Mastering Data Science with R
August 14, 2024 2025-06-24 10:31Mastering Data Science with R
Data Science with R Programming Course | Statistical Analysis Training
Comprehensive curriculum covering R, statistics, machine learning, and deep learning.
Strong emphasis on practical application through hands-on projects.
Flexible program design to accommodate learners from diverse backgrounds.
Cutting-edge focus on deep learning for AI proficiency.
Equip graduates with in-demand skills for successful careers in data science.
Key Highlights of Mastering Data Science with R
Weekly sessions with industry professionals
Dedicated Learning Management Team
240 Hours of hands-on learning experience
Over 75 hours live sessions spread across 04 months
105 hours of self-paced Learning
Learn from IIT Faculty & Industry experts
More than 25+ industry-related projects and case studies
Personalised mentorship sessions with industry experts
24/7 Support
Dedicated Learning Management Team
1:1 Mock Interview
No-Cost EMI Option
Designed for both working professionals and fresh graduates
High Demand and Career Opportunities
Competitive Edge and Innovation
Problem-Solving and Critical Thinking
Weekly sessions with industry professionals
Dedicated Learning Management Team
240 hours of hands-on learning experience
Over 75 hours live sessions spread across 04 months
105 hours of self-paced Learning
Learn from IIT Faculty & Industry experts
More than 25+ industry-related projects and case studies
Personalised mentorship sessions with industry experts
24*7 Support
Dedicated Learning Management Team
1:1 Mock Interview
No-Cost EMI Option
Designed for both working professionals and fresh graduates
High Demand and Career Opportunities
Competitive Edge and Innovation
Problem-Solving and Critical Thinking
Why Join Mastering Data Science with R ?
Upcoming Batch:-
19th January 2025 (10pm to 1 am )
1st of February 2025 (10 pm to 1 am)
Advanced R Programming for Data Scientists
This Program offers a deep dive into fundamental AI technologies, Python programming language and essential libraries for machine learning image processing etc. Participants will learn cutting-edge tools such as Hugging Face Transformers, GPT, DALL-E 2.0, MidJourney, GANs, RAG, LanguageChain. The curriculum integrates theoretical understanding with real-world use-cases to develop a skills-rich ecosystem capable of utilizing AI and generative technologies at an advanced level.
ENROLL NOW & BOOK YOUR SEAT AT FLAT 50% WAIVER ON FEE
Batch Schedule
| Batch | Batch Type |
|---|---|
| Online Live Instructor Led Session | Full-Time |
| Online Live Instructor Led Session | Part-Time |
Regional Timings
| Region | Time |
|---|---|
| IST (India Standard Time) | 09:00 PM – 12:00 AM |
| Bahrain, Qatar, Kuwait, Saudi Arabia | 06:30 PM – 09:30 PM |
| UAE / Oman | 07:30 PM – 09:00 PM |
Talk to our Corporate training advisor
Mastering Data Science with RObjectives
The course aims to provide participants a thorough understanding of AI technologies with its more high-level programming side. After completing this, the student will be proficient in python programming and have experience working with Hugging Face Transformers, GPT, DALL-E 2, MidJourney. Then they can integrate those levels of solutions into their AI application. And will be equipped with hands-on experience on GANs, RAG and LangChain to face the complex challenges and innovations in the AI community.
Why Learn Mastering Data Science with R ?
Program Advantages
Industry-relevant skills in high-demand AI technologies like GPT, DALL-E 2, and Hugging Face Transformers.
Comprehensive curriculum covering Python programming to advanced AI techniques such as GANs and RAG.
Hands-on experience through real-world projects and applications.
Expert guidance from experienced instructors in AI and generative technologies.
Enhanced career opportunities with skills valued in the tech industry.
Industry-relevant skills in high-demand AI technologies like GPT, DALL-E 2, and Hugging Face Transformers.
Comprehensive curriculum covering Python programming to advanced AI techniques such as GANs and RAG.
Hands-on experience through real-world projects and applications.
Expert guidance from experienced instructors in AI and generative technologies.
Enhanced career opportunities with skills valued in the tech industry.
