DS-R.AJ1
R for Data Science
Start your data science journey with the R programming language. Learn how to model, structure, visualize, and transform data.
- Practice in 38 Hands-On Labs — nothing to install
- 13 Interactive Lessons and 47 topics mapped to the official exam objectives
- 175 Practice Test Questions
Intermediate Self-paced · 1 year access 4.8/5 (11 Reviews)
38 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
13Interactive Lessons
47Topics
38LiveLab
175Practice Test Questions
113Flashcards
113Glossary of terms
01 / Skills you'll get
What you will be able to do
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.
- 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 version control and collaborative projects
- Creating reproducible reports with R markdown
Course Highlights
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13 Structured Lessons Comprehensive coverage of core course objectives
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38 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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175 Practice Questions Assessment tests with detailed answer rationales
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
13 Interactive Lessons · 47 topics01 Preface 4 topics · 1 LiveLab +
- What this course covers?
- What you need for this course?
- Who this course is for?
- Conventions
1 LiveLab in this lesson — see the labs panel →
02 Data Mining Patterns 5 topics · 8 LiveLab +
- Cluster analysis
- Anomaly detection
- Association rules
- Questions
- Summary
8 LiveLab in this lesson — see the labs panel →
03 Data Mining Sequences 3 topics · 5 LiveLab +
- Patterns
- Questions
- Summary
5 LiveLab in this lesson — see the labs panel →
04 Text Mining 3 topics · 2 LiveLab +
- Packages
- Questions
- Summary
2 LiveLab in this lesson — see the labs panel →
05 Data Analysis – Regression Analysis 3 topics · 3 LiveLab +
- Packages
- Questions
- Summary
3 LiveLab in this lesson — see the labs panel →
06 Data Analysis – Correlation 3 topics · 1 LiveLab +
- Packages
- Questions
- Summary
1 LiveLab in this lesson — see the labs panel →
07 Data Analysis – Clustering 4 topics · 2 LiveLab +
- Packages
- K-means clustering
- Questions
- Summary
2 LiveLab in this lesson — see the labs panel →
08 Data Visualization – R Graphics 3 topics · 2 LiveLab +
- Packages
- Questions
- Summary
2 LiveLab in this lesson — see the labs panel →
09 Data Visualization – Plotting 5 topics · 4 LiveLab +
- Packages
- Scatter plots
- Bar charts and plots
- Questions
- Summary
4 LiveLab in this lesson — see the labs panel →
10 Data Visualization – 3D 4 topics · 2 LiveLab +
- Packages
- Generating 3D graphics
- Questions
- Summary
2 LiveLab in this lesson — see the labs panel →
11 Machine Learning in Action 4 topics · 2 LiveLab +
- Packages
- Dataset
- Questions
- Summary
2 LiveLab in this lesson — see the labs panel →
12 Predicting Events with Machine Learning 3 topics · 1 LiveLab +
- Automatic forecasting packages
- Questions
- Summary
1 LiveLab in this lesson — see the labs panel →
13 Supervised and Unsupervised Learning 3 topics · 5 LiveLab +
- Packages
- Questions
- Summary
5 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
38 LiveLabs- R Studio Sandbox
- 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
- 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
- Manipulating Text
- Analyzing the XML Text
- Performing Simple Regression
- Performing Multiple Regression
- Performing Multivariate Regression Analysis
- Performing Tetrachoric Correlation
- Estimating the Number of Clusters Using Medoids
- Performing Affinity Propagation Clustering
- Grouping and Organizing Bivariate Data
- Plotting Points on a Map
- Displaying a Histogram of Scatter Plots
- Creating an Enhanced Scatter Plot
- Constructing a Bar Plot
- Producing a Word Cloud
- Generating a 3D Graphic
- Producing a 3D Scatterplot
- Finding a Dataset
- Making a Prediction
- Using Holt Exponential Smoothing
- Developing a Decision Tree
- Producing a Regression Model
- Understanding Instance-Based Learning
- Performing Cluster Analysis
- Constructing a Multitude of Decision Trees
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
What is the benefit of using the R programming language for data analysis?+
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.
What are the prerequisites for this Data Science course?+
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 or Python, which programming language is recommended for data science?+
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.
Is R for Data Science an easy or difficult study?+
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.
How will I get to practice the concepts while learning R for Data Science?+
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.
Is this course relevant for me?+
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.
Will I get a certificate of completion?+
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.
Keep going
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- 1 year of full access
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