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R Language Courses

    Learn R programming for statistical analysis and visualization

    Build skills in data manipulation and exploratory analysis

    Work on real datasets using R packages and workflows

    Train on analytics, modeling basics, and reporting with R

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Learn From The Best

8

Instructors

10

Industry Experts

Slide 1 of 8
Debabrata Das, Instructor

Debabrata Das

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Director, IIITB

Dr. Debabrata Das is Director of IIITB. He has received his PhD from IIT-KGP. His main areas of research are IoT and Wireless Access Network 

IIITB Profile
Prof. Dhiya Al-Jumeily, Instructor

Prof. Dhiya Al-Jumeily

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Professor - Artificial Intelligence

A senior member of the IEEE and a chartered IT Professional, he is a fellow of the UK Higher Education ACademy 

LJMU Profile
Prof. G. Srinivasaraghavan, Instructor

Prof. G. Srinivasaraghavan

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Professor

Prof. Srinivasaraghavan has a PhD in Computer Science from IIT-K and 18 years of experience with Infosys and several other MNCs. 

IIITB Profile
Dr. Atif Waraich, Instructor

Dr. Atif Waraich

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Faculty - Computer Science

A Senior faculty of Engineering and Technology at LJMU who has multiple publications in the health care domain 

LJMU Profile
Chandrashekar Ramanathan, Instructor

Chandrashekar Ramanathan

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Dean Academics

Prof. Chandrashekar has a PhD from Mississippi State University and experience of over 10 years in several multinational organisations. 

IIITB Profile
Prof. Paulo Lisboa, Instructor

Prof. Paulo Lisboa

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Head of Dept - Applied Mathematics

Studied mathematical physics at LU and was the chairman of Industrial Mathematics at KMJU in 1996 and Head of Graduate School in 2002 

LJMU Profile
Dr. Gabriela Czanner, Instructor

Dr. Gabriela Czanner

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Faculty - Engineering and Technology

A Senior lecturer at Department of Applied Mathematics at LJMU. Her research focus is advanced statistics for decision support 

LJMU Profile
Tricha Anjali, Instructor

Tricha Anjali

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Ex-Associate Dean

Prof. Anjali has a PhD from Georgia Institute of Technology as well as an integrated MTech (EE) from IIT Bombay. 

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Learn by Doing

Our data science online certification programs have projects to apply theoretical knowledge in practical scenarios, helps you to tackle real-world problems.

16+

Industry projects to choose from

Slide 1 of 17

In this assignment, you will work for a consumer finance company which specialises in lending various types of loans to urban customers and use EDA to analyse the patterns present in the data. This will ensure that the applicants are capable of repaying the loan are not rejected.

Data Visualisation

Data Analysis

Data Interpretation

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Credit EDA Assignment

In this assignment, you will work for a consumer finance company which specialises in lending various types of loans to urban customers and use EDA to… Know More

Skills learned

Data Cleaning

Data Visualisation

Data Analysis

Data Interpretation

MySQL Queries

Data Manipulation

Data Analysis

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RSVP Case Study

In this assignment, you will work on a movies dataset using SQL to extract exciting insights about popular films and the factors that drive a film. 

Skills learned

MySQL

MySQL Queries

Data Manipulation

Data Analysis

ML Modelling

Model Evaluation

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Bike Sharing Assignment

Build a regression model to understand the factors on which the demand for bike sharing systems vary on and help a company optimise its revenue. 

Skills learned

Linear Regression

ML Modelling

Model Evaluation

In this case study, the company requires you to build a machine learning classification model wherein using some demographics and behavioral data of the leads (potential buyers), it will be able to identify the ones most likely to convert.

Decision Trees

Classification

ML Modelling

Model Evaluation

Business Problem Solving

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Lead Scoring Case Study

In this case study, the company requires you to build a machine learning classification model wherein using some demographics and behavioral data of t… Know More

Skills learned

Logistic Regression

Decision Trees

Classification

ML Modelling

Model Evaluation

Business Problem Solving

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customers that are likely to churn and make data-driven strategies to retain them from the perspective of a business analyst.

