sets of advantages and disadvantages, so you have to know where to use them to get the best results.

When you’re dealing with large sets of data, you’ll also have to spend a considerable amount of time “cleaning” unstructured data. This means handling data that’s missing values or has nonsensical outliers or even inconsistent formatting.

So before you can engage in data analytics, you have to break the data down to a form that you can work with. This can be achieved easily by leveraging NumPy and Pandas. To learn more, the Pythonic Data Cleaning With NumPy and Pandas tutorial is an excellent place to start.

For those of you who are interested in data science, blindly installing Python will be the wrong approach, as it can quickly become overwhelming. There are thousands of modules in Python, so it can take days to manually install a PyData stack if you don’t know what tools you’ll need to engage in data analytics.

The best way around this is to go with the Anaconda Python distribution, which will install most of what you’ll need. Everything else can be installed through a GUI. The good news is that the distribution is available for all major platforms.

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What’s Jupyter/iPython Notebook?

Jupyter (formerly known as iPython) Notebook is an interactive programming environment that allows for coding, data exploration, and debugging in the web browser. The Jupyter Notebook, which can be accessed via a web browser, is an incredibly powerful Python shell that’s ubiquitous across PyData.

It will allow you to mix code, graphics (even interactive ones), and text. You can even say that it works like a content management system as you can also write a blog post such as this one with a Jupyter Notebook. Learn more by checking out the Jupyter Notebook for Data Science course on Udemy.

As it comes preinstalled with Anaconda, you can start using it as soon as it’s installed. Using it will be as simple as typing the following:

In 1: print('Hello World')

Out 1: Hello World

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Overview of Python Libraries

There are plenty of active data science and ML libraries that can be leveraged using Python for data science. Below, let’s go over some of the leading Python libraries in the field.



Matplotlib can be described as a Python module that’s useful for data visualization. For example, you can quickly generate line graphs, histograms, pie charts, and much more with Matplotlib. Further, you can also customize every aspect of a figure.

When you use it within Jupyter/IPython Notebook, you can take advantage of interactive features like panning and zooming. Matplotlib supports multiple GUI backends of all operating systems and is enabled to export leading graphics and vectors formats.

💡 Want to learn more? Check out the Matplotlib Intro with Python course on Udemy. 


NumPy, short for “Numerical Python,” is an extension module that offers fast, precompiled functions for numerical routines. As a result, it becomes much easier to work with large multidimensional arrays and matrices.

When you use NumPy, you don’t have to write loops to apply standard mathematical operations on an entire data set. However, it doesn’t provide powerful data analysis capabilities or functionalities.

💡 Want to learn more? Check out the Create Arrays in Python NumPy – Learn Scientific Computing course on Mammoth Interactive.


SciPy is a Python module for linear algebra, integration, optimization, statistics, and other frequently used tasks in data science. It’s highly user-friendly and provides for fast and convenient N-dimensional array manipulation.

SciPy’s main functionality is built upon NumPy, so its arrays heavily depend on NumPy. With the help of its specific submodules, it also provides efficient numerical routines like numerical integration and optimization. All functions in all submodules are also heavily documented.

💡 Want to learn more? Check out the Python SciPy: The Open Source Python Library course on Udemy.


Pandas is a Python package that contains high-level data structures and tools that are perfect for data wrangling and data munging. They are designed to enable fast and seamless data analysis, data manipulation, aggregation, and visualization.

Pandas is also built on NumPy, so it’s quite easy to leverage NumPy-centric applications like data structures with labeled axes. Pandas makes it easy to handle missing data by using Python and prevents common errors resulting from misaligned data derived from a variety of sources.

💡 Want to learn more? Check out the Complete Beginners Data Analysis with Pandas and Python course on Mammoth Interactive.


PyTorch, based on Torch, is an open-source machine learning library that was primarily built for Facebook’s artificial intelligence research group. While it’s a great tool for natural language processing and deep learning, it can also be leveraged effectively for data science.

💡 Want to learn more? Check out the Foundations of PyTorch course on Pluralsight.


Seaborn is highly focused on the visualization of statistical models and essentially treats Matplotlib as a core library (like Pandas with NumPy). Whether you’re trying to create heat maps, statistically meaningful plots or aesthetically pleasing plots, Seaborn does it all by default.

As it understands the Pandas DataFrame, they both work well together. Seaborn isn’t prepacked with Anaconda like Pandas, but it can be easily installed.