Mastering Data Science with R Certification
Mastering Data Science with R Learning Path/Curriculum
Module 01 - Orientation
Lecture 01: Orientation (Introduction to Data Science, Scope of Data Science)
Module 02 - R-Programming
Lecture 02: Introduction to R, Installing R and RStudio, Basics of RStudio IDE, Writing and executing R scripts, Variables and Data Type in R, Operators
Lecture 03: Creating vectors, Vector indexing and slicing, Vectorized operations, Creating matrices, Matrix operations, Matrix indexing, Creating lists, Creating data frames, Indexing and manipulating lists and data frames
Lecture 04: Conditional statements, Loops, Applying functions, Flow Control, Functions in R, Object-Oriented Programming in R, S3 and S4 classes, Methods and inheritance, Creating and using objects
Lecture 05: Creating and using factors, Working with dates and times, Reading and writing (CSV files and Excel files), Introduction to the readr and readxl packages
Lecture 06: Introduction to dplyr, Selecting, filtering, and arranging data, Grouping data, Summarizing data with summarize and mutate
Lecture 07: Data Manipulation in R - dplyr, Data Manipulation & Data Visualization in R - tidyr
Lecture 08: Introduction to Text Mining, Text Preprocessing, Document-Term Matrix (DTM) and TF-IDF, Exploratory Text Analysis, Sentiment Analysis
Lecture 09: Install Necessary Packages, Create a New Package, Package Structure, Writing Functions, Documenting Functions, Testing Your Package, Building and Checking and Sharing Your Package
Lecture 10: Introduction to APIs, Using the 'httr' Package, GET & POST Request, and Authentication, Introduction to Web Scraping, Using the 'rvest' Package, Handling Dynamic Content, Handling Sessions and Cookies
Lecture 11: Connecting to Databases in R, Packages Installation, Connect to Database, Execute Queries, Write Data, Disconnect and Error Handling
Module 03 - Statistics
Lecture 12: Introduction to Statistics, Descriptive Statistics, Sample, Population, Major of Central Tendency, Standard Deviation
Lecture 13: Variance, Range, IQR, Outliers, Correlation, Covariance, Skewness, Kurtosis, Probability
Lecture 14: Probability, Probability distributions, Central Limit Theorem, Binomial and Poisson Distribution
Lecture 15: Normal Distribution, Type I & Type II Error
Lecture 16: T-test, Z-test, Hypothesis, Testing Interview Questions
Module 04 - Machine Learning
Lecture 17: Introduction to ML, Types of variables, Encoding, Normalization, Standardization, Types of ML, Linear Regression
Lecture 18: Linear Regression, Logistic Regression, SVM, KNN, Naïve Bayes, Decision Tree, Random Forest
Lecture 19: Mean Absolute Error, Mean and Root Mean Square Error, Confusion Matrix, R2 Score, Adjusted R2 Score, F1 Score
Lecture 20: Classification Report, AUC ROC, Accuracy, Ensemble Techniques, Random Forest, Xgboost
Lecture 21: Unsupervised Machine Learning, PCA, Clustering, k-Means Clustering and Hierarchical clustering
Module 05 - Deep Learning
Lecture 22: Introduction to Neural Network, Forward Propagation, Activation Function
Lecture 23: Activation Function (Linear, Sigmoid, Relu, Leaky Relu), Optimizers, Gradient Descent, Stochastic Gradient Descent
Lecture 24: Mini batch Gradient Descent, Adagrad, Padding, Pooling, Convolution, Checkpoints and Neural Networks Implementation
Lecture 25: Introduction to Time Series Analysis, Various components of the TSA, Decomposition Method (Additive Method and Multiplicative)
Lecture 26: ARMA and ARIMA
Lecture 28: Project Session
Mastering Data Science with R Skills Covered
Data Analysis
Data Manipulation,
Statistical Analysis
Data Visualization
Machine Learning
Model Evaluation
Feature Engineering
Neural networks
Transfer learning
Deep Learning
Computer Vision techniques
Image Processing
Text Processing
Sentiment Analysis
Language Modelling
Reinforcement Learning Algorithms
Advanced NLP Techniques
Transformer Architectures
Text Generation & Comprehension
Creative Image Generation
Synthetic Data Creation
Enhanced Language Models
AI Application Development
Retrieval-Augmented Generation (RAG)
Mastering Data Science with R Tools Covered
Mastering Data Science with R Program Benefits
Projects that you will Work On
Machine Learning
No. of Projects:10
Deep Learning
No. of Projects:10
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Job Obligation after this course
You can apply for
jobs in the following roles:-
Develop and deploy AI models and algorithms for various applications.
Continuously optimize machine learning and deep learning systems.
Analyze data and extract actionable insights to drive business decisions.
Create and manage AI-driven products, ensuring they meet user needs and business goals.
Stay updated with the latest advancements in AI and apply them to improve existing solutions.
Collaborate with cross-functional teams to integrate AI technologies into business operations.
Troubleshoot and resolve issues related to AI models and systems.
Contribute to AI research and innovation within the organization or industry.
Ensure ethical and responsible use of AI technologies in all applications.
Companies Hiring for this course
Program Advisors
IITs
IIMs
NITs
Experts from the IT Industries.
Admission Details
The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.
