DATA SCIENCE SYLLABUS – 6 MONTHS

Month 1: Programming Foundations (Python for Data Science)

* Introduction to Data Science & Career Scope

* Python Basics

* Variables, Data Types, Operators

* Conditional Statements

* Loops

* Functions & Modules

* Data Structures

* Lists, Tuples, Sets, Dictionaries

* File Handling (CSV, Text Files)

* Exception Handling

* Introduction to Jupyter Notebook & Anaconda

---

Month 2: Mathematics & Statistics for Data Science

* Basics of Mathematics

* Linear Equations

* Matrices & Vectors

* Descriptive Statistics

* Mean, Median, Mode

* Variance & Standard Deviation

* Probability Concepts

* Data Distribution

* Normal Distribution

* Skewness & Kurtosis

* Inferential Statistics

* Sampling

* Hypothesis Testing

* Correlation & Covariance

---

Month 3: Data Analysis & Visualization

* NumPy

* Arrays, Indexing, Slicing

* Mathematical Operations

* Pandas

* Series & DataFrames

* Data Import (CSV, Excel)

* Data Cleaning & Preprocessing

* Handling Missing Data

* Data Aggregation & Grouping

* Data Visualization

* Matplotlib

* Seaborn

* Exploratory Data Analysis (EDA)

---

Month 4: Machine Learning – Fundamentals

* Introduction to Machine Learning

* Types of Machine Learning

* Supervised

* Unsupervised

* Supervised Learning Algorithms

* Linear Regression

* Multiple Regression

* Logistic Regression

* K-Nearest Neighbors (KNN)

* Decision Trees

* Model Evaluation

* Train-Test Split

* Accuracy, Precision, Recall, F1-Score

---

Month 5: Advanced Machine Learning

* Ensemble Techniques

* Random Forest

* Gradient Boosting

* Support Vector Machines (SVM)

* Unsupervised Learning

* K-Means Clustering

* Hierarchical Clustering

* Dimensionality Reduction

* PCA

* Feature Engineering

* Hyperparameter Tuning

* Cross Validation

---

Month 6: Real-World Applications & Projects

* Introduction to Deep Learning

* Neural Networks Basics

* Natural Language Processing (NLP)

* Text Cleaning

* Tokenization

* Sentiment Analysis

* Time Series Analysis (Basics)

* Data Science Project Lifecycle

* End-to-End Projects

* Sales Prediction

* Customer Segmentation

* Fraud Detection

* Recommendation System

* Deployment Basics

* Flask / Streamlit

* Resume Preparation & Interview Questions

---

Tools & Technologies Covered

* Python

* NumPy, Pandas

* Matplotlib, Seaborn

* Scikit-learn

* Jupyter Notebook

---

Outcome After 6 Months

✔ Strong Python & Statistics foundation

✔ Hands-on Machine Learning experience

✔ Real-time projects

✔ Job-ready Data Science skills