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

Begin your journey into Data Science! Even if you've never written a line of code in your life, you'll be able to follow this course and witness the power of Python to perform Data Science. You'll use data to solve the mystery of Bayes, the kidnapped Golden Retriever, and along the way you'll become familiar with basic Python syntax and popular Data Science modules like Matplotlib (for charts and graphs) and pandas (for tabular data).

Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.

Dive into data science using Python and learn how to effectively analyze and visualize your data using Pandas and Matplotlib. No coding experience needed.

Introduction to Data Science 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

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

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)

Say you've found a great dataset and would like to learn more about it. How can you start to answer the questions you have about the data? You can use dplyr to answer those questions—it can also help with basic transformations of your data. You'll also learn to aggregate your data and add, remove, or change the variables. Along the way, you'll explore a dataset containing information about counties in the United States. You'll finish the course by applying these tools to the babynames dataset to explore trends of baby names in the United States.

Learn to transform and manipulate your data using dplyr. 

Learn how to use dplyr to transform and aggregate data, then add, remove, or change variables. You'll then apply your skills to a real-world case study.

Data Manipulation with dplyr

Have you got your basic Python programming chops down for Data Science but are  yearning for more? Then this is the course for you. Herein, you'll consolidate and practice your knowledge of lists, dictionaries, tuples, sets, and date times. You'll see their relevance in working with lots of real data and how to leverage several of them in concert to solve multistep problems, including an extended case  study using Chicago metropolitan area transit data. You'll also learn how to use many of the objects in the Python Collections module, which will allow you to store and manipulate your data for a variety of Data Scientific purposes. After taking this course, you'll be ready to tackle many Data Science challenges Pythonically.

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.

Data Types for Data Science in Python

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

This hands-on-course with real-life credit data will teach you how to model credit risk by using logistic regression and decision trees in R.Modeling credit risk for both personal and company loans is of major importance for banks. The probability that a debtor will default is a key component in getting to a measure for credit risk. While other models will be introduced in this course as well, you will learn about two model types that are often used in the credit scoring context; logistic regression and decision trees. You will learn how to use them in this particular context, and how these models are evaluated by banks.

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Learn to create models to evaluate financial credit risk like a professional

Credit Risk Modeling in R

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

In Quantitative Risk Management (QRM), you will build models to understand the risks of financial portfolios. This is a vital task across the banking, insurance and asset management industries. The first step in the model building process is to collect data on the underlying risk factors that affect portfolio value and analyze their behavior. In this course, you will learn how to work with risk-factor return series, study the empirical properties or so-called "stylized facts" of these data - including their typical non-normality and volatility, and make estimates of value-at-risk for a portfolio.

Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk. 

In this course, you will work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk for a portfolio.

Quantitative Risk Management in R

This course will cover the basics on financial trading and will give you an overview of how to use quantstrat to build signal-based trading strategies in R. It will teach you how to set up a quantstrat strategy, apply transformations of market data called indicators, create signals based on the interactions of those indicators, and even simulate orders. Lastly, it will explain how to analyze your results both from statistical and visual perspectives.

This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.

This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies in R and even simulate orders.

Financial Trading in R

After this course on quantitative finance with R, you will be able to use R to develop a model to value a fixed interest rate bond, estimate and analyze a bond's yield (i.e., a measure of the opportunity cost of bond investors), and model techniques used to protect bond portfolios from changes in interest rates. Why value bonds? Bonds are securities issued by governments or corporations that pay interest over a fixed schedule and are the most well-known type of fixed income securities. The US fixed income market is 1.5x larger than the US stock market, but, unlike stocks, most fixed income instruments, including bonds, trade very infrequently. Consequently, a bond's price may be a less reliable indicator of its value and analytical techniques are necessary when analyzing and valuing bonds.

Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.

Bond Valuation and Analysis 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

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 finance, quantitative analysts ensure portfolios are risk balanced, help find new trading opportunities, and evaluate asset prices using mathematical models.

Ensure portfolios are risk balanced, help find new trading opportunities, and evaluate asset prices using mathematical models.

 with R

Quantitative Analyst

In this introduction to R course, you'll master the basics of this widely used open source language&mdash;including vectors, factors, lists, and data frames. With the coding skills you'll gain in this course, you'll be ready to undertake your own data analysis in R. There are millions of R users worldwide, cementing it as a leading programming language in statistics and data science. More and more organizations are using R, so begin your coding journey in one of DataCamp's most popular courses today!

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

DataCamp's Into to R training course teaches you how to use R programming for data science at your own pace with video tutorials & interactive challenges.

Introduction to R Online

Introduction to R

In this course, you will take a deeper dive into the wide range of data formats out there. More specifically, you'll learn how to import data from relational databases and how to import and work with data coming from the web. Finally, you'll get hands-on experience with importing data from statistical software packages such as SAS, STATA, and SPSS.

Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

Learn how you can use R to parse data in any format, whether that data is from flat files, statistical software, databases, or data right from the web.

Intermediate Importing Data in R

Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.

Discover how you can use deep learning to run natural language processing, image recognition, and artificial intelligence with Python package, Keras 2.0.

Introduction to Deep Learning in Python

R is mostly optimized to help you write data analysis code quickly and readably. Apache Spark is designed to analyze huge datasets quickly. The <code>sparklyr</code> package lets you write <code>dplyr</code> R code that runs on a Spark cluster, giving you the best of both worlds. This course teaches you how to manipulate Spark DataFrames using both the <code>dplyr</code> interface and the native interface to Spark, as well as trying machine learning techniques. Throughout the course, you'll explore the Million Song Dataset.

Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package.

Learn how to analyze huge datasets and manipulate Spark DataFrames using the dplyr and Spark interface, as well as trying machine learning techniques in R.

Introduction to Spark with sparklyr 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.

Joining Data in SQL

This is an introduction to the programming language R, focused on a powerful set of tools known as the Tidyverse. You'll learn the intertwined processes of data manipulation and visualization using the tools dplyr and ggplot2. You'll learn to manipulate data by filtering, sorting, and summarizing a real dataset of historical country data in order to answer exploratory questions. You'll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. You’ll get a taste of the value of exploratory data analysis and the power of Tidyverse tools. This is a suitable introduction for those who have no previous experience in R and are interested in performing data analysis.

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.

Discover the fundamentals of the Tidyverse, and learn all about renaming and reordering variables, while becoming familiar with binomial distribution.

Introduction to the Tidyverse

Being able to combine and work with multiple datasets is an essential skill for any aspiring Data Scientist. pandas is a crucial cornerstone of the Python data science ecosystem, with Stack Overflow recording 5 million views for pandas questions. Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using pandas. You'll work with datasets from the World Bank and the City Of Chicago. You will finish the course with a solid skillset for data-joining in pandas.

Learn to combine data from multiple tables by joining data together using pandas.

Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using Pandas. You'll gain a solid skillset for data-joining.

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