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)

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)

A majority of data is stored in databases and knowing the necessary tools needed to analyze and clean data directly in databases is indispensable. This course focuses on T-SQL, the version of SQL used in Microsoft SQL Server, needed for data analysis. You will learn several concepts in this course such as dealing with missing data, working with dates, and calculating summary statistics using advanced queries. After completing this course, you will have the skills needed to analyze data and provide insights quickly and easily.

In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.

Learn concepts for working in  T-SQL, such as dealing with missing data, working with dates, and calculating summary statistics using advanced queries.

Intermediate SQL Server

In this finance-oriented introduction to R, you will learn essential data structures such as lists and data frames and have the chance to apply that knowledge to real-world financial examples. By the end of the course, you will be comfortable with the basics of manipulating your data to perform financial analysis in R.

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Learn the essential data structures, including lists and data frames and have the chance to apply that knowledge directly to financial examples using R.

Intro to R for Finance

Introduction to R for Finance

Time series are all around us, from server logs to high frequency financial data. Managing and manipulating ordered observations is central to all time series analysis. The xts and zoo packages provide a set of powerful tools to make this task fast and mistake free. In this course, you will learn everything from the basics of xts to advanced tips and tricks for working with time series data in R.

The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.

You will learn everything from the basics of working with the xts and zoo packages to advanced tips and tricks for working with time series data in R. 

Manipulating Time Series Data with xts and zoo in R

Many phenomena in our day-to-day lives, such as the movement of stock prices, are measured in intervals over a period of time. Time series analysis methods are extremely useful for analyzing these special data types. In this course, you will be introduced to some core time series analysis concepts and techniques.

Learn the core techniques necessary to extract meaningful insights from time series data.

Learn the core techniques necessary to extract meaningful insights from time series data, including WN and RW models, correlation analysis, and more. 

Time Series Analysis in R

If you enjoyed the Introduction to R for Finance course, then you will love Intermediate R for Finance. Here, you will first learn the basics about how dates work in R, an important skill for the rest of the course. Your next step will be to explore the world of if statements, loops, and functions. These are powerful ideas that are essential to any financial data scientist's toolkit. Finally, we will spend some time working with the family of apply functions as a vectorized alternative to loops. And of course, all examples will be finance related! Enjoy!

Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.

Learn about how dates work in R, and explore the world of if statements, loops, and functions. You'll practice this knowledge using financial examples.

Intermediate R for Finance

Forecasting involves making predictions about the future. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call centre next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.

Learn how to make predictions about the future using time series forecasting in R.

Make predictions about the future using time series forecasting in R. You will learn how to plot data, observe patterns, benchmark, forecast, and more.

Forecasting in R

In this course, you will become an expert in fitting ARIMA models to time series data using R.  First, you will explore the nature of time series data using the tools in the R stats package.  Next, you learn how to fit various ARMA models to simulated data (where you will know the correct model) using the R package astsa.  Once you have mastered the basics, you will learn how to fit integrated ARMA models, or ARIMA models to various real data sets. You will learn how to check the validity of an ARIMA model and you will learn how to forecast time series data.  Finally, you will learn how to fit ARIMA models to seasonal data, including forecasting using the astsa package.

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

In this course, you will become an expert in fitting ARIMA models to time series data using R, from fitting to simulated data and seasonal data to using astsa.

ARIMA Models in R

A golden rule in investing is to always test the portfolio strategy on historical data, and, once you are trading the strategy, to constantly monitor its performance. In this course, you will learn this by critically analyzing portfolio returns using the package PerformanceAnalytics. The course also shows how to estimate the portfolio weights that optimally balance risk and return. This is a data-driven course that combines portfolio theory with the practice in R, illustrated on real-life examples of equity portfolios and asset allocation problems.  If you'd like to continue exploring the data after you've finished this course, the data used in the first three chapters can be obtained using the tseries-package. The code to get them can be found <a href='http://s3.amazonaws.com/assets.datacamp.com/course/portfolio-analysis/data_portfolio_analysis.R' target='_blank'>here</a>. The data used in chapter 4 can be downloaded <a href='http://s3.amazonaws.com/assets.datacamp.com/course/portfolio-analysis/prices.rds' target='_blank'>here</a>.

Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.

