Hero Image

Mastering Data Science: From Basics to Real-World Applications

About the Course:

Data Science is one of the most in-demand fields in today’s technology-driven world, known for its ability to extract meaningful insights, predict trends, and automate decision-making using data. Python has emerged as the most popular language in the Data Science ecosystem due to its simplicity and a vast array of powerful libraries. This course is designed to help participants build a strong foundation in Python programming, data analytics, and machine learning within a structured timeframe. The course begins with Python fundamentals and progresses to more advanced topics relevant to data manipulation and analysis. Participants will learn to use essential libraries such as NumPy, Pandas, Matplotlib, and Seaborn for data analysis and visualization. The next phase focuses on applying statistical and machine learning techniques, including both supervised and unsupervised algorithms, to real-world datasets. The hands-on approach ensures learners not only understand theoretical concepts but also gain practical experience in solving real-world problems. By the end of this course, the learners will develop the skills to analyze complex datasets, build predictive models, and communicate data-driven insights effectively - empowering them for careers in Data Science and related domains.



Course Objectives:
  • To make the participants understand the fundamentals of Python programming and its applications in Data Science.
  • To develop proficiency in using Python libraries for data analysis, preprocessing, and visualization.
  • To introduce participants to essential statistical and machine learning techniques used in data-driven decision-making.
  • To equip learners with the ability to build, evaluate, and interpret predictive models
  • To provide hands-on experience through projects using real-world datasets.
  • To lay a strong foundation for advanced topics such as Deep Learning, Neural Networks and Reinforcement Learning


Link to Register :- https://rzp.io/rzp/MasteringDataScience

Batch Details:
Class Timings: Saturday & Sunday (4 pm – 6 pm)                Start Date: 24th May 2025
Duration: 76 Hours                                                                                End Date: 5th Oct 2025
Mode: Online (ILT over Zoom/Webex/GMeet)                            Certification: Globally accepted
Last Date to Register: 23rd May 2025


Course Fee:
Students/PhD Scholars/RA/JRF/SRF/Postdoc fellows: Rs. 12,000/-
Faculty/Working Professional: Rs. 14,000/-


Contact Person: Dr. Subrat Kotoky
CTO, Ritvij Bharat Pvt. Ltd. (RBPL)
Ph.D. in Mechanical Engineering (IIT Guwahati)
rbpl.edu@gmail.com/subrat.kotoky@ritvij.co.in
9085317465/8473874389


Expert Profile: Mr. Shreyas Shukla
Professional Corporate Trainer & Microsoft Azure Certified Data Engineer
M.Tech-IIT Kharagpur & BE- The Aeronautical Society of India, New Delhi
1. DP-203: Microsoft Certified: Azure Data Engineer Associate
2. DP-900: Microsoft Certified: Azure Data Fundamentals
3. AZ-900: Microsoft Certified: Azure Fundamentals

Our Students Rate This Course

4.5
Program Fee

Rs 12,000/- & Rs. 14,000

Available Seats

100

Schedule

Saturday & Sunday (4 pm – 6 pm)

Only Few Seats Left

Reviews

Testimonials

Module 1

Fundamentals of Python Programming

Basics of Python Language

Python objects with details of Numbers/Variables/Strings/Lists/Dictionaries/Tuples etc. 

Variable Assignment, Indexing & Slicing

Comparison & Logical operators

Range, List Comprehension

Functions, Lambda expressions etc.


Module 2

Data Science & Statistics Fundamentals

·       Fundamentals of Data Science

·       Descriptive Statistics: Types of Data, Frequency Distribution, Mean, Median, Mode

·        Skewness, Variance, Standard Deviation, Covariance, Correlation

·       Inferential Statistics: Distribution, Normal Distribution, Standard Normal Distribution, Central Limit Theorem

·       Confidence Intervals: Z-score, T-score, Dependent and Independent Samples

Module 3

Data Analytics operations with Python libraries

Introduction to NumPy

Random Functions, Reshape & Arithmetic operations

Introduction and working with Pandas

Selecting a single column, important series methods

Indexing & Sorting; loc & iloc with series; Inspecting dataFrames, filtering with conditional operators

Adding & removing columns; updating values, working with date & time 


Module 4

Data Visualization Techniques

Working with Matplotlib Library; Working with different plots 

Working with text 

Concatenating Series & DataFrames 

Working with Seaborn Library

Seaborn Categorical plots

Hands-on Project on Data Analytics using Real-World Datasets


Module 5

ML Basics & Supervised Learning Methods

Machine Learning Basics, introduction to supervised & unsupervised learning

Linear Regression for One and Multiple Variables, Cost Function & Gradient Function

Ordinary Least Square, Dummy Variables, One Hot Encoding

Polynomial Regression

Anscombe’s quartet, Performance Metrics Mean Absolute Error, RMS Error

Logistic Regression, Sigmoid Function

Confusion Matrix, interpreting parameters like F-1 score, Accuracy, Precision, Recall etc. 


Module 6

Supervised Learning Methods & NLP Basics

Bias-variance trade off, Overfitting, Underfitting of Models

K- nearest neighbors (KNN), Elbow Method; Distance Metric in KNN

Decision Trees, Nodes: Root, Leaf, Parent, Children. Tree Pruning, Gini Impurity

Random Forests, Ensemble Learners, Information Gain

Naive Bayes classifier, Conditional Probability, Bayes Theorem

Natural Language Processing (NLP) basics

Count Vectorization, Extracting Features from Text Data, Term Frequency - Inverse Document Frequency (TF-IDF), Document Term Matrix (DTM)


Module 7

Unsupervised Learning Methods

Unsupervised Learning Basics

K-Means Clustering, Clustering of unlabelled data, Assigning new point to the cluster

Hierarchical Clustering: Agglomerative and Divisive Approach, Dendrogram, Linkage Matrix, Similarity Metrics, Ward

DBSCAN, epsilon distance, Core, Border and Outlier 

Principal Component Analysis (PCA), Dimension Reduction

Hands-on Project using Real-World Datasets


Module 8

Deep Learning Foundations

Introduction to Deep Learning

Applications of Deep learning

Artificial Neural Networks

Perceptron Model, Activation Functions; Cost Functions and Gradient Descent

Forward and Backward Propagation; Keras & TensorFlow

Portfolio building on GitHub/LinkedIn


NEWS & UPDATES

Career Transitions

55% Average Salary Hike

$1,27,000 Highest Salary

800+ Career Transitions

300+ Hiring Partners

Who Can Apply for the Course?

  • No coding experience required. We’ll start from scratch.
  • This course can be taken up by any undergraduate/postgraduate student of Basic & Applied Sciences, Engineering, and Computer Applications and also by Research Scholars/Faculties/Working Professionals who want to upskill themselves
  • Participants need to have a laptop/PC (with a minimum of 4 GB RAM, 100 GB HDD, Intel i3processor) and proper internet/Wi-Fi connection.
Who can aaply

About Program

This program by iHub Divya Sampark, IIT Roorkee helps you gain the data analytics, machine learning, and artificial intelligence skills sought after by top employers.

Key Highlights

Industry-Relevant High-In-Demand Skills.
Hands-on based learning experience through practical projects.
Globally accepted certification from iHUB Divyasampark IIT Roorkee
Full-time access to recorded lectures/PPTs/PDFs/Study Materials.
Session on Resume Preparation/Interview Preparation.

Our Alumni Work At

Master Client Desktop

What is included in this course?

  • Non-biased career guidance
  • Counselling based on your skills and preference
  • No repetitive calls, only as per convenience
  • Rigorous curriculum designed by industry experts
  • Complete this program while you work

I’m Interested in This Program