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

There's been a lot of buzz about Big Data over the past few years, and it's finally become mainstream for many  companies. But what is this Big Data? This course covers the fundamentals of Big Data via PySpark. Spark is a "lightning fast cluster computing" framework for Big Data. It provides a general data processing platform engine and lets you run programs up to 100x faster in memory, or 10x faster on disk,  than Hadoop. You’ll use PySpark, a Python package for Spark programming and its powerful, higher-level libraries such as SparkSQL, MLlib (for machine learning), etc. You will explore the works of William Shakespeare, analyze Fifa 2018 data and perform clustering on genomic datasets. At the end of this course, you will have gained an in-depth understanding of PySpark and its application to general Big Data analysis.

Learn the fundamentals of working with big data with PySpark.

Learn to conduct Big Data analysis using the Python package for Spark programming, PySpark, as well as higher level libraries like SparkSQL and MLlib.

Big Data Fundamentals with PySpark

As a Data Scientist, the majority of your time should be spent gleaning actionable insights from data -- not waiting for your code to finish running. Writing efficient Python code can help reduce runtime and save computational resources, ultimately freeing you up to do the things you love as a Data Scientist. In this course, you'll learn how to use Python's built-in data structures, functions, and modules to write cleaner, faster, and more efficient code. We'll explore how to time and profile code in order to find bottlenecks. Then, you'll practice eliminating these bottlenecks, and other bad design patterns, using Python's Standard Library, NumPy, and pandas. After completing this course, you'll have the necessary tools to start writing efficient Python code!

Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.

Writing Efficient Python Code

Every data science project needs unit testing. It comes with huge benefits - saving a lot of development and maintenance time, improving documentation, increasing end-user trust and reducing downtime of productive systems. As a result, unit testing has become a must-have skill in the industry, used by almost every company. This course teaches unit testing in Python using the most popular testing framework pytest. By the end of this course, you will have written a complete test suite for a data science project. In the process, you will learn to write unit tests for data preprocessors, models and visualizations, interpret test results and fix any buggy code. You will also learn advanced concepts like TDD, test organization, fixtures and mocking so that you can test your own data science projects properly.

Learn how to write unit tests for your Data Science projects in Python using pytest.

Unit Testing for Data Science in Python

Working with data is tricky - working with millions or even billions of rows is worse.
Did you receive some data processing code written on a laptop with fairly pristine data?
Chances are you’ve probably been put in charge of moving a basic data process from prototype to production.
You may have worked with real world datasets, with missing fields, bizarre formatting, and orders of magnitude more data. Even if this is all new to you, this course helps you learn what’s needed to prepare data processes using Python with Apache Spark.
You’ll learn terminology, methods, and some best practices to create a performant, maintainable, and understandable data processing platform.

Learn how to clean data with Apache Spark in Python.

Learn how to use PySpark to clean your data in Python with DataFrames and data pipelines. You'll also learn why clean data is so important for analysis

Cleaning Data with PySpark

Before you can analyze data, you first have to acquire it. This course teaches you how to build pipelines to import data kept in common storage formats. You’ll use pandas, a major Python library for analytics, to get data from a variety of sources, from spreadsheets of survey responses, to a database of public service requests, to an API for a popular review site. Along the way, you’ll learn how to fine-tune imports to get only what you need and to address issues like incorrect data types. Finally, you’ll assemble a custom dataset from a mix of sources.

Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.

Streamlined Data Ingestion with pandas

Have you heard people talk about data engineers and wonder what it is they do? Do you know what data engineers do but you're not sure how to become one yourself? This course is the perfect introduction. It touches upon all things you need to know to streamline your data processing. This introductory course will give you enough context to start exploring the world of data engineering. It's perfect for people who work at a company with several data sources and don't have a clear idea of how to use all those data sources in a scalable way. Be the first one to introduce these techniques to your company and become the company star employee.

Learn about the world of data engineering with an overview of all its relevant topics and tools!

Introduction to Data Engineering

In 2019, the average salary for data engineers overtook data scientists. How did this happen? Companies wanting to find the gold within their data realized it wasn’t possible if they hadn’t yet built the mine. Data engineers lay the foundations that make data science possible.
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In this course, you’ll learn about a data engineer’s core responsibilities, how they differ from data scientists, and facilitate the flow of data through an organization. Through hands-on exercises you’ll follow Spotflix, a fictional music streaming company, to understand how their data engineers collect, clean, and catalog their data. By the end of the course, you’ll understand what your company's data engineers do, be ready to have a conversation with a data engineer, and have a solid foundation to start your own data engineer journey.


Discover how data engineers lay the groundwork that makes data science possible. No coding involved!

Data Engineering for Everyone

Want to grow your data engineering skills and more efficiently process big data? Well, it’s time to develop your ETL skills. In this course, you’ll learn the foundations of creating pipelines to efficiently extract, transform, and load data into the systems your company commonly uses. You’ll get hands-on experience by helping a fictional private equity firm process the sales data they need to make informed business decisions when buying real estate. Jump in, learn how to create ETL pipelines, and develop one of the most in-demand engineering skills needed in the market.

Leverage your Python and SQL knowledge to create a pipeline ingesting, transforming and loading data into a database.

ETL in Python

Over 50% of all corporate data is stored in the cloud. As cloud computing becomes more and more ubiquitous, mastering at least one cloud platform is now crucial for data practitioners. In this course, you’ll get to grips with AWS—the market-leading cloud provider. You’ll also learn about its main core services, best practices to design AWS applications, and the benefits of using AWS for businesses.
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This course is a great starting point to prepare you for the AWS Cloud Practitioner Certification.


Learn the fundamentals of cloud computing with AWS.

Learn about AWS cloud computing including benefits, applications, economics, and core services like storage, management, and well-architected framework.

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