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

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.

Data Manipulation with pandas

pandas DataFrames are the most widely used in-memory representation of complex data collections within Python. Whether in finance, a scientific field, or data science, familiarity with pandas is essential. This course teaches you to work with real-world datasets containing both string and numeric data, often structured around time series. You will learn powerful analysis, selection, and visualization techniques in this course.

Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.

Pandas Foundations

pandas Foundations

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

If you want to apply your new 'Python for Data Science' skills to real-world financial data, then this course will give you some very valuable tools.
First, you will learn how to get data out of Excel into pandas and back. Then, you will learn how to pull stock prices from various online APIs like
Google or Yahoo! Finance, macro data from the Federal Reserve, and exchange rates from OANDA. Finally, you will learn how to calculate returns for various time horizons,
analyze stock performance by sector for IPOs, and calculate and summarize correlations.


In this course, you'll learn how to import and manage financial data in Python using various tools and sources.

Learn how to pull stock prices from online APIs to calculate returns for time horizons, analyze stock performance by sector, and summarize correlations.

Importing and Managing Financial Data 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

Understanding the basic principles of finance is essential for making important financial decisions ranging from taking out a student loan to constructing an investment portfolio. Combining basic financial knowledge with Python will allow you to construct some very powerful tools. You'll come out of this course understanding the time value of money, how to compare potential projects and how to make rational, data-driven financial decisions.

Using Python and NumPy, learn the most fundamental financial concepts. 

Learn about the time value of money, how to compare potential projects, and how to make rational, data-driven, financial decisions with Python and Numpy.

Introduction to Financial Concepts in Python

Managing risk using Quantitative Risk Management is a vital task across the banking, insurance, and asset management industries. It’s essential that financial risk analysts, regulators, and actuaries can quantitatively balance rewards against their exposure to risk.


This course introduces you to financial portfolio risk management through an examination of the 2007—2008 financial crisis and its effect on investment banks such as Goldman Sachs and J.P. Morgan. You’ll learn how to use Python to calculate and mitigate risk exposure using the Value at Risk and Conditional Value at Risk measures, estimate risk with techniques like Monte Carlo simulation, and use cutting-edge  technologies such as neural networks to conduct real time portfolio rebalancing.

Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python. 

Learn about risk management, value at risk and more applied to the 2008 financial crisis in Python. 

Quantitative Risk Management in Python

Have you ever had wondered whether an investment fund is actually a good investment? Or compared two investment options and asked what the difference between the two is? What does the risk indicator of these funds even mean? Or do you frequently work with financial data in your daily job and you want to get an edge? In this course, you’re going to get familiar with the exciting world of investing, by learning about portfolios, risk and return, and how to critically analyze them. By working on actual historical stock data, you’ll learn how to calculate meaningful measures of risk, how to break-down performance, and how to calculate an optimal portfolio for the desired risk and return trade-off. After this course, you’ll be able to make data-driven decisions when it comes to investing and have a better understanding of investment portfolios.

Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off. 

Introduction to Portfolio Analysis in Python

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

Are you a financial or business analyst, or simply looking for an easier way to manage your stock portfolio? If so learning Python can automate financial tasks such as calculating risk, mapping market health, and visualizing a stock's price trends, saving you time and money.

In this course, you’ll learn how to use Python data structures, execution control statements, and DataFrames to manipulate financial data. You will then work with pandas, using data from the Federal Reserve Bank, to explore national economic trends—an essential part of understanding investment strategies. You will also calculate risk based on stock price data, and display this data in easy to read plots. By the end of this course, you’ll be the new Python of Wall Street.

Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.

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