Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.

Discover how to use Python for data science, learning about the ways to store and manipulate data in the environment to begin conducting your own analyses.

Introduction to Python

What is data science, why is it so popular, and why did the Harvard Business Review hail it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!

An introduction to data science with no coding involved.

Discover what data science is, why it's so popular, and learn how to do it with hand-on exercises. There is no code required in this introductory course. 

Data Science for Everyone

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

In this intermediate Python course, learn to visualize real data with Matplotlib's functions and get to know new data structures such as the pandas DataFrame.

Intermediate Python

Gain a 360° overview of exploring and using Power BI to build impactful reports. In this course, you’ll go from zero to hero as you discover how to use this popular business intelligence platform through hands-on exercises. Before diving into creating visualizations using Power BI's drag-and-drop functionality, you’ll first learn how to confidently load and transform data using Power Query and the importance of data models. You’ll also learn to drill down into reports and make your reports fully interactive. You've got the power!

Gain a 360° overview of how to explore and use Power BI to build impactful reports.

Learn how to use the Power BI platform to load and transform data, create visualzations, run DAX formulas, and create reports through hands-on exercises.

Introduction to Power BI

Tableau is a widely used business intelligence (BI) and analytics software trusted by companies like Amazon, Experian, and Unilever to explore, visualize, and securely share data in the form of Workbooks and Dashboards. With its user-friendly drag-and-drop functionality it can be used by everyone to quickly clean, analyze, and visualize your team’s data.

You’ll learn how to navigate Tableau’s interface and connect and present data using easy-to-understand visualizations. By the end of this training, you’ll have the skills you need to confidently explore Tableau and build impactful data dashboards. Let’s dive in.

Get started with Tableau, a widely used business intelligence (BI) and analytics software to explore, visualize, and securely share data.

Learn to navigate Tableau’s interface, connect and present data, and build easy-to-understand visualizations to build impactful dashboards for business.

Introduction to Tableau

It's time to push forward and develop your Python chops even further. There are tons of fantastic functions in Python and its library ecosystem. However, as a data scientist, you'll constantly need to write your own functions to solve problems that are dictated by your data. You will learn the art of function writing in this first Python Data Science Toolbox course. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You'll gain insight into scoping in Python and be able to write lambda functions and handle errors in your function writing practice. And you'll wrap up each chapter by using your new skills to write functions that analyze Twitter DataFrames.

Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.

Python Data Science Toolbox (Part 1)

There’s a lot more to Microsoft Excel than SUM and COUNT. Companies and institutions around the world use Excel’s powerful functions to efficiently transform mountains of raw data into clear insights, and now you can too.

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In this course, you’ll develop employable analyst skills as you learn how to use time-saving keyboard shortcuts, convert and clean data types including text, times and dates, and build impressive logic functions and conditional aggregations. Through hands-on practice, you’ll learn over 35 new Excel functions, including CONCATENATE, VLOOKUP, and AVERAGEIF(S), and work with real-world Kickstarter data as you use your new-found Excel skills to analyze what makes a successful project.

Discover time-saving shortcuts in Excel to help you convert and clean your data. You will learn 35+ new Excel functions including CONCAT, VLOOKUP, and more.

Data Analysis in Excel

In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

Python Data Science Toolbox (Part 2)

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

You'll learn the many ways to read data into Python: from flat files such as CSVs to Excel spreadsheets and relational databases in SQLite & PostgreSQL.

Introduction to Importing Data in Python

What is data science and how can you use it to strengthen your organization? This course will teach you about the skills you need on your data team, and how you can structure that team to meet your organization's needs. Data is everywhere! This course will provide you with an understanding of data sources your company can use and how to store that data. You'll also discover ways to analyze and visualize your data through dashboards and A/B tests. To wrap up the course, we'll discuss exciting topics in machine learning, including clustering, time series prediction, natural language processing (NLP), deep learning, and explainable AI! Along the way, you'll learn about a variety of real-world applications of data science and gain a better understanding of these concepts through practical exercises.

Learn about data science and how can you use it to strengthen your organization. 

Data Science for Business

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In the prequel to this course, you learned many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL. In this course, you'll extend this knowledge base by learning to import data from the web and by pulling data from Application Programming Interfaces— APIs—such as the Twitter streaming API, which allows us to stream real-time tweets.

Improve your Python data importing skills and learn to work with web and API data.

Learn how to import data into Python from sources like the web and by pulling data from APIs, such as the Twitter streaming API to stream real-time tweets.

Intermediate Importing Data in Python

pandas is the world's most popular Python library, used for everything from data manipulation to data analysis. In this course, you'll learn how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis. Using pandas you’ll explore all the core data science concepts. Using real-world data, including Walmart sales figures and global temperature time series, you’ll learn how to import, clean, calculate statistics, and create visualizations—using pandas to add to the power of Python!

Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.

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