Data science with AI Course by Pravesh Kumar

The Data Science with AI Course is a comprehensive, industry-oriented training program designed to help students build strong foundations in data analytics, machine learning, deep learning, and modern artificial intelligence tools. This course takes learners from beginner-level programming and statistics to advanced concepts such as neural networks, generative AI, and real-world data science workflows.

In today’s digital economy, data science and AI are among the most in-demand career fields. Organizations rely on data professionals to analyze large datasets, build predictive models, and develop intelligent solutions for business problems. This course is ideal for students, graduates, working professionals, and beginners who want to build practical skills and pursue careers in data science, AI, and analytics.

The program combines conceptual understanding, practical coding experience, and project-based learning to ensure students gain both academic knowledge and job-ready skills.

What Students Will Learn

Module 1: Introduction to Data Science

• What is Data Science?

• Data Science Lifecycle

• Role of Data Scientist

• Applications of Data Science in Industry

• Tools & Technologies Overview

• Real-world Case Studies

🔹 Module 2: Python Programming for Data Science

Python Basics

• Variables & Data Types

• Operators

• Conditional Statements (if-else)

• Loops (for, while)

• Functions (args, kwargs, lambda)

• List Comprehension

• Exception Handling

Advanced Python

• OOP (Class, Object, Inheritance, Polymorphism)

• Modules & Packages

• File Handling

• Working with JSON & CSV

• Virtual Environment

Module 3: Mathematics & Statistics for Data Science

• Basic Mathematics for ML

• Linear Algebra (Vectors, Matrices)

• Probability Concepts

• Descriptive Statistics

• Inferential Statistics

• Hypothesis Testing

• Normal Distribution

• Correlation & Covariance

Module 4: NumPy & Pandas

NumPy

• Arrays & Indexing

• Broadcasting

• Mathematical Operations

• Random Module

Pandas

• Series & DataFrame

• Data Cleaning

• Handling Missing Values

• GroupBy Operations

• Merge & Join

• Data Transformation

• Working with Large Datasets

Module 5: Data Visualization

• Matplotlib (Line, Bar, Pie, Histogram)

• Seaborn (Heatmap, Pairplot, Boxplot)

• Plotly (Interactive Charts)

• Dashboard Concepts

• Visualization Best Practices

Module 6: SQL for Data Science

• Database Concepts

• CREATE, INSERT, UPDATE, DELETE

• WHERE, GROUP BY, HAVING

• JOIN (Inner, Left, Right, Full)

• Subqueries

• Window Functions

• Case Study Queries

Module 7: Exploratory Data Analysis (EDA)

• Data Profiling

• Outlier Detection

• Feature Engineering

• Correlation Analysis

• EDA Project

Module 8: Machine Learning

Supervised Learning

Regression

• Linear Regression

• Multiple Regression

• Ridge & Lasso

• Evaluation Metrics (MAE, MSE, RMSE, R²)

Classification

• Logistic Regression

• KNN

• Decision Tree

• Random Forest

• SVM

• Naive Bayes

Unsupervised Learning

• K-Means Clustering

• Hierarchical Clustering

• DBSCAN

• PCA (Dimensionality Reduction)

Model Evaluation

• Train-Test Split

• Cross Validation

• Confusion Matrix

• ROC-AUC

• Hyperparameter Tuning

• GridSearchCV

Module 9: Deep Learning

• Introduction to Neural Networks

• Perceptron

• Activation Functions

• ANN using Keras / TensorFlow

• CNN Basics

• RNN Basics

• Practical Implementation

Module 10: Generative AI & AI Tools

• Introduction to AI

• NLP Basics

• Transformers

• Introduction to LLM

• Prompt Engineering

• ChatGPT & AI Tools in Industry

• AI Ethics

Teaching Method

This course is conducted through live online sessions with a focus on practical and interactive learning. Teaching methods include:

• Step-by-step concept explanations
• Live coding demonstrations
• Real-world datasets and case studies
• Hands-on assignments and exercises
• Guided project-based learning
• Interactive doubt-solving sessions

Students will also complete projects and practical tasks to build a strong portfolio.

Why This Course

This program provides a complete learning pathway covering data science fundamentals, machine learning, deep learning, and modern AI technologies in a structured manner. The curriculum is designed to balance theoretical understanding with practical implementation, ensuring students develop job-ready analytical and technical skills.

Benefits and Outcomes

By completing this course, students will:

• Develop strong data analysis and programming skills
• Gain practical experience in machine learning and AI
• Learn to work with real-world datasets
• Build projects to strengthen their professional portfolio
• Understand modern AI tools and industry trends
• Improve problem-solving and analytical thinking abilities
• Explore career opportunities in data science, AI, and analytics

This course provides a complete foundation for learners aiming to build successful careers in data science and artificial intelligence.