Learning Path: R Programming
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Learning Path: R Programming

Get started with Spark for large-scale distributed data science and processing
4.2 (7 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
72 students enrolled
Created by Packt Publishing
Last updated 4/2017
English
Curiosity Sale
Current price: $10 Original price: $200 Discount: 95% off
30-Day Money-Back Guarantee
Includes:
  • 9 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Create and master the manipulation of vectors, lists, dataframes, and matrices
  • Write conditional control structures, and debug and handle errors for efficient error handling
  • Handle dates using lubridate and manipulate strings with stringr package
  • Work with databases without having to write SQL using the dplyr package
  • Work on a full-scale data analysis / data munging project
  • Perform pre-model-building steps
  • Understand the working behind core machine learning algorithms
  • Implement unsupervised learning algorithms
  • Construct nice looking charts with Ggplot2
  • Build R packages from scratch and submit them to CRAN
View Curriculum
Requirements
  • This is for absolute beginners. No prior knowledge of R is required.
Description

Do you want to step into the ever-growing field of data science? Do you wish to equip yourself with one of the most widely used language for data science?

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

Data is on the rise and it’s the need of the hour to process it and make sense out it. Analysts and statisticians need to get this job done. It’s an art to tactfully and efficiently process data. But, as it goes an art becomes a reality only with the help of right tools and the knowledge of using these right. So, it is with data science. R is a powerful language that provides with all the tools required to build probabilistic models, perform data science, and build machine learning algorithms.

With this Learning Path, you’ll be introduced to R Studio and the basics of R. Then, you’ll taken through a number of topics such as handling dates with the lubridate package, handling strings with the stringr package, and making statistical inferences. Finally,  the focus will be on machine learning concepts in depth and applying them in the real world with R.

The goal of this course to introduce you to R and have a solid knowledge of machine learning and the R language itself. You’ll also solve numerous coding challenges throughout the course.

This Learning Path is authored by one of the best in the fields.

Selva Prabhakaran Selva Prabhakaran is a data scientist with a large E-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies. Selva lives in Bangalore with his wife.

Who is the target audience?
  • If you are looking to start your data science career, or are already familiar with data science, statistics, and machine learning concepts, but want to switch to R, this Video Learning Path will be a great place to start.
  • The Learning Path follows a pragmatic approach where you’ll find step-by-step instructions of the functions, tools, and concepts, and the reason you’re learning about them. Most of the videos close with coding challenges, putting your newly learned skills into practical use immediately. You’ll get hands-on working sessions and detailed explanations.
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Curriculum For This Course
103 Lectures
08:58:22
+
Introduction to R Programming
49 Lectures 03:46:22

This video will provide you an overview of entire course.

Preview 04:54

The aim of this video is to show how to install R on our system.

Installing R
03:45

To run and write code in R, we first need to focus on how to get and install the IDE.

Installing RStudio
04:35

We have installed R and RStudio. Now let’s check out how to install the packages.

Installing Packages
04:49

The aim of this video is to teach you what data types and data structures in R are.

Data Types and Data Structures
03:04

In this video, we will see how to work with vectors in R.

Vectors
05:43

The aim of this video is to show how to work with random numbers and do rounding and binning.

Random Numbers, Rounding, and Binning
04:00

Taking vectors a step ahead, let’s see how we can to handle missing values.

Missing Values
02:46

We now know a lot about how vectors work, but how do we get specific items from a vector based on any condition? Let’s check out just that in this video.

The which() Operator
03:11

This video will introduce a new data structure called list and how to work with it.

Lists
04:34

In this video, our goal is to understand how to perform set operations in R.

Set Operations
02:08

What is sampling and sorting and how to do it in R? 

Sampling and Sorting
02:52

Checking conditions is often a requirement for a programmer to write maintainable code. Let’s understand how we can check conditions in R.

Check Conditions
02:17

You may have come across several instances whilst coding where you need to perform repetitive operations through loops, right? In this video, we’ll see how to do that in R using for loops.

For Loops
02:34

Let’s explore what data frames are and how to work with them.

Dataframes
08:30

In this video, we will check out how to import and export data in R.

Importing and Exporting Data
06:29

The aim of this video is to check out how to work with matrices and frequency tables.

Matrices and Frequency Tables
03:41

Our goal in this video is to use W to merge data frames.

Merging Dataframes
02:26

How to do aggregation in R?

Aggregation
02:48

In this video, we will look at how to de-aggregate data frames and create cross tabulations. 

Melting and Cross Tabulations with dcast()
03:58

In this video, we will look at how to handle date variables in R.

Dates
05:35

The goal of this video is to see how to perform string operations in R.

String Manipulation
05:14

Let’s learn how to avoid code replication.

Functions
05:34

The aim of this video is to understand how to debug and handle errors.

