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R for Data Science

Start your data science journey with the R programming language. Learn how to model, structure, visualize, and transform data.

(DS-R.AJ1) / ISBN : 978-1-64459-310-3
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About This Course

R for Data Science is a comprehensive course that leverages the popular R-syntax for mastering the techniques of data exploration, manipulation and visualization. You’ll learn the basics for using R vectors for creating lists, matrices, arrays, and data frames. Next, you’ll learn how to deploy conditional statements, functions, classes, and debugging. You’ll discover ways to read and write with R for creating transformative visualizations using ggplot2. By the end of this course, you’ll gain the confidence to tackle complex data challenges and write your own R scripts.

Skills You’ll Get

  • Importing data using readr, heaven and dbplyr packages
  • Cleaning data using features like na.rm, filter(), and mutate () 
  • Reshaping and summarizing data with group-by()
  • Utilizing tidyverse suite for ‘tidy data’ 
  • Using R’s built-in features for statistical analysis
  • Ability to use the ggplot2 package for visualization and customisation
  • Exploring Git for vision control and collaborative projects
  • Creating reproducible reports with R markdown

Download Course Outline

1

Preface

  • What this course covers?
  • What you need for this course?
  • Who this course is for?
  • Conventions
2

Data Mining Patterns

  • Cluster analysis
  • Anomaly detection
  • Association rules
  • Questions
  • Summary
3

Data Mining Sequences

  • Patterns
  • Questions
  • Summary
4

Text Mining

  • Packages
  • Questions
  • Summary
5

Data Analysis – Regression Analysis

  • Packages
  • Questions
  • Summary
6

Data Analysis – Correlation

  • Packages
  • Questions
  • Summary
7

Data Analysis – Clustering

  • Packages
  • K-means clustering
  • Questions
  • Summary
8

Data Visualization – R Graphics

  • Packages
  • Questions
  • Summary
9

Data Visualization – Plotting

  • Packages
  • Scatter plots
  • Bar charts and plots
  • Questions
  • Summary
10

Data Visualization – 3D

  • Packages
  • Generating 3D graphics
  • Questions
  • Summary
11

Machine Learning in Action

  • Packages
  • Dataset
  • Questions
  • Summary
12

Predicting Events with Machine Learning

  • Automatic forecasting packages
  • Questions
  • Summary
13

Supervised and Unsupervised Learning

  • Packages
  • Questions
  • Summary

1

Preface

  • R Studio Sandbox
2

Data Mining Patterns

  • Plotting a Graph by Performing k-means Clustering
  • Calculating K-medoids Clustering
  • Displaying the Hierarchical Cluster
  • Plotting Graphs By Performing Expectation-Maximization
  • Plotting the Density Values
  • Computing the Outliers for a Set
  • Calculating Anomalies
  • Using the apriori Rules Library
3

Data Mining Sequences

  • Using eclat to Find Similarities in Adult Behavior
  • Finding Frequent Items in a Dataset
  • Evaluating Associations in a Shopping Basket
  • Determining and Visualizing Sequences
  • Computing LCP, LCS, and OMD
4

Text Mining

  • Manipulating Text
  • Analyzing the XML Text
5

Data Analysis – Regression Analysis

  • Performing Simple Regression
  • Performing Multiple Regression
  • Performing Multivariate Regression Analysis
6

Data Analysis – Correlation

  • Performing Tetrachoric Correlation
7

Data Analysis – Clustering

  • Estimating the Number of Clusters Using Medoids
  • Performing Affinity Propagation Clustering
8

Data Visualization – R Graphics

  • Grouping and Organizing Bivariate Data
  • Plotting Points on a Map
9

Data Visualization – Plotting

  • Displaying a Histogram of Scatter Plots
  • Creating an Enhanced Scatter Plot
  • Constructing a Bar Plot
  • Producing a Word Cloud
10

Data Visualization – 3D

  • Generating a 3D Graphic
  • Producing a 3D Scatterplot
11

Machine Learning in Action

  • Finding a Dataset
  • Making a Prediction
12

Predicting Events with Machine Learning

  • Using Holt Exponential Smoothing
13

Supervised and Unsupervised Learning

  • Developing a Decision Tree
  • Producing a Regression Model
  • Understanding Instance-Based Learning
  • Performing Cluster Analysis
  • Constructing a Multitude of Decision Trees

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R is a great choice for Data Science especially when you are using it for statistics and in-depth analysis. It equips you with the knowledge of using powerful features like the ggplot2 package and boasts of a vast library for hypothesis, testing and modeling.

Deep understanding of ML concepts and statistics; proficient with advanced level algebra, knowledge of database management and experience with Python or R programming language.

R and Python both are relevant for data science with their own set of advantages. R has a rich library ideal for in-depth analysis and data visualization  whereas Python stands out for its easier syntax (closer to English language) and scikit-learn library ideal for versatility and machine learning.

This depends on your background. If you have prior coding experience and you are good with statistics, you’ll find it more manageable. However, R’s unique syntax can be a little bit challenging for those without any coding experience. This is where uCertify can aid your progress with hands-on learning and practice exercises that’ll make it easier to grasp the core concepts.

You’ll get hands-on experience as this course is majorly focused on practical learning. At uCertify, we facilitate your learning experience with our 49+ interactive features where you’ll be doing a lot of exercises and projects to solidify your understanding of the core concepts.

If you want to excel in the field of data science and make an impact with your statistical and analytical capabilities, this is the course for you.

Yes, after completing the course successfully, you’ll get a certificate of achievement to showcase that you are well versed with R for Data science.

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