Learning Path- R Programming

Learning Path: R Programming, available at $19.99, has an average rating of 4.1, with 103 lectures, based on 17 reviews, and has 160 subscribers.

You will learn about 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 This course is ideal for individuals who are 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. or 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. It is particularly useful for 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. or 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.

Enroll now: Learning Path: R Programming

Summary

Title: Learning Path: R Programming

Price: $19.99

Average Rating: 4.1

Number of Lectures: 103

Number of Published Lectures: 103

Number of Curriculum Items: 103

Number of Published Curriculum Objects: 103

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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.

Course Curriculum

Chapter 1: Introduction to R Programming

Lecture 1: The Course Overview

Lecture 2: Installing R

Lecture 3: Installing RStudio

Lecture 4: Installing Packages

Lecture 5: Data Types and Data Structures

Lecture 6: Vectors

Lecture 7: Random Numbers, Rounding, and Binning

Lecture 8: Missing Values

Lecture 9: The which() Operator

Lecture 10: Lists

Lecture 11: Set Operations

Lecture 12: Sampling and Sorting

Lecture 13: Check Conditions

Lecture 14: For Loops

Lecture 15: Dataframes

Lecture 16: Importing and Exporting Data

Lecture 17: Matrices and Frequency Tables

Lecture 18: Merging Dataframes

Lecture 19: Aggregation

Lecture 20: Melting and Cross Tabulations with dcast()

Lecture 21: Dates

Lecture 22: String Manipulation

Lecture 23: Functions

Lecture 24: Debugging and Error Handling

Lecture 25: Fast Loops with apply()

Lecture 26: Fast Loops with sapply(), lapply() and vapply()

Lecture 27: Creating and Customizing an R Plot

Lecture 28: Drawing Plots with 2 Y Axes

Lecture 29: Multiplots and Custom Layouts

Lecture 30: Creating Basic Graph Types

Lecture 31: Univariate Analysis

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

Lecture 33: Correlation and Covariance

Lecture 34: Chi-sq Statistic

Lecture 35: ANOVA

Lecture 36: Statistical Tests

Lecture 37: Project 1 – Data Munging and Summarizing

Lecture 38: Project 2 – Visualization with Base Graphics

Lecture 39: Project 3 – Statistical Inference

Lecture 40: Pipes with Magrittr

Lecture 41: The 7 Data Manipulation Verbs

Lecture 42: Aggregation and Special Functions

Lecture 43: Two Table Verbs

Lecture 44: Working With Databases

Lecture 45: Understanding Basics, Filter, and Select

Lecture 46: Understanding Syntax, Creating and Updating Columns

Lecture 47: Aggregating Data, .N, and .I

Lecture 48: data.table

Lecture 49: Fast Loops with set(), Keys, and Joins

Chapter 2: Mastering R Programming

Lecture 1: The Course Overview

Lecture 2: Performing Univariate Analysis

Lecture 3: Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA

Lecture 4: Detecting and Treating Outlier

Lecture 5: Treating Missing Values with `mice`

Lecture 6: Building Linear Regressors

Lecture 7: Interpreting Regression Results and Interactions Terms

Lecture 8: Performing Residual Analysis and Extracting Extreme Observations With Cooks Dis

Lecture 9: Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA

Lecture 10: Validating Model Performance on New Data with k-Fold Cross Validation

Lecture 11: Building Non-Linear Regressors with Splines and GAMs

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

Lecture 13: Understanding the Concept and Building Naive Bayes Classifier

Lecture 14: Building k-Nearest Neighbors Classifier

Lecture 15: Building Tree Based Models Using RPart, cTree, and C5.0

Lecture 16: Building Predictive Models with the caret Package

Lecture 17: Selecting Important Features with RFE, varImp, and Boruta

Lecture 18: Building Classifiers with Support Vector Machines

Lecture 19: Understanding Bagging and Building Random Forest Classifier

Lecture 20: Implementing Stochastic Gradient Boosting with GBM

Lecture 21: Regularization with Ridge, Lasso, and Elasticnet

Lecture 22: Building Classifiers and Regressors with XGBoost

Lecture 23: Dimensionality Reduction with Principal Component Analysis

Lecture 24: Clustering with k-means and Principal Components

Lecture 25: Determining Optimum Number of Clusters

Lecture 26: Understanding and Implementing Hierarchical Clustering

Lecture 27: Clustering with Affinity Propagation

Lecture 28: Building Recommendation Engines

Lecture 29: Understanding the Components of a Time Series, and the xts Package

Lecture 30: Stationarity, De-Trend, and De-Seasonalize

Lecture 31: Understanding the Significance of Lags, ACF, PACF, and CCF

Lecture 32: Forecasting with Moving Average and Exponential Smoothing

Lecture 33: Forecasting with Double Exponential and Holt Winters

Lecture 34: Forecasting with ARIMA Modelling

Lecture 35: Scraping Web Pages and Processing Texts

Lecture 36: Corpus, TDM, TF-IDF, and Word Cloud

Lecture 37: Cosine Similarity and Latent Semantic Analysis

Lecture 38: Extracting Topics with Latent Dirichlet Allocation

Lecture 39: Sentiment Scoring with tidytext and Syuzhet

Lecture 40: Classifying Texts with RTextTools

Lecture 41: Building a Basic ggplot2 and Customizing the Aesthetics and Themes

Lecture 42: Manipulating Legend, AddingText, and Annotation

Lecture 43: Drawing Multiple Plots with Faceting and Changing Layouts

Lecture 44: Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots

Lecture 45: ggplot2 Extensions and ggplotly

Lecture 46: Implementing Best Practices to Speed Up R Code

Lecture 47: Implementing Parallel Computing with doParallel and foreach

Lecture 48: Writing Readable and Fast R Code with Pipes and DPlyR

Lecture 49: Writing Super Fast R Code with Minimal Keystrokes Using Data.Table

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

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2 stars: 1 votes

3 stars: 4 votes

4 stars: 8 votes

5 stars: 4 votes

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