Analyze Data Science Concepts Using R
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Analyze Data Science Concepts Using R
This course is part of Analyze and Apply R for Data Analytics Specialization
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What you'll learn
Analyze and visualize data using R to identify patterns and insights.
Apply statistical methods and machine learning techniques to real-world datasets.
Build and interpret predictive models using end-to-end data science workflows in R.
Skills you'll gain
- Data Transformation
- Regression Analysis
- Data Manipulation
- Data Wrangling
- Data-Driven Decision-Making
- Data Analysis
- Statistical Analysis
- Applied Machine Learning
- Statistical Programming
- Machine Learning
- Data Science
- Machine Learning Methods
- Statistical Modeling
- Probability & Statistics
- Data Preprocessing
- Data Visualization Software
Tools you'll learn
Details to know
February 2026
27 assignments
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There are 7 modules in this course
By completing this course, learners will be able to analyze data using R, apply statistical and machine learning techniques, and interpret complex datasets through effective visualizations. Learners will evaluate data patterns, construct statistical models, and apply machine learning workflows to solve real-world problems using R.
This course provides a comprehensive, end-to-end introduction to Data Science with R, covering data visualization, statistical analysis, probability, regression models, decision trees, and machine learning. Learners progress from foundational concepts to advanced techniques, gaining practical experience in exploring data, building models, and drawing actionable insights. The course emphasizes hands-on learning through structured modules, real datasets, and applied case studies, ensuring learners not only understand concepts but can implement them confidently. What makes this course unique is its balanced integration of visualization, statistics, and machine learning within a single R-based workflow. Unlike fragmented learning paths, this course connects analytical thinking with practical implementation, helping learners understand why methods are used, not just how. Designed for aspiring data analysts, statisticians, and data science professionals, the course builds industry-relevant skills that can be directly applied in academic, research, and business environments.
This module introduces the fundamental concepts of data science and establishes R as a core tool for statistical computing and data visualization. Learners gain an understanding of the data science ecosystem, the role of R in analytical workflows, and the importance of visualization for interpreting data-driven insights.
What's included
6 videos4 assignments
6 videos•Total 27 minutes
- Introduction to Data Science with R•3 minutes
- Understanding Datascience and its Modules•4 minutes
- R Project for Statistical Computing•10 minutes
- Purpose of using R Tool•2 minutes
- Module on Data Visualization•2 minutes
- Creating Pie Charts•6 minutes
4 assignments•Total 60 minutes
- Graded - Foundations of Data Science with R•30 minutes
- Introduction to Data Science and R•10 minutes
- R for Statistical Computing•10 minutes
- Getting Started with Data Visualization•10 minutes
This module focuses on essential visualization techniques used to explore data distributions, relationships, and trends. Learners build foundational skills in selecting and applying charts that effectively represent categorical, numerical, and time-based data.
What's included
8 videos4 assignments
8 videos•Total 42 minutes
- Creating Bar Charts•7 minutes
- Functions of Histogram•5 minutes
- Method of Using Scatterplots•5 minutes
- Creating Data for Line Charts•5 minutes
- Case Study for Vector Values•6 minutes
- Module on Advanced Data Visualization•4 minutes
- with Functions for Plotting Values•6 minutes
- How to Plot Car Value•4 minutes
4 assignments•Total 60 minutes
- Graded - Core Data Visualization Techniques•30 minutes
- Visualizing Distributions and Relationships•10 minutes
- Trend-Based Visualizations•10 minutes
- Advanced Visualization Concepts•10 minutes
This module introduces advanced visualization using the ggplot framework in R. Learners explore layered graphics, aesthetic mappings, and enhanced plots to communicate multivariate data insights effectively.