Course Fees & Financing
Course Fees
(50% OFF upto 31ˢᵗ March)
(Inclusive Of All Taxes)
Payment Partners
We partnered with financing companies to provide competitive finance option at 0% interest rate with no hidden costs
Upcoming Batches/Program Cohorts
| Batch | Date | Time | Batch Type |
|---|---|---|---|
| Online Live Instructor Led Session | 5th April 2025 | 10:00 AM | Full-Time |
| Online Live Instructor Led Session | 29th March 2025 | 02:00 PM | Part-Time |
Comparison with Others
| Feature | Our Course | Competitor A | Competitor B |
|---|---|---|---|
| Curriculum Scope | Comprehensive: Python, ML, DL, NLP, CV, Generative AI | Basic ML and DL focus | General AI with less focus on Generative AI |
| Hands-On Experience | Extensive practical projects with tools like GPT, DALL-E 2 | Limited practical projects | Hands-on projects mainly in traditional AI |
| Advanced Tools | GPT, DALL-E 2, MidJourney, Hugging Face Transformers, GANs, RAG, LangChain | Focus on traditional ML frameworks | Emphasis on standard ML and AI tools |
| Instructor Expertise | Experienced professionals with industry and research background | Mix of industry and academic instructors | Primarily academic-focused instructors |
| Real-World Applications | Emphasis on real-world problem solving and innovation | Mostly theoretical applications | General applications with less focus on innovation |
| Career Support | Strong focus on career advancement and networking | Basic career services | Limited career support and networking opportunities |
| Networking Opportunities | Connect with peers and industry leaders | Limited networking events | Few networking opportunities |
| Certification Value | Recognized certification for advanced AI roles | Standard certification | General certification with less industry recognition |
Self Assessments
Mastering Data Science with R Training Faqs
A Masters in Data Science with R course trains students in advanced data analysis, statistical techniques, and machine learning using the R programming language, with practical experience on real-world data projects.
Joining a Masters in Data Science with R program enhances your expertise in data analysis and statistical techniques using R, a powerful tool in data science. It offers practical experience with real-world data projects, preparing you for advanced roles in data analysis and decision-making across various industries.
Key features include advanced data analysis with R, statistical techniques, machine learning, hands-on projects, real-world data applications, and career preparation in data science.
Data manipulation, data visualization, statistical analysis, machine learning, deep learning, regression analysis, classification, clustering, model evaluation, feature engineering, neural network architectures
RStudio, Tidyverse, ggplot2, dplyr, Shiny, RMarkdown, caret, R (base functions, stats package), Excel, randomForest, e1071, xgboost, Keras, TensorFlow, h2o
A degree in Engineering, Mathematics, Science, or any equivalent field is ideal for pursuing a career in Artificial Intelligence and Data Science.
INR Fees (Exclusive of GST) INR 89,999.00
INR Fees (Inclusive of GST) INR 106,199.00
USD Fees USD 1099
INR Fees (Inclusive of GST) INR 106,199.00
USD Fees USD 1099
Data Scientist, Statistical Analyst, R Programmer
Entry-level: 7-12 lakhs per annum
Mid-level: 12-25 lakhs per annum
Senior-level: 25+ lakhs per annum
Mid-level: 12-25 lakhs per annum
Senior-level: 25+ lakhs per annum
25+ Projects
3-4 months
Our courses offer practical, industry-relevant content, expert instructors, flexible learning options, and strong career support.
A Master’s in Data Science and Generative AI is a program designed to combine sophisticated generative AI techniques with fundamental data science concepts. Along with deep learning, generative adversarial networks (GANs), and natural language processing (NLP), this curriculum usually covers fundamental subjects including statistical analysis, machine learning, data mining, and big data technologies.
Core Curriculum: Statistics, machine learning, deep learning, natural language processing, GANs, and reinforcement learning.
Practical Experience: Capstone projects, internships, and practicums with real-world applications.
Seminars, seminars, and lab access are examples of research opportunities.
Technical skill development includes programming (Python, R, SQL) and data engineering abilities.
Ethical and societal impact courses cover AI ethics, prejudice, data privacy, and regulation.
Career support includes job placement aid, resume seminars, and an alumni network.
Interdisciplinary Approach: Integration of areas such as business, healthcare, and social sciences.
1, we will optimise linked in profile and the algorithm of linkedin profile.
2, We will conduct GITHUB and Kaggle sessions.
3,We will do multiple Hackathons and guide you in problem solving skills for the interview process .
4, We will ensure peer learning session are being conducted .
5, We will issue mini certification for every tools.
6,We will asign you a personal mentor on pre booking i’t a one one session.
2, We will conduct GITHUB and Kaggle sessions.
3,We will do multiple Hackathons and guide you in problem solving skills for the interview process .
4, We will ensure peer learning session are being conducted .
5, We will issue mini certification for every tools.
6,We will asign you a personal mentor on pre booking i’t a one one session.