💡 Want to learn more? Check out this Seaborn tutorial on Coursera. 


Scikit-Learn is a module focused on machine learning that’s built on top of SciPy. The library provides a common set of machine learning algorithms through its consistent interface and helps users quickly implement popular algorithms on data sets. It also has all the standard tools for common ML tasks like classification, clustering, and regression.

 💡 Want to learn more? Check out the Building Your First scikit-learn Solution course on Pluralsight. 


PySpark enables data scientists to leverage Apache Spark (which comes with an interactive shell for Python and Scala) and Python to interface with Resilient Distributed Datasets. A popular library integrated within PySpark is Py4J, which allows Python to interface dynamically with JVM objects (RDDs).

💡 Want to learn more? Check out the Apache PySpark by Example course on LinkedIn Learning.


If you’re going to use dataflow programming across a range of tasks, TensorFlow is the open-source library to work with. It’s a symbolic math library that’s popular in machine learning applications like neural networks. More often than not, it’s considered an efficient replacement for DistBelief.

💡 Want to learn more? Check out the TensorFlow in Practice Specialization on Coursera. 

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Where Can I Learn Python for Data Science?

Interested in getting started with Python for data science? The courses below will help you learn Python for data science with a variety of specializations.

Please note that pricing listed below may change in the future!

1. Python for Data Science and Machine Learning Bootcamp (Udemy)

This course teaches you how to code in Python, create amazing data visualizations, and implement machine learning algorithms over the course of 100+ video lectures and detailed code notebooks. After completing this bootcamp, you’ll know how to set up basic environments, prove your mastery of Python basics, and understand how to apply data exploration packages in the real world. 

It’s also one of the most popular Python for data science courses on Udemy, with a 4.6 star rating, 83,485 ratings, and 372,593 students. 

🌟 Platform: Udemy

➡️ Course URL: 

💡 What you’ll learn: NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, TensorFlow, and more

👋 Instructed by: Jose Portilla

📈 Level: Intermediate. This course is meant for people with some programming experience.

⏰ How long it takes to complete: 25 hours

💰 Price: $109.99

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

In this Python for data science course, you’ll go from learning the fundamentals of Python to creating advanced graphs and visualizations using libraries like Seaborn. With homework challenges, real-life data science examples (e.g., basketball statistics, world trends, movie statistics), and easy-to-follow tutorials, this course is great for complete beginners.

🌟 Platform: Udemy

➡️ Course URL:

💡 What you’ll learn: Foundations of Python, how to code in Jupyter Notebook, statistical analysis, data mining, visualization, and more.

👋 Instructed by: Kirill Eremenko

📈 Level: Beginner

⏰ How long it takes to complete: 11 hours

💰 Price: $94.99

3. Applied Data Science with Python Specialization (Coursera)

Explore a career as a data scientist throughout this 5-course Coursera specialization that teaches you how to use Python to visualize data, apply basic natural language processing methods to text, manipulate networked data using the NetworkX library, and much more. Topics also touch on machine learning.

This course is meant for students who already have a basic Python or programming background and want to learn more about popular Python data science toolkits like Pandas, Matplotlib and scikit-learn.

🌟 Platform: Coursera

➡️ Course URL: 

5️⃣ Coursera coding courses included:

💡 What you’ll learn: Machine learning, information visualization, data cleansing, text analysis, and social network analysis techniques with Pandas, Matplotlib, scikit-learn, NLTK, and NetworkX.

🎓 University taught at: University of Michigan

📈 Level: Intermediate. Requires basic Python or programming experience. 

⏰ How long it takes to complete: 5 months (suggested 6 hours/week)

💰 Price: $49/per month X 5 months = $245

4. Doing Data Science with Python (Pluralsight)

With the Doing Data Science with Python course, you’ll learn how to work on real-world data science projects from start to finish, including extracting data from different sources all the way to more advanced topics like building and evaluating machine learning models. 

Along the way, you’ll become familiar with various data science concepts and libraries in the Python ecosystem. You’ll also get the chance to work through a case study to help apply what you learn to a real data science project. 

🌟 Platform: Pluralsight

➡️ Course URL:

💡 What you’ll learn: Various stages of a typical data science project cycle, standard libraries in the Python ecosystem (e.g., Pandas, NumPy, Matplotlib, Scikit-Learn, Pickle, Flask), building and evaluating machine learning models, and more. 