What's included
9 videos4 assignments
9 videos•Total 57 minutes
- Understanding the ggplot Value•4 minutes
- Basic Example on Scatterplot•7 minutes
- Scatterplot With Encircling•8 minutes
- Learning the Jitter Plot•5 minutes
- Counts Charts in ggplot•4 minutes
- Section on Bubble Chart•6 minutes
- Diverging Bars with ggplot•11 minutes
- Diverging Lollipchart with ggplot•6 minutes
- Implementation of Dot Plot•6 minutes
4 assignments•Total 60 minutes
- Graded - Advanced Visualization with ggplot•30 minutes
- ggplot Fundamentals•10 minutes
- Enhancing Scatter and Count Plots•10 minutes
- Comparative and Dot-Based Charts•10 minutes
This module covers specialized visualization methods for hierarchical, demographic, and time-based data. Learners develop skills to represent structured relationships, changes, and seasonal patterns using appropriate visual tools.
What's included
7 videos4 assignments
7 videos•Total 45 minutes
- Purpose of using Area Charts•9 minutes
- Ordered Bar Chart for Multiple Items•8 minutes
- Simple Demonstration on Pie Chart•7 minutes
- Example on Hierarchical Dendrogram•4 minutes
- Learning about the Population Pyramids•9 minutes
- Understanding the Change Plot•3 minutes
- Case Study on Seasonal Plot•5 minutes
4 assignments•Total 60 minutes
- Graded - Specialized Visualization Techniques•30 minutes
- Area and Ordered Visualizations•10 minutes
- Hierarchical and Circular Charts•10 minutes
- Temporal and Change-Based Plots•10 minutes
This module builds statistical foundations required for data analysis, including descriptive statistics, probability distributions, and regression modeling. Learners apply statistical techniques to analyze relationships, trends, and variability in data.
What's included
11 videos4 assignments
11 videos•Total 87 minutes
- Basic Understanding on Statistics•3 minutes
- Implementation of Mean Median and Mode•9 minutes
- Understanding the Linear Regression•10 minutes
- Understanding Multiple Regression•9 minutes
- Functions of Logistic Regression•8 minutes
- Learning Normal Distribution Curve•9 minutes
- Understanding the Binomial Distribution•6 minutes
- Involvement of Poisson Regression•6 minutes
- Analysis of Covariance•9 minutes
- Time Series Analysis•11 minutes
- Nonlinear Least Square•9 minutes
4 assignments•Total 60 minutes
- Graded - Statistical Analysis and Regression Models•30 minutes
- Descriptive Statistics and Regression Basics•10 minutes
- Probability Distributions and Regression Extensions•10 minutes
- Advanced Statistical Methods•10 minutes
This module explores decision-based models, probability theory, and essential data preparation techniques. Learners develop analytical skills for hypothesis testing, simulation, and preparing datasets for modeling.
What's included
11 videos4 assignments
11 videos•Total 58 minutes
- Section on Decision Tree•7 minutes
- The Random Forest Approach•6 minutes
- Learning the Chi Square Test•5 minutes
- Case Study on Survival Analysis•7 minutes
- Understanding the Concept of Probability•4 minutes
- Counting the Number of Combinations•4 minutes
- Generating Random Numbers•7 minutes
- Generating Random Sequences•3 minutes
- Converting Probabilities to Quantiles•4 minutes
- Criteria for Plotting a Density Function•6 minutes
- Concept of Data Manipulation•4 minutes
4 assignments•Total 60 minutes
- Graded - Decision Models, Probability, and Data Manipulation•30 minutes
- Tree-Based Models and Hypothesis Testing•10 minutes
- Probability and Randomness•10 minutes
- Probability Functions and Data Preparation•10 minutes
This module introduces machine learning concepts and demonstrates their application using R. Learners work with datasets, implement machine learning workflows, and apply models to real-world problems.
What's included
4 videos3 assignments
4 videos•Total 28 minutes
- Module on Machine Learning•8 minutes
- Machine Learning Concepts with R•6 minutes
- Machine Learning Datasets•7 minutes
- Machine learning project with R•8 minutes
3 assignments•Total 50 minutes
- Graded -Machine Learning with R•30 minutes
- Machine Learning Foundations•10 minutes
- Applied Machine Learning in R•10 minutes
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