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

Object-oriented programming (OOP) is a powerful programming paradigm that reduces the complexity of systems as is employs heavy re-usability of code. OOP leverages the concept of objects and classes. Objects may contain data (known as attributes, stored as instance or class variables) as well as procedures (defined as methods). Objects are generated from blueprints known as classes, which specify the attributes and methods to be generated at the time of object instantiation. This course introduces the internals of classes and the utilization of objects, as well as important object-oriented programming fundamentals such as inheritance, polymorphism, and composition.

Learn the fundamentals of object-oriented programming: classes, objects, methods, inheritance, polymorphism, and others!

Working with the Class System in Python

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

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)

From online social networks such as Facebook and Twitter to transportation networks such as bike sharing systems, networks are everywhere—and knowing how to analyze them will open up a new world of possibilities for you as a data scientist. This course will equip you with the skills to analyze, visualize, and make sense of networks. You'll apply the concepts you learn to real-world network data using the powerful NetworkX library. With the knowledge gained in this course, you'll develop your network thinking skills and be able to look at your data with a fresh perspective.

This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

Taught by Eric Ma from the Runstadler Lab at MIT, this course will equip you with the skills to analyze and visualize networks such as Facebook and Twitter.

Introduction to Network Analysis in Python

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

Object-oriented programming (OOP) lets you specify relationships between functions and the objects that they can act on, helping you manage complexity in your code. This is an intermediate level course, providing an introduction to OOP, using the S3 and R6 systems. S3 is a great day-to-day R programming tool that simplifies some of the functions that you write. R6 is especially useful for industry-specific analyses, working with web APIs, and building GUIs.

Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.

Object-Oriented Programming with S3 and R6 in R

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

Do you know the basics of supervised learning and want to use state-of-the-art models on real-world datasets? Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes. XGboost is a very fast, scalable implementation of gradient boosting, with models using XGBoost regularly winning online data science competitions and being used at scale across different industries. In this course, you'll learn how to use this powerful library alongside pandas and scikit-learn to build and tune supervised learning models. You'll work with real-world datasets to solve classification and regression problems.

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

Explore the fundamentals of gradient boosting, with a focus on Regression with XGBoost, using XGBoost in pipelines and how to fine-tune your XGBoost model.

Extreme Gradient Boosting with XGBoost

In this course, you'll learn how to use Spark from Python! Spark is a tool for doing parallel computation with large datasets and it integrates well with Python. PySpark is the Python package that makes the magic happen. You'll use this package to work with data about flights from Portland and Seattle. You'll learn to wrangle this data and build a whole machine learning pipeline to predict whether or not flights will be delayed. Get ready to put some Spark in your Python code and dive into the world of high-performance machine learning!

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

Learn to wrangle data and build a machine learning pipeline to make predictions with  PySpark Python package. Practice your skills with real-world data.

Introduction to PySpark

In this course, you will learn the end-to-end process for creating an R package from scratch.  You will start off by  creating the basic structure for your package, and adding in important details like functions and metadata. Once the basic components of your package are in place, you will learn about how to document your package, and why this is important for creating quality packages that other people - as well as your future self - can use with ease. Once you have created the components of your package, you will learn how to test they work properly, by creating tests, running checks, and building your package.  By the end of this course you can expect to have all the necessary skills to create and share your own R packages.

Learn how to create and share your own R Packages, from setting up the structure and writing functions to creating tests to run and check its functionality.

Developing R Packages

Python and R have seen immense growth in popularity in the "Machine Learning Age". They both are high-level languages that are easy to learn and write. The language you use will depend on your background and field of study and work. R is a language made by and for statisticians, whereas Python is a more general purpose programming language. Regardless of the background, there will be times when a particular algorithm is implemented in one language and not the other, a feature is better documented, or simply, the tutorial you found online uses Python instead of R.

In either case, this would require the R user to work in Python to get his/her work done, or try to understand how something is implemented in Python for it to be translated into R. This course helps you cross the R-Python language barrier.

This course is for R users who want to get up to speed with Python!

Python for R Users

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

This course begins by reviewing slopes and intercepts in linear regressions before moving on to random-effects. You'll learn what a random effect is and how to use one to model your data. Next, the course covers linear mixed-effect regressions. These powerful models will allow you to explore data with a more complicated structure than a standard linear regression. The course then teaches generalized linear mixed-effect regressions. Generalized linear mixed-effects models allow you to model more kinds of data, including binary responses and count data. Lastly, the course goes over repeated-measures analysis as a special case of mixed-effect modeling. This kind of data appears when subjects are followed over time and measurements are collected at intervals. Throughout the course you'll work with real data to answer interesting questions using mixed-effects models.

In this course you will learn to fit hierarchical models with random effects.

Discover what a random effect is and how to use one, as well as how to use generalized linear mixed-effect regressions and repeated-measures analysis.

Hierarchical and Mixed Effect Models in R

Hierarchical and Mixed Effects Models in R

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

Are you concerned about inaccurate or suspicious records in your data, but not sure where to start? An anomaly detection algorithm could help! Anomaly detection is a collection of techniques designed to identify unusual data points, and are crucial for detecting fraud and for protecting computer networks from malicious activity. In this course, you'll explore statistical tests for identifying outliers, and learn to use sophisticated anomaly scoring algorithms like the local outlier factor and isolation forest. You'll apply anomaly detection algorithms to identify unusual wines in the UCI Wine quality dataset and also to detect cases of thyroid disease from abnormal hormone measurements.

Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms. 

Introduction to Anomaly Detection in R

So you've learned how to aggregate and join data from tables in your database—now what? How do you manipulate, transform, and make the most sense of your data? This intermediate-level course will teach you several key functions necessary to wrangle, filter, and categorize information in a relational database, expand your SQL toolkit, and answer complex questions. You will learn the robust use of CASE statements, subqueries, and window functions—all while discovering some interesting facts about soccer using the European Soccer Database.

Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.

Intermediate SQL

Neural networks have been at the forefront of Artificial Intelligence research during the last few years, and have provided solutions to many difficult problems like image classification, language translation or Alpha Go. PyTorch is one of the leading deep learning frameworks, being at the same time both powerful and easy to use. In this course you will use PyTorch to first learn about the basic concepts of neural networks, before building your first neural network to predict digits from MNIST dataset. You will then learn about convolutional neural networks, and use them to build much more powerful models which give more accurate results. You will evaluate the results and use different techniques to improve them. Following the course, you will be able to delve deeper into neural networks and start your career in this fascinating field.

Learn to create deep learning models with the PyTorch library.

Learn to create deep learning models with the PyTorch library by building neural networks, including convolutional, to predict digits from MNIST dataset.

Introduction to Deep Learning with PyTorch

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.

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