Generative AI  /  Beginner to Mastery

Mastering Data Science with R

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Course Duration

240 Hours

 

Course Material

Live. Online. Interactive.

Cutting-edge focus on deep learning for AI proficiency.

Equip graduates with in-demand skills for successful careers in data science.

Industry-relevant case studies to bridge the gap between theory and real-world problem-solving.

Work on real-world projects and case studies to apply concepts practically and build a strong portfolio.

KEY HIGHLIGHTS OF MASTERING DATA SCIENCE WITH R PROGRAM

1) Weekly sessions with industry professionals

2) Dedicated Learning Management Team

3) 240 hours of hands-on learning experience

4) 75+ Live Sessions Across 4 months

5) 105 Hours of Self-Paced Learning

6) Learn from Industry Experts.

🔺More than 25+ industry-related projects and case studies

🔺24*7 Support

🔺1:1 Mock Interview

🔺Designed for both working professionals and fresh graduates

🔺Competitive Edge and Innovation

🔺 Personalised mentorship sessions with industry experts

🔺Dedicated Learning Management Team

🔺No-Cost EMI Option

🔺High Demand and Career Opportunities

🔺Problem-Solving and Critical Thinking

WHY JOIN MASTERING DATA SCIENCE WITH R PROGRAM?

Hands-On Learning

Gain practical experience with advanced tools like GPT, DALL-E 2, and Hugging Face Transformers.

Comprehensive Skill Set

Master everything from Python programming to cutting-edge AI techniques.

Stay Ahead

Learn the latest AI and generative technologies shaping the future.

Career Advancement

Boost your qualifications and open doors to advanced AI roles.

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Mastering Data Science with R OVERVIEW

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.

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Mastering Data Science with R Objectives

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.

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Why Learn Mastering Data Science with R ?

Master R & Data Analysis

Gain proficiency in R, statistical analysis, and data visualization to effectively explore and understand data.

Build Predictive Models

Learn machine learning and deep learning techniques to create intelligent systems for accurate predictions.

Unlock Data Potential

Discover hidden patterns and insights within your data through data analysis and feature engineering, driving informed decision-making.

Enhance Communication

Innovative ways of communicating detailed understanding and findings with data visualisation techniques.

Advance Your Career

Gain skills that can lead you to top jobs in data science, machine learning, and deep learning.

Hands-On Projects

Apply your knowledge through real-world case studies and projects, building practical experience and a strong portfolio.

Industry-Relevant Skills

Equip yourself with R packages and frameworks widely used in data science and analytics, ensuring you stay aligned with industry needs.

Drive Innovation

Develop cutting-edge technologies and solutions with deep understanding of these tools.

Program Advantages

✅ Covers R, statistical analysis, machine learning, and deep learning for a broad data science foundation.

✅ Focuses on current tools and practices to ensure up-to-date training.

✅ Combines theory with projects for hands-on problem-solving skills.

✅ Taught by knowledgeable educators with a focus on individual student attention.

✅ Prepares students for roles like Data Analysts, Machine Learning Engineers, and AI Specialists.

✅ Teaches state-of-the-art libraries and frameworks such as TensorFlow and Keras.

✅ Develops problem-solving abilities and data-driven decision-making.

✅ Encourages teamwork, idea sharing, and networking among students.

✅ Helps students build a professional portfolio for job searches and promotions.

✅ Provides a solid foundation for continuous learning in evolving data science technologies.

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Mastering Data Science with R program Certifications

Mastering Data Science with R Curriculum

Module 01 - R-Programming
Lecture 01: Introduction to R, Installing R and RStudio, Basics of RStudio IDE, Writing and executing R scripts, Variables and Data Type in R, Operators
Lecture 02: 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 03: 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 04: Data Structures – List, Tuple, and List Comprehensions
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 02 - 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 03 - 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 04 - Deep Learning
Lecture 22: Introduction to Neural Network, Foreward Propagation, Activation Function
Lecture 23: Activation Function(Linear, Sigmoid, Relu, Leaky Relu), Optimizers, Gradient Descent, Stochastics 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 27: Project Session

Mastering Data Science with R Skills Covered

Mastering Data Science with R Tools Covered

Mastering Data Science with R Program Benefits

Cutting-Edge Knowledge
Enhance your ability to design and create cutting-edge AI applications.
Future Of Your Career
Unlock well paying high-demand jobs in the AI and tech industry.
Real-world Applications
Solve challenging problems in all competitive coding related domains.
Breadth of Skills
Develop skills in programming, data analysis and AI methods across the board.
Innovative Problem-Solving
Enhance your ability to design and create cutting-edge AI applications.
Earn Certification
Earn a certification that validates your expertise and enhances your professional credibility.
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Admission Process

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.