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

There's been a lot of buzz about Big Data over the past few years, and it's finally become mainstream for many  companies. But what is this Big Data? This course covers the fundamentals of Big Data via PySpark. Spark is a "lightning fast cluster computing" framework for Big Data. It provides a general data processing platform engine and lets you run programs up to 100x faster in memory, or 10x faster on disk,  than Hadoop. You’ll use PySpark, a Python package for Spark programming and its powerful, higher-level libraries such as SparkSQL, MLlib (for machine learning), etc. You will explore the works of William Shakespeare, analyze Fifa 2018 data and perform clustering on genomic datasets. At the end of this course, you will have gained an in-depth understanding of PySpark and its application to general Big Data analysis.

Learn the fundamentals of working with big data with PySpark.

Learn to conduct Big Data analysis using the Python package for Spark programming, PySpark, as well as higher level libraries like SparkSQL and MLlib.

Big Data Fundamentals with PySpark

Intermediate R is the next stop on your journey in mastering the R programming language. In this R training, you will learn about conditional statements, loops, and functions to power your own R scripts. Next, make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in R, data structure manipulations, and times and dates. This course will allow you to take the next step in advancing your overall knowledge and capabilities while programming in R.

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

Discover conditional statements, loops, and functions to power your own R scripts, and learn to make your R code more efficient using the apply functions.

Intermediate 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

This course provides a comprehensive introduction on how to plot data with R’s default graphics system, base graphics.After an introduction to base graphics, we look at a number of R plotting examples, from simple graphs such as scatterplots to plotting correlation matrices. The course finishes with exercises in plot customization. This includes using R plot colors effectively and creating and saving complex plots in R.Base Graphics Background R supports four different graphics systems: base graphics, grid graphics, lattice graphics, and ggplot2. Base graphics is the default graphics system in R, the easiest of the four systems to learn to use, and provides a wide variety of useful tools, especially for exploratory graphics where we wish to learn what is in an unfamiliar dataset.

This course provides a comprehensive introduction to working with base graphics in R.

Data Visualization 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

Understanding the basic principles of life insurance products is essential for your personal financial planning, ranging from taking out a mortgage to designing your retirement plan and seeking financial protection for the risk of dying early. In this course, you'll study the time value of money and you’ll work with human mortality data to derive demographic markers (such as the life expectancy). Combining the basics of cash flow valuation with the calculation of survival and death probabilities in R will allow you to construct insightful tools to design life insurance products. You'll come out of this course understanding the valuation of life contingent claims: life annuities, which provide an income upon survival, and life insurance products, which pay a benefit upon death of the policyholder.

Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.

Learn about the basics of cash flow valuation, work with human mortality data and build life insurance products in R by studying the time value of money.

 Life Insurance Products Valuation in R

Linear regression serves as a workhorse of statistics, but cannot handle some types of complex data. A generalized linear model (GLM) expands upon linear regression to include non-normal distributions including binomial and count data. Throughout this course, you will expand your data science toolkit to include GLMs in R. As part of learning about GLMs, you will learn how to fit model binomial data with logistic regression and count data with Poisson regression. You will also learn how to understand these results and plot them with ggplot2.

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

Learn to fit model binomial data with logistic regression to count data with Poisson regression, and how to understand and plot the results with ggplot2.

Generalized Linear Models in R

The most successful companies today are the ones that know their customers so well that they can anticipate their needs. Data analysts play a key role in unlocking these in-depth insights, and segmenting the customers to better serve them. In this course, you will learn real-world techniques on customer segmentation and behavioral analytics, using a real dataset containing anonymized customer transactions from an online retailer. You will first run cohort analysis to understand customer trends. You will then learn how to build easy to interpret customer segments. On top of that, you will prepare the segments you created, making them ready for machine learning. Finally, you will make your segments more powerful with k-means clustering, in just few lines of code! By the end of this course, you will be able to apply practical customer behavioral analytics and segmentation techniques.

Learn how to segment customers in Python.

Learn techniques to segmment your customer data in Python, including behaviour, transaction, and cohort data. You will learn about RFM analysis and more.

