From social media to product reviews, text is an increasingly important type of data across applications, including marketing analytics. In many instances, text is replacing other forms of unstructured data due to how inexpensive and current it is. However, to take advantage of everything that text has to offer, you need to know how to think about, clean, summarize, and model text. In this course, you will use the latest tidy tools to quickly and easily get started with text. You will learn how to wrangle and visualize text, perform sentiment analysis, and run and interpret topic models.

Analyze text data in R using the tidy framework.

Find out how to analyze text data using the tidy framework in R. You'll learn to perform sentiment analysis, topic modeling and wrangle and visualize text.

Introduction to Text Analysis in R

It is estimated that over 70% of potentially usable business information is unstructured, often in the form of text data. Text mining provides a collection of techniques that allows us to derive actionable insights from unstructured data. In this course, we explore the basics of text mining using the bag of words method. The first three chapters introduce a variety of essential topics for analyzing and visualizing text data. The final chapter allows you to apply everything you've learned in a real-world case study to extract insights from employee reviews of two major tech companies.

Learn the bag of words technique for text mining with R.

Discover the basics of text mining using the bag of words method and a variety of other essential topics for analyzing and visualizing text data in R.

Text Mining with Bag-of-Words 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

Add sentiment analysis to your text mining toolkit! Sentiment analysis is used by text miners in marketing, politics, customer service and elsewhere. In this course you will learn to identify positive and negative language, specific emotional intent, and make compelling visualizations. You will end the course by applying your sentiment analysis skills to Airbnb reviews to learn what makes for a good rental.

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

Sentiment Analysis in R

This course introduces students to the areas involved in topic modeling: preparation of corpus, fitting of topic models using Latent Dirichlet Allocation algorithm (in package topicmodels), and visualizing the results using ggplot2 and wordclouds.

Learn how to fit topic models using the Latent Dirichlet Allocation algorithm.

This course introduces students to the areas involved in topic modeling.

Topic Modeling in R

TensorFlow is a state-of-the-art machine learning framework that specializes in the ability to develop deep learning neural networks.  And now, it's available in R! This course will walk you through the basics of using TensorFlow in R. From simple linear regressions to more complex deep learning neural networks (which perform extremely well with BIG datasets) , you'll be introduced to both the basics of TensorFlow and higher-level APIs such as Keras and TFEstimators. We'll put your new-found skills to the test by exploring whether there is a predictable relationship between beer consumption and weather, and find out if we can accurately build a deep neural network to help predict whether a banknote is forged or genuine based on image data.

Learn how to use TensorFlow, a state-of-the-art machine learning framework that specializes in the ability to develop deep learning neural networks.

Introduction to TensorFlow in R

Datasets are often larger than available RAM, which causes problems for R programmers since by default all the variables are stored in memory. You’ll learn tools for processing, exploring, and analyzing data directly from disk. You’ll also implement the split-apply-combine approach and learn how to write scalable code using the bigmemory and iotools packages. In this course, you'll make use of the Federal Housing Finance Agency's data, a publicly available data set chronicling all mortgages that were held or securitized by both Federal National Mortgage Association (Fannie Mae) and Federal Home Loan Mortgage Corporation (Freddie Mac) from 2009-2015.

Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.

Scalable Data Processing in R

Missing data is part of any real world data analysis. It can crop up in unexpected places, making analyses challenging to understand. In this course, you will learn how to use tidyverse tools and the naniar R package to visualize missing values. You'll tidy missing values so they can be used in analysis and explore missing values to find bias in the data. Lastly, you'll reveal other underlying patterns of missingness. You will also learn how to "fill in the blanks" of missing values with imputation models, and how to visualize, assess, and make decisions based on these imputed datasets.

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

Learn how to use tidyverse tools and the naniar R package to 
visualize missing values, tidy values, and eveal other underlying patterns of missingness.

Dealing With Missing Data in R

As with any fundamentals course, Introduction to Natural Language Processing in R is designed to equip you with the necessary tools to begin your adventures in analyzing text. Natural language processing (NLP) is a constantly growing field in data science, with some very exciting advancements over the last decade. This course will cover the basics of these topics and prepare you for expanding your analysis capabilities. We dive into regular expressions, topic modeling, named entity recognition, and others, all while providing thorough examples that can be used to kick start your future analysis.

Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.

Discover basics skills and tools for natural language processing (NLP) in R, like regular expressions, topic modeling, named entity recognition and more.

Introduction to Natural Language Processing in R

Shiny is an R package that makes it easy to build highly interactive web apps directly in R. Using Shiny, data scientists can create interactive web apps that allow your team to dive in and explore your data as dashboards or visualizations. If you want to bring your data to life, Shiny is the way to go! Using data about baby names, food ingredients, and UFO sightings, you'll build a variety of different Shiny apps that leverage different inputs and outputs. You’ll also learn the basics of reactive expressions. By the end of this course, you’ll have the Shiny skills you need to build your first app in R.

Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations. 

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