DATA SCIENCE

About Course

This Data Science program equips learners with the skills to analyze data, build predictive models, and extract insights using industry-standard tools and techniques. The first month covers theoretical concepts, while the second month focuses on real-world projects with mentor guidance.

What Will You Learn?

✅ Fundamentals of Data Science & AI
✅ Data Preprocessing & Feature Engineering
✅ Statistical Analysis & Exploratory Data Analysis (EDA)
✅ Supervised & Unsupervised Machine Learning
✅ Deep Learning & Neural Networks
✅ Data Visualization & Storytelling
✅ Industry Tools: Python, Pandas, NumPy, Matplotlib, Scikit-learn
✅ Hands-on Industry Projects

Material Includes

📚 LMS Access for Self-paced Learning
📜 2 Industry-recognized Certificates
📂 Real-world Data Science Projects
📝 Weekly Assignments

Course Curriculum

Month 1: Theory & Concepts (Monday – Friday Classes)

Module 1: Introduction to Data Science

Module 2: Data Preprocessing & Feature Engineering

Module 3: Exploratory Data Analysis (EDA) & Statistical Analysis

Module 4: Supervised Learning

Module 5: Unsupervised Learning & Clustering

Module 6: Introduction to Deep Learning & AI

Module 7: Data Science for Business Intelligence & Model Deployment

Month 2: Industry-Level Project (Mentor-Guided)

Project 1: Customer Churn Prediction

Project 2: Sentiment Analysis using NLP

Your Instructors

mojeshbondu1@gmail.com

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This course includes:

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mojeshbondu1@gmail.com

March 15, 2025

About Course

What Will You Learn?

Material Includes

Course Curriculum

Month 1: Theory & Concepts (Monday – Friday Classes)

Module 1: Introduction to Data Science

What is Data Science?

AICTE & NSDC Guidelines in Data Science Training

Role of a Data Scientist in the Industry

Module 2: Data Preprocessing & Feature Engineering

Handling Missing Data & Outliers

Data Cleaning & Transformation

Feature Selection & Dimensionality Reduction

Module 3: Exploratory Data Analysis (EDA) & Statistical Analysis

Descriptive & Inferential Statistics

Data Visualization using Matplotlib & Seaborn

Correlation, Hypothesis Testing, and Probability Theory

Module 4: Supervised Learning

Regression (Linear, Polynomial, Ridge, Lasso)

Classification (Logistic Regression, Decision Trees, Random Forest)

Model Evaluation: Accuracy, Precision-Recall, Confusion Matrix

Module 5: Unsupervised Learning & Clustering

K-Means, Hierarchical Clustering

Principal Component Analysis (PCA)

Association Rule Learning

Module 6: Introduction to Deep Learning & AI

Basics of Neural Networks & Deep Learning

Convolutional Neural Networks (CNNs)

Natural Language Processing (NLP) Fundamentals

Module 7: Data Science for Business Intelligence & Model Deployment

Industry Use Cases & Applications

Cloud Deployment (AWS/GCP)

Resume Building & Interview Preparation

Month 2: Industry-Level Project (Mentor-Guided)

Project 1: Customer Churn Prediction

Analyzing Customer Data & Behavior

Implementing Machine Learning Models for Churn Prediction

Project 2: Sentiment Analysis using NLP

Collecting & Preprocessing Text Data

Implementing Sentiment Analysis Models

Your Instructors

mojeshbondu1@gmail.com

This course includes:

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