Customer Segmentation in Python

Spark is a powerful, general purpose tool for working with Big Data. Spark transparently handles the distribution of compute tasks across a cluster. This means that operations are fast, but it also allows you to focus on the analysis rather than worry about technical details. In this course you'll learn how to get data into Spark and then delve into the three fundamental Spark Machine Learning algorithms: Linear Regression, Logistic Regression/Classifiers, and creating pipelines. Along the way you'll analyse a large dataset of flight delays and spam text messages. With this background you'll be ready to harness the power of Spark and apply it on your own Machine Learning projects!

Learn how to make predictions with Apache Spark.

Learn how to make predictions from data with PySpark in Python, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Machine Learning with PySpark

Have you heard people talk about data engineers and wonder what it is they do? Do you know what data engineers do but you're not sure how to become one yourself? This course is the perfect introduction. It touches upon all things you need to know to streamline your data processing. This introductory course will give you enough context to start exploring the world of data engineering. It's perfect for people who work at a company with several data sources and don't have a clear idea of how to use all those data sources in a scalable way. Be the first one to introduce these techniques to your company and become the company star employee.

Learn about the world of data engineering with an overview of all its relevant topics and tools!

Introduction to Data Engineering

Delivering data on a schedule can be a manual process. You write scripts, add complex cron tasks, and try various ways to meet an ever-changing set of requirements—and it’s even trickier to manage everything when working with teammates. Airflow can remove this headache by adding scheduling, error handling, and reporting to your workflows. In this course, you’ll master the basics of Airflow and learn how to implement complex data engineering pipelines in production. You'll also learn how to use Directed Acyclic Graphs (DAGs), automate data engineering workflows, and implement data engineering tasks in an easy and repeatable fashion—helping you to maintain your sanity.

Learn how to implement and schedule data engineering workflows.

Learn to implement and schedule data engineering workflows by adding scheduling, error handling, and reporting using Airflow and Airflow DAGs in Python. 

Introduction to Airflow in Python

Gain a 360° overview of exploring and using Power BI to build impactful reports. In this course, you’ll go from zero to hero as you discover how to use this popular business intelligence platform through hands-on exercises. Before diving into creating visualizations using Power BI's drag-and-drop functionality, you’ll first learn how to confidently load and transform data using Power Query and the importance of data models. You’ll also learn to drill down into reports and make your reports fully interactive. You've got the power!

Gain a 360° overview of how to explore and use Power BI to build impactful reports.

Learn how to use the Power BI platform to load and transform data, create visualzations, run DAX formulas, and create reports through hands-on exercises.

Introduction to Power BI

There’s a lot more to Microsoft Excel than SUM and COUNT. Companies and institutions around the world use Excel’s powerful functions to efficiently transform mountains of raw data into clear insights, and now you can too.

 

In this course, you’ll develop employable analyst skills as you learn how to use time-saving keyboard shortcuts, convert and clean data types including text, times and dates, and build impressive logic functions and conditional aggregations. Through hands-on practice, you’ll learn over 35 new Excel functions, including CONCATENATE, VLOOKUP, and AVERAGEIF(S), and work with real-world Kickstarter data as you use your new-found Excel skills to analyze what makes a successful project.

Discover time-saving shortcuts in Excel to help you convert and clean your data. You will learn 35+ new Excel functions including CONCAT, VLOOKUP, and more.

Data Analysis in Excel

Every day we interact with the cloud—whether it’s using Google Drive, apps like Salesforce, or accessing our favorite websites. Cloud computing has become the norm for many companies, but what exactly is the cloud and why is everyone rushing to adopt it? Designed for absolute novices, this course breaks down what the cloud is and explains terminology such as scalability, latency, and high-availability. You'll learn about the many advantages including ease of remote collaboration, how there are no hardware limitations, and reliable disaster recovery. You'll also discover the range of tools provided by major cloud providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. By the end of this course, you'll be able to confidently explain how cloud tools can increase productivity and save money, as well as ask the right questions about how to optimize your use of cloud tools.

A non-coding introduction to the world of cloud computing.

Discover what the cloud is, how it works, and what its benefits are. You’ll also learn about terminology like scalability, latency, and high availability.

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