Tree Models

Model Selection

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

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Model Selection Case Study - Telecom Churn

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customer… Know More

Skills learned

Logistic Regression

Tree Models

Model Selection

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customers that are likely to churn and make data-driven strategies to retain them from the perspective of a data scientist.

Tree Models

Boosting

Model Selection

Regularization

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

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Advanced ML Case Study - Telecom Churn

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customer… Know More

Skills learned

Logistic Regression

Tree Models

Boosting

Model Selection

Regularization

Feature Engineering

Classification

ML Modelling

Model Evaluation

Business Problem Solving

Lexical Processing

Regex

POS Tagging

Dependency Parsing

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Syntactic Processing Assignment

Use the techniques such as POS tagging and Dependency parsing to extract information from unstructured text data. 

Skills learned

Natural Language Processing

Lexical Processing

Regex

POS Tagging

Dependency Parsing

Sqoop

Redshift

Spark

ETL Pipeline

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ETL Project

Make use of Sqoop, Redshift & Spark to design an ETL data pipeline. 

Skills learned

AWS

Sqoop

Redshift

Spark

ETL Pipeline

Analyse movie data from the past 100 years and find out various insights to determine what makes a movie do well. Use data manipulation, slicing, and various other dataframe operations to successfully find usable insights from the movies.

Data Processing

Data Visualisation

Data Analysis

Dashboarding

Data Storytelling

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IPL Visualization Assignment

Analyse movie data from the past 100 years and find out various insights to determine what makes a movie do well. Use data manipulation, slicing, and … Know More

Skills learned

Tableau/Power BI

Data Processing

Data Visualisation

Data Analysis

Dashboarding

Data Storytelling

Linear Regression

ML Model Evaluation

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Advanced Regression

Build a regularized regression model to understand the most important variables to predict 

Skills learned

ML Modeling

Linear Regression

ML Model Evaluation

Hadoop

MapReduce Programming

mrjob

Linux

Sqoop

Apache HBase

SQL

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MapReduce Programming Assignment

Perform MapReduce Programming in a big data environment on a large dataset. 

Skills learned

AWS

Hadoop

MapReduce Programming

mrjob

Linux

Sqoop

Apache HBase

SQL

Big Data with Spark

PySpark

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Big Data Case Study

Understand how a big data project in the industry is taken up and solved through a comprehensive big data case study on cloud. 

Skills learned

AWS

Big Data with Spark

PySpark

Python

Data Analysis

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SQL & Python Lab

Solve intensive programming questions in a competitive lab environment to showcase and evaluate your SQL and Python coding skills. 

Skills learned

SQL

Python

Data Analysis

Understand the factors that drive the Airbnb business and help the team leads tackle problems and bottlenecks through extensive data analysis on UI tools.

Data Storytelling techniques

Data Visualisation

Tableau

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Airbnb Case Study

Understand the factors that drive the Airbnb business and help the team leads tackle problems and bottlenecks through extensive data analysis on UI to… Know More

Skills learned

Data Analysis

Data Storytelling techniques

Data Visualisation

Tableau

Spark

Spark Streaming

Apache Kafka

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Retail Project

Build an end-to-end real-time data processing application using Spark Streaming and Kafka. 

Skills learned

AWS

Spark

Spark Streaming

Apache Kafka

Domain understanding

Data Analysis

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Business Case Study

Understand how data projects are taken up in the industry and implement the steps required to successfully manage and complete a data project. 

Skills learned

Business Problem Solving

Domain understanding

Data Analysis

In this assignment, you will build a complete neural network using Numpy. You will implement all the steps required to build a network - feedforward, loss computation, backpropagation, weight updates etc. You will use the MNIST dataset to train your model to classify handwritten digits between 0-9.