Debugging and Error Handling
04:29

We’ll see in this video how to write fast loops with apply().

Fast Loops with apply()
04:26

Sometimes we’d want to iterate through lists. What do we do then? Let’s learn using fast loops with sapply, vapply and lapply to help us achieve this goal. 

Fast Loops with sapply(), lapply() and vapply()
01:59

How to make plots and customize them. 

Creating and Customizing an R Plot
07:03

Sometimes, just a single Y axis is not enough. It becomes difficult to depict the variations for two variables on different scales in the same chart. To solve this, we’ll look at how to make a plot with two Y axes. 

Drawing Plots with 2 Y Axes
02:23

In this video, we will learn how to make multiple plots and custom layout to get better at our analyzing skills.

Multiplots and Custom Layouts
03:07

The aim of this video is to create different types of plots.

Creating Basic Graph Types
04:47

What are the steps and actions one needs to do as part of data analysis before jumping to predictive modeling? Let’s understand this better.

Univariate Analysis
06:16

The aim of this video is to teach you what normal distribution, central limit theorem, and confidence intervals are.

Normal Distribution, Central Limit Theorem, and Confidence Intervals
05:32

In this video, we will understand correlation and Covariance, the concept behind them, and their implementation in R.

Correlation and Covariance
03:03

What is the chi-square statistic, when is it used, and how to do the chi-sq test? 

Chi-sq Statistic
04:42

What is ANOVA, its purpose, when to use it, and how to implement it in R?

ANOVA
04:54

What are the other commonly used statistical tests in R and how to implement them?

Statistical Tests
05:14

All knowledge is incomplete without being put to practice. We’ve got a good taste of the core concepts that govern statistical analysis with R. Let’s solve the challenges pertaining to data manipulation in this video.

Project 1 – Data Munging and Summarizing
11:31

What is data if not represented visually! We have solved challenges related to data manipulation. Now it’s time to tackle visualization in this video.

Project 2 – Visualization with Base Graphics
05:42

Practice solving exercises that involve making statistical inferences.

Project 3 – Statistical Inference
03:50

The aim of this video is to introduce the magrittr package, its significance, and features such as pipe operators.

Pipes with Magrittr
05:21

Understand and use the 7 data manipulation verbs.

The 7 Data Manipulation Verbs
05:19

How to group datasets by one or more variables using dplyr.

Aggregation and Special Functions
03:36

How to join two tables using the two table verbs of dplyr.

Two Table Verbs
02:42

How to work with databases with DplyR. 

Working With Databases
05:30

Understand the basics of data.table; do filter and select operations.

Understanding Basics, Filter, and Select
07:34

Understand the syntax; create and update columns in a data.table.

Understanding Syntax, Creating and Updating Columns
04:06

Learn how to aggregate data.tables. Also learn the .N and .I operators.

Aggregating Data, .N, and .I
04:20

Understand and implement chaining, keys, functions, and .SD. 

data.table
04:17

How to write for-loops with set, set keys, and join data.tables?

Fast Loops with set(), Keys, and Joins
09:12
+
Mastering R Programming
54 Lectures 05:12:00

This video gives an overview of the entire course.

Preview 07:44

In this video, we will take a look at how to perform univariate analysis.

Performing Univariate Analysis
05:22

The goal of this video is to perform bivariate analysis in R using three cases.

Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA
05:42

In this video, we will see how to detect and treat outliers.

Detecting and Treating Outlier
03:20

The goal of this video is to see how to treat missing values in R.

Treating Missing Values with `mice`
03:59

In this video we'll see what is linear regression, its purpose, when to use it, and how to implement in R.

Building Linear Regressors
07:35

We'll see how to interpret regression results and Interaction effects in this video

Interpreting Regression Results and Interactions Terms
05:19

In this video we will discuss what is residual analysis and detect multivariate outliers using Cook's Distance

Performing Residual Analysis and Extracting Extreme Observations With Cook's Dis
03:25

The goal of this video is to understand how to do model selection and comparison using best subsets, stepwise regression and ANOVA.

Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA
04:39

In this video we will see how to do k-fold cross validation in R.

Validating Model Performance on New Data with k-Fold Cross Validation
02:29

The goal of this video is check out how to build non-linear regression models using Splines and GAMs.

Building Non-Linear Regressors with Splines and GAMs
05:19

Our goal in this video would be to understand logistic regression, evaluation metrics of binary classification problems, and interpretation of the ROC curve.

Building Logistic Regressors, Evaluation Metrics, and ROC Curve
12:38

In this video, we will understand the concept and working of naïve Bayes classifier and how to implement the R code.

Understanding the Concept and Building Naive Bayes Classifier
09:23

In this video, we will look at what k-nearest neighbors algorithms, how does it works and how to implement it in T.

Building k-Nearest Neighbors Classifier
07:01

The goal of this video is to understand how decision trees work, what they are used for, and how to implement then.

