Data Science & AI Analytics Bootcamp – Zero to Hero

Course Overview

The Data Science & AI Analytics Bootcamp – Zero to Hero is a 40-hour industry-focused certification program designed especially for non-technical students and graduates (BCom, BBA, MBA, Arts, Commerce backgrounds) who want to enter the high-growth fields of Data Science, AI, and Analytics—even with zero coding experience.

This bootcamp takes learners from absolute basics to real-world applications, combining hands-on technical training (32 hours) with career and placement readiness training (8 hours). The program emphasizes practical learning through 5 real-world projects, interview preparation, and job-readiness skills required to target high-paying roles (up to 12 LPA) in analytics-driven industries.

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Who This Course Is For

• BCom / BBA / MBA students & graduates

• Non-technical backgrounds transitioning into IT & analytics

• Freshers aiming for Data Analyst / ML roles

• Working professionals seeking career switch

• Anyone with zero coding experience but strong career ambition

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Course Duration

• Total Duration: 40 Hours

  • Technical Training: 32 Hours

  • Career & Skill Development: 8 Hours

• Mode: Practical, hands-on, instructor-led

• Tools Used: Google Colab, Python, Pandas, Matplotlib, Scikit-Learn, Seaborn

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Course Structure & Curriculum

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Module 1: Python for Data Science (10 Hours)

(8 Hours Core + 2 Hours Hands-on Practice)

This module builds a strong foundation in Python, focusing only on what is required for Data Science and Analytics, making it easy for non-tech learners.

Topics Covered

• Introduction to Python & Data Science

• IDE Setup: Google Colab

• Python Basics & Syntax

• Keywords, Variables, and Operators

• Data Types (Numeric, String, Boolean)

• Basic Functions (print() and built-in functions)

• Data Structures:

  • Lists, Tuples, Sets, Dictionaries

  • Operations on Data Structures

• Loops & Conditional Statements (for, if-else)

• Understanding Data & Datasets

• Types of Datasets:

  • Training vs Testing Data

  • train_test_split

• Handling Categorical Data

  • Categorical to Numerical Conversion (3 Techniques)

• Data Preprocessing:

  • Handling missing values

  • Cleaning datasets

• Essential Libraries:

  • Pandas

  • Matplotlib

  • Seaborn

  • Scikit-Learn

• Working with real datasets using all libraries

📌 Outcome:
Students become comfortable reading, writing, and manipulating data using Python—even with no prior coding experience.

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Module 2: Machine Learning & AI Fundamentals (16 Hours)

(8 + 4 + 4 Hours)

This module introduces Machine Learning and AI concepts from scratch, focusing on business understanding + practical implementation rather than heavy mathematics.

Topics Covered

• Introduction to Data Science

• What is AI vs ML vs Deep Learning

• End-to-End Machine Learning Project Flow

• Features & Labels:

  • Dependent vs Independent Variables

• Types of Machine Learning:

  • Supervised Learning

  • Unsupervised Learning

• Understanding ML Models:

  • Underfitting vs Overfitting

• What is an Algorithm (Beginner-Friendly)

• Supervised Learning Algorithms

  • Classification:

    • Decision Tree (Complete)

    • Random Forest (Overview)

    • Logistic Regression (Complete)

  • Regression:

    • Linear Regression

• Unsupervised Learning Algorithms

  • K-Means Clustering

  • Elbow Method

  • K-Nearest Neighbors (KNN)

• Introduction to Neural Networks

• Model Evaluation Techniques

• Confusion Matrix & Performance Metrics

📌 Outcome:
Students gain clarity on how real ML models work in companies and learn to build and evaluate models confidently.

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Module 3: 5 Real-World Industry Projects (10 Hours)

(8 Hours Projects + 2 Hours Netflix Recommendation System)

Hands-on projects to build real resumes and GitHub portfolios.

Projects Included

1. CO₂ Emission Prediction

   • Linear Regression

   • Environmental Analytics Use Case

2. Iris Dataset Clustering

   • K-Means Clustering

   • Elbow Method Implementation

3. German Credit Risk Analysis

   • Logistic Regression

   • Finance & Banking Use Case

4. HR Analytics Project

   • Employee Attrition Prediction

   • Logistic Regression

5. Netflix Recommendation System (2 Hours – Full Project)

   • Recommendation Logic

   • User Preference Analysis

📌 Outcome:
Students complete 5 resume-ready projects that demonstrate practical analytics and ML skills.

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Career & Skill Development Module (8 Hours)

This exclusive module prepares students to crack interviews, HR rounds, and group discussions, and to confidently apply for high-paying analytics roles.

Topics Covered

• Resume Building for Data Science & Analytics

• How to Apply for Jobs Online (Step-by-Step)

• Understanding Job Portals & Hiring Patterns

• How to Target 12 LPA Analytics Roles

• Technical Interview Preparation

• HR Interview Training

• Group Discussion (GD) Techniques

• Personal Branding & Communication Skills

• What is LinkedIn and How to Use It Effectively

• LinkedIn Profile Optimization

• How Recruiters Shortlist Candidates

• Mock Interviews & Feedback

📌 Outcome:
Students become job-ready, confident, and interview-prepared with a clear roadmap to high-paying roles.

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Key Highlights

• Zero coding prerequisite

• Designed for non-tech students

• 5 real-world projects

• Industry-relevant curriculum

• Interview & HR preparation included

• Certificate with QR code verification

• Career-focused training (no false placement promises)