Neural Networks

PyTorch

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NN Assignment

In this assignment, you will build a complete neural network using Numpy. You will implement all the steps required to build a network - feedforward, … Know More

Skills learned

Deep Learning

Neural Networks

PyTorch

Our Placement Numbers

Excel in data-driven careers with our certification in Data Science, boasting a high rate of successful student placements. Get hired by top companies.

57%
Average Salary Hike
₹1.23 Cr
Highest Salary Offered
6000+
Career Transitions
300+
Hiring Partners

Top Recruiters

Tech Mahindra
Amazon
Tata
Wipro
Microsoft
TCS
Infosys
Flipkart
IBM
HDFC Bank
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What Our Learners Have To Say

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upGrad's student mentor played a very importany role in my learning journey

upGrad's student mentors played a crucial part in my learning journey. I followed them and they helped a lot in clarifying my queries related to my online data science course. They also gave me placement assistance as a part of my data science course, where they perfectly guide you on how to start a career as a Data Analyst.

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Abhinay Bandaru

Product Analyst, Head Infotech

3 Years of Experience

The upGrad's placement drive helped me to secure a job in MNC

My online data science course's content was so well-structured that it helped me grasp concepts that were entirely new to me. Thanks to upGrad's placement drive, I was able to secure a job at Kantar Analytics.

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Bhagvathi

Data Analyst, Kantar

1 Year of Experience

Additional benifits of upGrad helped a lot in terms of gaining practical exposure

upGrad's online data science courses are truly amazing. The mentorship through industry veterans, BaseCamps, and student mentors makes the programme extremely engaging. I Would definitely endorse the data science programme for its rich content and comprehensive approach to Data Science.

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Moulik Srivastava

Technology Project Manager & Commercial Strategy Associate, Victory Farms

5 Years of Experience

Being online, it was like a university experience

Learning with upGrad for my data science course was like going back to University for me. Their data science certification, career support and mentorship calls really helped me switch to a career in the field of Data Science.

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Atul Agarwal

Data Scientist, Scaler

7 Years of Experience

Structured and easy curriculumn

The curriculum of the data science online course is very structured and easy to consume. The mock calls and encouraging words from the CEO before my interview were extremely encouraging. One of the best online data science course there is in the market

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Aishwarya Ramachandran

News Analyst, Thomson Reuters

6 Years of Experience

How Will upGrad Supports You

Industry Mentors

    Receive unparalleled guidance from industry mentors, teaching assistants, and graders

    Receive one-on-one feedback from our seasoned data science faculty on submissions and personalized feedback to improvement

    Our Data Science Syllabus is designed to provide you with ample of industry relevant knowledge with examples

R Language Courses Overview

R Language courses are designed to help learners analyze, visualize, and interpret data using a programming language built specifically for statistics and data science. In 2026, R continues to be widely used in research, analytics, finance, healthcare, and academia due to its strong statistical capabilities and visualization ecosystem.

Learning R enables you to work with complex datasets, apply statistical models, and communicate insights effectively through charts and reports. Courses usually progress from basic syntax to data manipulation, visualization, and applied statistical analysis.

Eligibility Criteria for R Language Courses

Most R language online courses are beginner-friendly, especially for learners focused on analytics and statistics.

Educational background

  • Bachelor’s degree from a recognized institution (any stream).
  • Suitable for learners from science, commerce, arts, and engineering backgrounds.
  • Prior exposure to statistics is helpful but not mandatory.
  • Final-year students and working professionals are eligible.

Basic skills

  • Comfort with numbers, charts, and basic data interpretation.
  • Willingness to learn programming logic gradually.
  • Curiosity about data patterns and trends.
  • Readiness to practice coding and analysis regularly.