Apply your R skills to backtest, analyze, and optimize financial portfolios. The course also shows how to estimate the weights that optimize risk and return.

Introduction to Portfolio Analysis in R

If you've ever done anything with financial or economic time series, you know the data come in various shapes, sizes, and periodicities. Getting the data into R can be stressful and time-consuming, especially when you need to merge data from several different sources into one data set. This course will cover importing data from local files as well as from internet sources.

Learn how to access financial data from local files as well as from internet sources.

Learn how to import and manage different financial or economic time series data into R from different sources, including local files and the internet.

Importing and Managing Financial Data in R

As the saying goes, “A chart is worth a thousand words”. This is why visualization is the most used and powerful way to get a better understanding of your data. After this course you will have a very good overview of R time series visualisation capabilities and you will be able to better decide which model to choose for subsequent analysis. You will be able to also convey the message you want to deliver in an efficient and beautiful way.

Learn how to visualize time series in R, then practice with a stock-picking case study.

Visualizing Time Series Data in R

In this course, you will strengthen your knowledge of time series topics through interactive exercises and interesting datasets. You’ll explore a variety of datasets about Boston, including data on flights, weather, economic trends, and local sports teams.

Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

Strengthen your knowledge of the topics covered in Manipulating Time Series in R with xts and zoo using real case study data.

Case Studies: Manipulating Time Series Data in R

Are you ready to gain the foundational skills you need to become a Python programmer? In this track, you'll learn the Python basics you need to start on your programming journey, including how to clean real-world data ready for analysis, use data visualization libraries, and even how to write your own Python functions.

Your instructor Hugo will introduce you to how companies worldwide use Python to gain a competitive edge. Through hands-on coding exercises you’ll then learn how to store, manipulate, and explore data using NumPy. Then it’s time to level-up as you learn how to visualize your data using Matplotlib, manipulate DataFrames and dictionaries using pandas, and write your own functions and list comprehension. Start this track to add these essential Python skills to your data science toolbox.

Grow your programmer skills. Discover how to manipulate dictionaries and DataFrames, visualize real-world data, and write your own Python functions.

Python Fundamentals

Time series are all around us, from server logs to high-frequency financial data. Learn the core techniques necessary to extract meaningful insights from time series data.

with R

Time Series 

If you are just starting to learn about finance and are new to R, this is the right track to kick things off! In this track, you will learn the basics of R and apply your new knowledge directly to finance examples, start manipulating your first (financial) time series, and learn how to pull financial data from local files as well as from internet sources.

Learn the very basics of R and finance, start manipulating your first-time series, and learn to pull financial data from local files and the internet.

with R 

Finance Basics

This course builds on the fundamental concepts from Introduction to Portfolio Analysis in R and explores advanced concepts in the portfolio optimization process. It is critical for an analyst or portfolio manager to understand all aspects of the portfolio optimization problem to make informed decisions. In this course, you will learn a quantitative approach to apply the principles of modern portfolio theory to specify a portfolio, define constraints and objectives, solve the problem, and analyze the results. This course will use the R package PortfolioAnalytics to solve portfolio optimization problems with complex constraints and objectives that mirror real world problems.

Advance you R finance skills to backtest, analyze, and optimize financial portfolios.

Learn to apply the principles of modern portfolio theory to specify a portfolio, define constraints and objectives, solve the problem, and analyze results.

Portfolio Analysis in R 

Intermediate Portfolio Analysis in R

Now that you've learned the basics of SQL in our <a href="https://learn.datacamp.com/courses/introduction-to-sql">Introduction to SQL</a> course,  it's time to supercharge your queries using joins and relational set theory. In this course, you'll learn all about the power of joining tables while exploring interesting features of countries and their cities throughout the world. You will master inner and outer joins, as well as self joins, semi joins, anti joins and cross joins—fundamental tools in any PostgreSQL wizard's toolbox. Never fear set theory again after learning all about unions, intersections, and except clauses through easy-to-understand diagrams and examples. Lastly, you'll be introduced to the challenging topic of subqueries. You will be able to visually grasp these ideas by using Venn diagrams and other linking illustrations.

Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.

Discover all the fundamental in PostgreSQL, like inner, outer self, semi, anti, and cross joins, as well as unions, intersections, and except clauses.

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