Data Science Course Syllabus

This course is designed to introduce learners to the fundamentals of Data Science, including data analysis, machine learning, and practical applications using Python.

Duration: 3 Months (12 Weeks)

Week 1-2: Introduction to Data Science & Python

- What is Data Science?

- Python Basics

- Libraries: Pandas, NumPy, Matplotlib

- Jupyter Notebook

Week 3-4: Data Wrangling & Exploratory Data Analysis

- Data Cleaning

- Handling Missing Values

- Data Visualization using Seaborn

- Feature Engineering

Week 5-6: Statistics & Probability for Data Science

- Descriptive & Inferential Statistics

- Probability Distributions

- Hypothesis Testing

- Correlation & Regression

Week 7-8: Machine Learning Basics

- Supervised vs Unsupervised Learning

- Linear Regression

- Logistic Regression

- Decision Trees & Random Forest

Week 9-10: Advanced Machine Learning

- Support Vector Machines

- Clustering (K-Means, Hierarchical)

- Neural Networks Basics

- Model Evaluation Metrics

Week 11: Real-world Applications & Deployment

- Building a Data Science Project

- Model Deployment using Flask

- Version Control with Git

- Using Cloud Platforms

Week 12: Capstone Project & Career Guidance

- Final Project

- Resume Building

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Interview Preparation

- Q&A Session

📚 Data Science Course Syllabus – 3 Months (12 Weeks) Program

This course is designed to introduce learners to Data Science fundamentals, including data analysis, machine learning, and real-world applications using Python.

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🗓 Course Duration: 3 Months (12 Weeks)

📌 Week 1-2: Introduction to Data Science & Python

What is Data Science?

Python Basics & Syntax

Libraries: Pandas, NumPy, Matplotlib

Working with Jupyter Notebook

📌 Week 3-4: Data Wrangling & Exploratory Data Analysis (EDA)

Data Cleaning & Preprocessing

Handling Missing Values

Feature Engineering – Preparing Data for ML

Data Visualization using Seaborn

📌 Week 5-6: Statistics & Probability for Data Science

Descriptive & Inferential Statistics

Probability Distributions (Normal, Binomial, Poisson)

Hypothesis Testing & Confidence Intervals

Correlation & Regression Analysis

📌 Week 7-8: Machine Learning Basics

Supervised vs Unsupervised Learning

Linear & Logistic Regression

Decision Trees & Random Forest

Overfitting & Regularization Techniques

📌 Week 9-10: Advanced Machine Learning

Support Vector Machines (SVM)

Clustering (K-Means, Hierarchical)

Introduction to Neural Networks

Model Evaluation Metrics (Confusion Matrix, Precision, Recall, F1 Score, ROC Curve)

📌 Week 11: Real-World Applications & Model Deployment

Building a Full Data Science Project

Model Deployment with Flask

Version Control with Git

Using Cloud Platforms (AWS, GCP, Azure)

📌 Week 12: Capstone Project & Career Preparation

Final Hands-on Data Science Project

Resume Building & Portfolio Development

Interview Preparation (Real-world Q&A)

Live Q&A and Doubt Clearing Session

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📌 This structured curriculum ensures you gain hands-on experience and job-ready skills in just 12 weeks!

📩 Interested? DM for details on enrollment!