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

The role of a data scientist is to turn raw data into actionable insights. Much of the world's raw data—from electronic medical records to customer transaction histories—lives in organized collections of tables called relational databases. To be an effective data scientist, you must know how to wrangle and extract data from these databases using a language called SQL . This course teaches syntax in SQL shared by many types of databases, such as PostgreSQL, MySQL, SQL Server, and Oracle. This course teaches you everything you need to know to begin working with databases today!

Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.

Learn the basics of syntax in SQL, how to extract and wrangle data, and use querying tables in relational databases like MySQL, SQL Server, and PostgreSQL.

Introduction to SQL

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

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)

Spreadsheets are an essential tool for any marketing professional, but how does one keep these spreadsheets clean and accurate - especially when multiple parties contribute data? Data validation and regular expressions are powerful tools for marketing analysts, but having clean data is only half the battle. After we learn how to clean the data, we will visualize it by building charts! Throughout the course, we will explore a dataset that includes the kind of information you will encounter in the world of digital marketing. We will spot errors in metrics using data validation, use regular expressions to aggregate campaign metrics, build charts to analyze campaign performance, and use everything we've learned to build a dynamic dashboard!

Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.

Marketing Analytics in Spreadsheets


Churn is when a customer stops doing business or ends a relationship with a company. It’s a common problem across a variety of industries, from telecommunications to cable TV to SaaS, and a company that can predict churn can take proactive action to retain valuable customers and get ahead of the competition.  This course will provide you a roadmap to create your own customer churn models. You’ll learn how to explore and visualize your data, prepare it for modeling, make predictions using machine learning, and communicate important, actionable insights to stakeholders. By the end of the course, you’ll become comfortable using the pandas library for data analysis and the scikit-learn library for machine learning.

Learn how to use Python to analyze customer churn and build a model to predict it.

Marketing Analytics: Predicting Customer Churn in Python

In this course, you'll learn the basics of using SQL with Python. This will be useful because databases are ubiquitous and data scientists, analysts, and engineers must interact with them constantly. The Python SQL toolkit SQLAlchemy provides an accessible and intuitive way to query, build, and write to essential databases, including SQLite, MySQL, and PostgreSQL.

In this course, you'll learn the basics of relational databases and how to interact with them.

Discover the Python SQL toolkit, SQLAlchemy, which provides an accessible way to query, build, and write to databases, like SQLite, MySQL, and PostgreSQL.

Introduction to Databases in Python

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)

In this course, you'll learn natural language processing (NLP) basics, such as how to identify and separate words, how to extract topics in a text, and how to build your own fake news classifier. You'll also learn how to use basic libraries such as NLTK, alongside libraries which utilize deep learning to solve common NLP problems. This course will give you the foundation to process and parse text as you move forward in your Python learning.

Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

Learn fundamental natural language processing (NLP) techniques using Python and how to apply them to extract insights from real-world text data in Python.

Introduction to Natural Language Processing in Python

This course will teach you how to evaluate basic portfolio risk and returns like a quantitative analyst on Wall Street. This is the most critical step towards being able to fully automate your portfolio construction and management processes. Discover what factors are driving your portfolio returns, construct market-cap weighted equity portfolios, and learn how to forecast and hedge market risk via scenario generation.

Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.

Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation

Introduction to Portfolio Risk Management in Python

The most successful companies today are the ones that know their customers so well that they can anticipate their needs.
Customer analytics and in particular A/B Testing are crucial parts of
leveraging quantitative know-how to help make business decisions that generate value. This course
 covers the ins and outs of how to use Python to
 analyze customer behavior and business trends as well as how to create, run,
 and analyze A/B tests to make proactive, data-driven business decisions.

Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.

Customer Analytics and A/B Testing in Python

Time series data is all around us; some examples are the weather, human behavioral patterns as consumers and members of society, and financial data. In this course, you'll learn how to calculate technical indicators from historical stock data, and how to create features and targets out of the historical stock data. You'll understand how to prepare our features for linear models, xgboost models, and neural network models. We will then use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. You will also learn how to evaluate the performance of the various models we train in order to optimize them, so our predictions have enough accuracy to make a stock trading strategy profitable.

Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.

Learn to model & predict stock data values using linear models, decision trees, random forests, and neural networks.

Machine Learning for Finance in Python

The financial industry is increasingly adopting Python for general-purpose programming and quantitative analysis, ranging from understanding trading dynamics to risk management systems. This course focuses specifically on introducing Python for financial analysis. Using practical examples, you will learn the fundamentals of Python data structures such as lists and arrays and learn powerful ways to store and manipulate financial data to identify trends.

This course introduces Python for financial analysis.

Learn how to use lists, arrays, and visualization in Python for financial analysis. You'll also get chance to apply techniques to a real-world case study.

Introduction to Python for Finance

The ability to build tools capable of retrieving and parsing information stored across the internet has been and continues to be valuable in many veins of data science. In this course, you will learn to navigate and parse html code, and build tools to crawl websites automatically. Although our scraping will be conducted using the versatile Python library scrapy, many of the techniques you learn in this course can be applied to other popular Python libraries as well, including BeautifulSoup and Selenium. Upon the completion of this course, you will have a strong mental model of html structure, will be able to build tools to parse html code and access desired information, and create a simple scrapy spiders to crawl the web at scale.

Learn to retrieve and parse information from the internet using the Python library scrapy.

Web Scraping with Python

Web Scraping in Python

Did you know that data scientists and data analysts spend a large amount of time cleaning data before they can analyze it? This is because real-world data is messy. To help you navigate messy data this course teaches you how to clean data stored in an SQL Server database. You’ll learn how to solve common problems such as how to clean messy strings, deal with empty values, compare the similarity between strings, and much more. You’ll get hands-on with all these tasks using a wide range of interesting and messy datasets, including monthly airline flights by airport, TV series and paper shop sales. Are you ready to get your hands messy?

Develop the skills you need to clean raw data and transform it into accurate insights.

Develop the skills you need to go from raw data to awesome insights as quickly and accurately as possible.

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