Data Analysis & Data Science with AI Training by Pravesh Kumar

The Data Analysis & Data Science with AI Course is a comprehensive online training program designed to help learners build strong expertise in data analytics, machine learning, and modern artificial intelligence tools. This course is ideal for students, working professionals, and beginners who want to develop practical skills in handling real-world data, performing analysis, building predictive models, and understanding AI technologies used in industry today.

The curriculum covers everything from programming fundamentals to advanced machine learning, deep learning, and generative AI concepts. Through hands-on projects, industry case studies, and structured modules, learners gain both theoretical knowledge and practical experience required to succeed in data science and AI-related careers.

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 pytorch / 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

Module 13: Projects

🔹 Beginner Level

• Sales Prediction

• Titanic Survival Prediction

• Student Performance Analysis

🔹 Intermediate

• Customer Churn Prediction

• Loan Approval Prediction

• House Price Prediction

🔹 Advanced

• Recommendation System

• Sentiment Analysis

• Resume Screening AI

• End-to-End ML Deployment Project

🎯 Additional Training Components

• Resume Building

• GitHub Portfolio Creation

• Mock Interviews

• Aptitude + Technical Test

• Industry Case Studies

• Capstone Project

Teaching Method

The course is delivered through interactive online sessions with a practical learning approach. Teaching methods include:

• Step-by-step concept explanation
• Hands-on coding exercises and assignments
• Real-world case studies and datasets
• Capstone project for end-to-end learning
• Continuous doubt-clearing and feedback sessions

Why This Course

This program combines data analytics, machine learning, deep learning, and generative AI into one structured learning path. It focuses on practical implementation, helping students gain job-ready skills while understanding modern AI technologies used across industries.

Benefits and Outcomes

By completing this course, learners will:
• Master data analysis using Python, SQL, and visualization tools
• Build machine learning and AI models for real-world problems
• Gain hands-on experience with multiple industry projects
• Understand generative AI tools and modern data science workflows
• Develop a professional portfolio for career opportunities in data science and analytics