Building Tree Based Models Using RPart, cTree, and C5.0
06:32

The goal of this video is know what the various features of the caret package are and how to build predictive models.

Building Predictive Models with the caret Package
08:11

The goal of this video is to know how to do feature selection before building predictive models.

Selecting Important Features with RFE, varImp, and Boruta
05:19

In this video, we will look at how support vector machines work.

Building Classifiers with Support Vector Machines
08:03

In this video, we will look at the concept behind bagging and random forests and how to implement it to solve problems.

Understanding Bagging and Building Random Forest Classifier
05:06

Let's understand what boosting is and how stochastic gradient boosting works with GBM.

Implementing Stochastic Gradient Boosting with GBM
05:18

In this video, we will look at what regularization is, ridge and lasso regression, and how to implement it.

Regularization with Ridge, Lasso, and Elasticnet
08:52

Let's look at how XG Boost works and how to implement it in this video.

Building Classifiers and Regressors with XGBoost
10:10

Our goal in this video would be to reduce the dimensionality of data with principal components, and understand the concept and how to implement it in R.

Dimensionality Reduction with Principal Component Analysis
05:04

In this video, we will understand the k-means clustering algorithm and implement it using the principal components.

Clustering with k-means and Principal Components
03:16

In this video, we will analyze the clustering tendency of a dataset and identify the ideal number of clusters or groups.

Determining Optimum Number of Clusters
05:24

The goal of this video is to understand the logic of hierarchical clustering, types, and how to implement it in R.

Understanding and Implementing Hierarchical Clustering
02:36

How to use affinity propagation to cluster data points? How is it different from conventional algorithms?

Clustering with Affinity Propagation
05:24

How to build recommendation engines to recommend products/movies to new and existing users?

Building Recommendation Engines
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The goal of this video is to understand what a time series is, how to create time series of various frequencies, and the enhanced facilities available in the xts package.

Understanding the Components of a Time Series, and the xts Package
05:41

The goal of this video is to understand the characteristics of a time series: stationarity and how to de-trend and de-seasonalize a time series.

Stationarity, De-Trend, and De-Seasonalize
04:07

In this video, we will introduce the characteristics of time series such as ACF, PACF, and CCF; why they matter; and how to interpret them.

Understanding the Significance of Lags, ACF, PACF, and CCF
03:49

Our goal in this video would be to understand moving average and exponential smoothing and use it to forecast.

Forecasting with Moving Average and Exponential Smoothing
02:25

In this video, we will understand how double exponential smoothing and holt winter forecasting works, when to use them, and how to implement them in R.

Forecasting with Double Exponential and Holt Winters
03:22

Let's look at what ARIMA forecasting is, understand the concepts, and learn how ARIMA modelling works in this video.

Forecasting with ARIMA Modelling
05:26

In this video, we'll take a look at how to scrape data from web pages and how to clean and process raw web and other textual data.

Scraping Web Pages and Processing Texts
09:24

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Corpus, TDM, TF-IDF, and Word Cloud
09:06

Let's see how to use cosine similarity and latent semantic analysis to find and map similar documents.

Cosine Similarity and Latent Semantic Analysis
07:20

In this video, we will see how to extract the underlying topics in a document, the keywords related to each topic and the proportion of topics in each document.

Extracting Topics with Latent Dirichlet Allocation
05:07

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Sentiment Scoring with tidytext and Syuzhet
04:23

How to classify texts with machine learning algorithms using the RTextTools package?

Classifying Texts with RTextTools
03:57

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Building a Basic ggplot2 and Customizing the Aesthetics and Themes
07:18

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Manipulating Legend, AddingText, and Annotation
03:31

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Drawing Multiple Plots with Faceting and Changing Layouts
03:18

How to make various types of plots in ggplot such as bar chart, time series, boxplot, ribbon chart,and so on.

Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots
05:25

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ggplot2 Extensions and ggplotly
03:11

We will discuss the best practices that should be followed to minimize code runtime in this video.

Implementing Best Practices to Speed Up R Code
05:46

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Implementing Parallel Computing with doParallel and foreach
04:22

The goal of this video is understand how to work with DplyR and pipes.

Writing Readable and Fast R Code with Pipes and DPlyR
05:39

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Writing Super Fast R Code with Minimal Keystrokes Using Data.Table
06:38

Our main focus in this video is to understand how to write C++ code and make it work in R. Also leverage the speed of C++ in R, interface Rcpp with R, and write Rcpp code.

Interface C++ in R with RCpp
11:09

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Understanding the Structure of an R Package
05:02

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Build, Document, and Host an R Package on GitHub
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Performing Important Checks Before Submitting to CRAN
04:05

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Submitting an R Package to CRAN
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About the Instructor
Packt Publishing
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Tech Knowledge in Motion

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.