👋 Instructed by: Abhishek Kumar

📈 Level: Beginner

⏰ How long it takes to complete: 6h 24m

💰 Price: $29.00/month X 6h 24m = $29

5. Python for Data Science (edX)

Part of the Data Science MicroMasters program at edX, Python for Data Science is an introduction to the Python tools you need to import, explore, analyze, visualize, and glean insights from large datasets. It will also teach you how to generate easily shareable reports.

This course is great for those who already have some programming experience and want to make the jump into data science. It also serves as a solid foundation if you want to move onto even more advanced topics through the MicroMasters program.

🌟 Platform: edX

➡️ Course URL:

💡 What you’ll learn: How to use Pandas, Git, and Matplotlib, to manipulate, analyze, and visualize complex datasets.

🎓 University taught at: University of California, San Diego (UC San Diego)

📈 Level: Advanced. Requires previous experience with any programming language (Java, C, C++, Python, PHP, etc.), as well familiarity with loops, if/else, and variables.

⏰ How long it takes to complete: 10 weeks (suggested 8-10 hours per week)

💰 Price: Free for the audit option or $350 for the verified enrollment track (which includes a certificate)

6. Learn Data Science with Python – Part 1: Python Basics, Anaconda Installation & Jupyter Notebooks (Skillshare)

Tony Staunton, the creator of this Python data science course, calls it the “first step on your data science journey.” Starting from Python foundations, you’ll learn how to analyze and manipulate large amounts of data and get an intro to scientific computing with NumPy.

You’ll also get to build a random number generator with Python, which can help you build more advanced games like rolling dice, and arm you with the tools to make predictions with data science and machine learning later on. 

🌟 Platform: Skillshare

➡️ Course URL: 

💡 What you’ll learn: Python foundations used by data scientists to analyze and manipulate data, scientific computing using NumPy, how to use Jupyter Notebooks, Python functions and packages, and more.

👋 Instructed by: Tony Staunton

📈 Level: Beginner/intermediate

⏰ How long it takes to complete: 9 lessons (1h 11m)

💰 Price: $8.25 per month with a Skillshare subscription (try for free with a 14-day trial of Skillshare Premium)

7. Data Scientist Career Path (Codecademy)

Companies are looking for data-driven decision-makers, and this Codecademy Career Path will teach you the skills you need to become just that. You’ll learn to analyze data, communicate your findings, and even draw predictions using machine learning. Along the way, you’ll build portfolio-worthy projects that will help you get job-ready.

🌟 Platform: Codecademy

➡️ Course URL:

💡 What you’ll learn: SQL to talk to databases and manipulate tables, Python for statistical analysis and creating data visualizations, machine learning and AI, and more.

📈 Level: Beginners welcome

⏰ How long it takes to complete: 35 weeks

💰 Price: Free for 7 days with trial of Codecademy Pro, then $19.99/month

8. Data Scientist with Python Career Track (DataCamp)

This career track aims you equip you with all the Python skills you need to become a data scientist. You don’t need any prior coding experience; this track will teach you everything from the basics to how to build your own machine learning model.

🌟 Platform: DataCamp

➡️ Course URL:

💡 What you’ll learn: How you can use Python to import, clean, manipulate, and visualize data, how to use the most popular Python libraries, plus statistical and machine learning techniques.

📈 Level: Beginners welcome

⏰ How long it takes to complete: 23 courses containing 88 hours of content and 6 projects

💰 Price: From $25/month billed annually (but you can take the first chapter for free)

Other Python for Data Science Courses to Check Out

Plus, I’ve rounded up even more top data science courses and books here!

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Learn Python for Data Science & Beyond

This beginner’s guide just scratched the surface of Python for data science. As the language evolves rapidly with the support of the open-source community, you can expect it to keep growing in importance within the field.


Choosing a language to learn, especially if it’s your first, is an important decision. For those of you thinking about learning Python for beginners and beyond, it can be a more accessible path to programming and data science. It’s relatively easy to learn, scalable, and powerful. It’s even referred to as the Swiss Army knife of programming languages.

With a wealth of online courses, tutorials, and workshops, you can figure out how to learn Python for data science and start working with oceans of data sooner rather than later. From there, the professional possibilities are essentially endless.

About the Author

This post was created by Quincy Smith of Springboard, a company dedicated to helping bridge the world’s skills gap through mentor-led courses like Data Science and Machine Learning. He’s passionate about strong coffee, solo travel, and clean data.