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

A vital component of data science involves acquiring raw data and getting it into a form ready for analysis. It is commonly said that data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This course will equip you with all the skills you need to clean your data in Python, from learning how to diagnose problems in your data, to dealing with missing values and outliers. At the end of the course, you'll apply all of the techniques you've learned to a case study to clean a real-world Gapminder dataset.

This course will equip you with all the skills you need to clean your data in Python.

Data scientists spend 80% of their time cleaning and manipulating data. This course will equip you with all the skills you need to clean your data in Python.

Cleaning Data in Python

This beginner-level introduction to machine learning covers four of the most common classification algorithms. You will come away with a basic understanding of how each algorithm approaches a learning task, as well as learn the R functions needed to apply these tools to your own work.

In this course you will learn the basics of machine learning for classification.

Supervised Learning in R: Classification

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

<p>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.</p><p>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.</p>

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

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

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In the prequel to this course, you learned many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL. In this course, you'll extend this knowledge base by learning to import data from the web and by pulling data from Application Programming Interfaces— APIs—such as the Twitter streaming API, which allows us to stream real-time tweets.

Improve your Python data importing skills and learn to work with web and API data.

Learn how to import data into Python from sources like the web and by pulling data from APIs, such as the Twitter streaming API to stream real-time tweets.

Intermediate Importing Data in Python

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

You'll learn the many ways to read data into Python: from flat files such as CSVs to Excel spreadsheets and relational databases in SQLite & PostgreSQL.

Introduction to Importing Data in 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

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

From a machine learning perspective, regression is the task of predicting numerical outcomes from various inputs. In this course, you'll learn about different regression models, how to train these models in R, how to evaluate the models you train and use them to make predictions.

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

Supervised Learning in R: Regression

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

Bayesian data analysis is an approach to statistical modeling and machine learning that is becoming more and more popular. It provides a uniform framework to build problem specific models that can be used for both statistical inference and for prediction. This course will introduce you to Bayesian data analysis: What it is, how it works, and why it is a useful tool to have in your data science toolbox.

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

Learn what Bayesian data analysis is and how to use it for business modeling focusing on cash flows, investments, annuities, and saving for retirement in R

Fundamentals of Bayesian Data Analysis

Fundamentals of Bayesian Data Analysis in R

Once you've started learning tools for building interactive web applications with shiny, this course will translate this knowledge into building dashboards. Dashboards, a common data science deliverable, are pages that collate information, often tracking metrics from a live-updating data source. You'll gain more expertise using shiny while learning to build and design these dynamic dashboards. In the process, you'll pick up tips to optimize performance as well as best practices to create a visually appealing product.

In this course you'll learn to build dashboards using the shinydashboard package.

Building Dashboards with shinydashboard

The Bayesian approach to statistics and machine learning is logical, flexible, and intuitive. In this course, you will engineer and analyze a family of foundational, generalizable Bayesian models. These range in scope from fundamental one-parameter models to intermediate multivariate & generalized linear regression models.  The popularity of such Bayesian models has grown along with the availability of computing resources required for their implementation. You will utilize one of these resources - the rjags package in R. Combining the power of R with the JAGS (Just Another Gibbs Sampler) engine, rjags provides a framework for Bayesian modeling, inference, and prediction.

In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.

Bayesian Modeling with RJAGS

You've taken a survey (or 1000) before, right? Have you ever wondered what goes into designing a survey and how survey responses are turned into actionable insights? Of course you have! In Analyzing Survey Data in R, you will work with surveys from A to Z, starting with common survey design structures, such as clustering and stratification, and will continue through to visualizing and analyzing survey results. You will model survey data from the National Health and Nutrition Examination Survey using R's survey and tidyverse packages. Following the course, you will be able to successfully interpret survey results and finally find the answers to life's burning questions!

Learn survey design using common design structures followed by visualizing and analyzing survey results.

Analyzing Survey Data in R

For many machine learning problems, simply running a model out-of-the-box and getting a prediction is not enough; you want the best model with the most accurate prediction. One way to perfect your model is with hyperparameter tuning, which means optimizing the settings for that specific model. In this course, you will work with the caret, mlr and h2o packages to find the optimal combination of hyperparameters in an efficient manner using grid search, random search, adaptive resampling and automatic machine learning (AutoML). Furthermore, you will work with different datasets and tune different supervised learning models, such as random forests, gradient boosting machines, support vector machines, and even neural nets. Get ready to tune!

Learn how to tune your model's hyperparameters to get the best predictive results.

Use the caret, mlr and h2o packages to find optimal hyperparameters using grid search, random search, adaptive resampling and automatic machine learning.

Hyperparameter Tuning in R

This course will introduce a powerful classifier, the support vector machine (SVM) using an intuitive, visual approach. Support Vector Machines in R will help students develop an understanding of the SVM model as a classifier and gain practical experience using R’s libsvm implementation from the e1071 package. Along the way, students will gain an intuitive understanding of important concepts, such as hard and soft margins, the kernel trick, different types of kernels, and how to tune SVM parameters. Get ready to classify data with this impressive model.

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

Support Vector Machines in R

Data visualization is an integral part of the data analysis process. This course will get you introduced to rbokeh: a visualization library for interactive web-based plots. You will learn how to use rbokeh layers and options to create effective visualizations that carry your message and emphasize your ideas. We will focus on the two main pieces of data visualization: wrangling data in the appropriate format as well as employing the appropriate visualization tools, charts and options from rbokeh.

Learn rbokeh: a visualization library for interactive web-based plots.

Learn how to wrangle data in the appropriate format and employ the appropriate visualization tools, charts, and options from rbokeh visualization library.

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