Mastering Data Science: From Basics to Real-World Applications

Mastering Data Science: From Basics to Real-World Applications
(In Association with iHUB Divyasampark IIT Roorkee)

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.

Batch Details:
Class Timings: 7:00 pm – 9:00 pm (Saturday); 10 am -12 noon (Sunday) Start Date: 29th Mar 2025
Duration: 80 Hours End Date: 17th Aug 2025
Mode: Online Certification: iHUB Divyasampark IIT Roorkee
Last Date to Register: 28th Mar 2025

Course Fee: Students/PhD Scholars/RA/JRF/SRF/Postdoc fellows: Rs. 12,000/-
Faculty/Working Professional: Rs. 14,000/-
(Amounts inclusive of GST)

Course Highlights:
•   Industry-Relevant 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.

Course Overview:

Module 1

Basics of Python Language
Python objects with details of shell/numbers/variables etc.
Comparison operators
Range, List Comprehension,
Functions, Lambda expressions etc.
Introduction to NumPy
Random functions, Reshape, Arithmetic Operations
Hands-on project

Module 2

Introduction to 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
Hands-on Project

Module 3

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

Module 4

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 like Mean Absolute Error, Root Mean Squared Error, - Regularization (Ridge & Lasso)

Module 5

Logistic Regression, Sigmoid Function, Anscombe’s quartet
Confusion Matrix, interpreting parameters like F-1 score, Accuracy, Precision, Recall etc.
Bias-variance trade off, Overfitting, Underfitting of Models
K- nearest neighbors (KNN), Elbow Method; Distance Metric in KNN
Understanding Support Vector machines using Hyperplanes; Maximum Margin Classifier

Module 6

Higher Dimension Transformation and Projection, Kernels :: Polynomial, RBF etc.
Decision Trees, Nodes: Root, Leaf, Parent, Children. Tree Pruning, Gini Impurity
Random Forests, Ensemble Learners, Information Gain
Boosted Trees, Weak and Strong Learners, AdaBoost, Gradient Boosting, Stump Classification
Naive Bayes classifier, Conditional Probability, Bayes Theorem

Module 7

Natural Language Processing (NLP), Count Vectorization, Extracting Features From Text Data, Term Frequency - Inverse Document Frequency (TF-IDF), Document Term Matrix (DTM)
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

Module 8

DBSCAN, epsilon distance, Core, Border and Outlier
Principal Component Analysis (PCA), Dimension Reduction
Introduction to Deep Learning
Artificial Neural Networks
Perceptron Model, Activation Functions; Cost Functions and Gradient Descent
Forward and Backward Propagation; Keras vs TensorFlow
Hands-on Project on ML

Prerequisites and eligibility:

No coding experience in any programming language required. We’ll start from scratch.
This course can be taken up by any undergraduate/postgraduate student of Basic & Applied Sciences, Engineering, Management 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 i3 processor) and proper internet/Wi-Fi connection.

Contact Person: Dr. Subrat Kotoky

Email: [email protected] / [email protected]

Phone: 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

Has successfully conducted 25+ courses and trained 1500+ learners in the fields of Python Programming, Data Analytics, Machine Learning, Deep Learning, Computer Vision etc. till now.

(Total Experience in conducting Professional Courses: 4+ Years)

Certifications: