Live Sessions

Applied Data Science and Machine Learning

Analyze data and build ML models using Pandas, NumPy, Scikit-learn, and Jupyter.

Tools you’ll work with
Python
Pandas
Sckit-learn
Seaborn
Numpy
R
₹50000
20 weeksBeginner
Instructor
Arun S
MSc AI • 10+ years • Ex - Microsoft
Enroll Now→

Course Overview

Description

25 lessons•57 exercises•4 exams•~12 hours

Are you looking for a well-structured data science fundamentals course?

Do you want to gain a clear understanding of the data science field?

This is the perfect course for you.

If terms like traditional data, big data, business intelligence, and machine learning sound confusing, this course will help you understand both meaning and practical application.

25 lessons•57 exercises•4 exams•12 hours

Course Curriculum

A structured, progressive curriculum designed to build depth, intuition, and real-world proficiency over time.

This section introduces key concepts and builds intuition through structured lessons and exercises.

Python data structures and memory management
Advanced functions, lambdas, and decorators
Object-Oriented Programming for data pipelines
Working with NumPy for numerical computing
Data manipulation using Pandas
Writing efficient, clean, and reusable Python code

This section introduces key concepts and builds intuition through structured lessons and exercises.

Data cleaning and preprocessing techniques
Exploratory Data Analysis (EDA) methods
Handling missing values and outliers
Data visualization using Matplotlib and Seaborn
Feature encoding and scaling
Data storytelling and insight communication

This section introduces key concepts and builds intuition through structured lessons and exercises.

Descriptive statistics and probability concepts
Probability distributions and sampling techniques
Hypothesis testing and confidence intervals
Correlation and covariance analysis
Statistical significance and p-values
Introduction to statistical modeling

This section introduces key concepts and builds intuition through structured lessons and exercises.

Supervised vs unsupervised learning concepts
Linear and logistic regression models
Classification algorithms (KNN, Decision Trees)
Ensemble methods (Random Forest, Gradient Boosting)
Clustering algorithms (K-Means, Hierarchical)
Model evaluation metrics and validation techniques

This section introduces key concepts and builds intuition through structured lessons and exercises.

End-to-end ML project workflow
Feature engineering techniques
Hyperparameter tuning and optimization
Model interpretability and explainability
Introduction to model deployment concepts
Best practices for production-ready ML solutions

This section introduces key concepts and builds intuition through structured lessons and exercises.

Problem statement selection and business understanding
Data collection and exploratory analysis
Data preprocessing and feature engineering
Model selection, training, and optimization
Model evaluation and result interpretation
Final project presentation and documentation

How You’ll Learn

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Live Classes
Quizzes
Assignments
Projects
Certification

Build Skills That Scale With You

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Learn deeply. Apply repeatedly.