Who can apply

  • Students aiming for analytics or research roles.
  • Working professionals handling data-driven decisions.
  • Analysts, statisticians, and researchers.
  • Professionals in healthcare, finance, and social sciences.
  • Learners transitioning into data-focused careers.

Is an R Language Course the Right Fit for You?

R is well suited for individuals who want to combine statistics with programming.

You should consider R language courses if you:

  • Enjoy working with data, charts, and statistical summaries.
  • Plan to work in analytics, research, or data science roles.
  • Want strong data visualization and statistical modeling skills.
  • Prefer analysis-oriented programming rather than application development.

Because it helps you:

  • Perform advanced statistical analysis.
  • Create publication-quality visualizations.
  • Work with large and complex datasets.
  • Support research and business decisions with evidence.

R is especially valuable for roles where analysis depth matters more than software engineering.

R Language Course Fees and Duration

Fees and duration depend on course depth, applied coverage, and project work.

Quick overview

Type of R Language Course

Average Fees (INR)

Duration

R Fundamentals / Bootcamps

50,000 – 1,00,000

3–6 months

Intermediate R Analytics Programs

1,20,000 – 1,90,000

6–12 months

Advanced Data Science with R Programs

2,50,000 – 5,50,000

12–18 months

Short-Term R Courses (4–8 weeks)

  • Focus on syntax, data handling, and visualization basics.
  • Suitable for beginners or analysts upskilling quickly.

Intermediate Programs (3–6 months)

  • Cover packages, data manipulation, and applied statistics.
  • Ideal for analytics and research roles.

Advanced or Long-Term Programs (6–12 months)

  • Include modeling, machine learning basics, and projects.
  • Suitable for data science and research-focused careers.

What Skills You Gain from R Language Courses

R language courses emphasize analytical depth and data clarity.

1. R programming fundamentals

  • R syntax and data types.
  • Working with vectors, lists, and data frames.
  • Writing reusable functions.
  • Understanding R workflows.

2. Data manipulation

  • Cleaning and transforming datasets.
  • Filtering, aggregating, and reshaping data.
  • Handling missing values.
  • Preparing data for analysis.

3. Data visualization

  • Creating charts and plots.
  • Visualizing distributions and trends.
  • Customizing graphs for insights.
  • Communicating findings visually.

4. Statistical analysis

  • Descriptive and inferential statistics.
  • Hypothesis testing.
  • Correlation and regression concepts.
  • Interpreting analytical outputs.

5. Applied use cases

  • Business and research data analysis.
  • Exploratory data analysis workflows.
  • Report-ready insights.
  • Analytical storytelling.

6. Projects and practice

  • Hands-on assignments using real datasets.
  • End-to-end analytical exercises.
  • Interpreting results clearly.
  • Improving analytical confidence.

How to Pick the Right R Language Course

A good R language course balances programming with statistical understanding.

1. Curriculum balance

  • Covers both coding and statistical reasoning.
  • Explains why methods are used, not just how.
  • Progresses logically from basics to application.
  • Avoids tool-only teaching.

2. Practical exposure

  • Frequent data analysis exercises.
  • Real-world datasets.
  • Visualization and interpretation tasks.
  • Continuous hands-on practice.

3. Tool ecosystem

  • Exposure to popular R packages and environments.
  • Working with notebooks or IDEs.
  • Understanding output interpretation.
  • Practical analytical workflows.

4. Mentorship and feedback

  • Guidance on analytical reasoning.
  • Feedback on code and interpretation.
  • Clarification of common mistakes.
  • Support during projects.

5. Career relevance

  • Skills aligned with analyst and researcher roles.
  • Projects suitable for portfolios.
  • Interview-oriented concepts.
  • Focus on clarity and insight generation.

Quick comparison

Factor

Good Course

Weak Course

Focus

Analysis + coding

Only syntax

Practice

Real datasets

Theory-heavy

Guidance

Interpretation feedback

No explanation

Outcome

Analytical confidence

Limited application

R Language Career Opportunities and Job Roles

R language skills are valued in roles that rely on statistical rigor and data interpretation.

Popular Job Roles After R Language Courses

Job Role

Average Pay (INR)

Data Analyst

6.8 LPA

Statistician

10.0 LPA

Research Analyst

6.5 LPA

Data Scientist

12.5 LPA

Quantitative Analyst

14.0 LPA

Industries Hiring R Professionals

  • Analytics and consulting firms
  • Healthcare and life sciences
  • Finance and banking
  • Research and academia
  • Technology and product companies

R Language Career Growth Path

Career Stage

Typical Roles

Entry-Level

Junior Analyst, Research Associate

Mid-Level

Data Analyst, Statistician

Senior-Level

Senior Analyst, Data Scientist

Leadership

Analytics Lead, Research Head

Frequently Asked Questions

1. How is learning R different from learning spreadsheet-based analysis?

R allows deeper analysis beyond spreadsheet limits by handling large datasets, complex statistical methods, and reproducible workflows. Unlike spreadsheets, R encourages structured analysis, automation, and consistency, which becomes essential when analyses grow in size, complexity, or need frequent updates.

2. Is R a good first programming language for data-focused learners?

Yes. R works well as a first programming language for learners interested in statistics and analytics. Its syntax is designed around data analysis tasks, which helps beginners connect code directly to data insights instead of focusing heavily on software engineering concepts.

3. How does R support reproducible analysis?

R enables reproducible analysis by allowing users to document data cleaning, analysis, and visualization steps in code. This makes results easier to review, validate, and repeat, which is especially important in research, audits, and collaborative analytical environments.

4. Can R handle unstructured or messy datasets effectively?

Yes. R is well suited for real-world datasets that contain missing values, inconsistencies, or formatting issues. Learners develop skills to clean, restructure, and validate data before analysis, which reflects how data is encountered in practical business and research settings.

5. Does learning R improve statistical intuition over time?

Learning R strengthens statistical intuition by encouraging experimentation and visualization. By rapidly testing assumptions, comparing outcomes, and viewing results graphically, learners gain a deeper understanding of how statistical methods behave across different datasets and scenarios.

6. How useful is R for interpreting surveys and experiments?

R is especially effective for survey and experimental analysis. It helps evaluate bias, compare groups, assess significance, and visualize outcomes clearly. This makes it popular in market research, social sciences, healthcare studies, and product experimentation environments.

7. Can R be used for storytelling with data?

Yes. R supports data storytelling by allowing users to combine analysis and visualization to present clear narratives. Learners can transform raw data into structured insights that explain trends, comparisons, and outcomes in a way that stakeholders can easily understand.

8. How does R help improve analytical accuracy?

R improves accuracy by reducing manual errors common in point-and-click tools. Since steps are coded and repeatable, calculations remain consistent. This encourages careful validation of assumptions and results, leading to more reliable conclusions across repeated analyses.

9. Is R commonly used in interdisciplinary roles?

R is widely used in interdisciplinary roles where data intersects with domain knowledge, such as economics, psychology, healthcare, and environmental studies. It allows professionals to apply statistical reasoning directly within their subject area without relying entirely on separate technical teams.

10. What learning pace works best for mastering R?

A steady pace works best, focusing on small datasets, repeated practice, and interpretation rather than speed. Learners benefit from revisiting concepts across different problems, which gradually builds confidence and reinforces analytical thinking instead of memorization.

11. How does R learning complement other data tools later?

R builds a strong analytical foundation that makes learning other tools easier. Concepts like distributions, modeling, and interpretation transfer well to visualization platforms, databases, or machine learning tools, enabling learners to adapt faster in evolving data environments.

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Disclaimer

  1. The above statistics depend on various factors and individual results may vary. Past performance is no guarantee of future results.

  2. The student assumes full responsibility for all expenses associated with visas, travel, & related costs. upGrad does not .