Data Science

Module 1: Introduction to Data Science

✅ Overview of Data Science

✅ Data Science lifecycle

✅ Industry use cases

✅ Career opportunities & roles

✅ Tools used in Data Science

✅ Python fundamentals

✅ Data types, loops, and functions

✅ NumPy & Pandas

✅ Data handling & file operations

✅ Jupyter Notebook

✅ Descriptive statistics

✅ Probability & distributions

✅ Correlation & covariance

✅ Hypothesis testing

✅ Basics of linear algebra

✅ Data sources (CSV, Excel, APIs, Databases)

✅ Handling missing values

✅ Outlier detection

✅ Data transformation

✅ Feature engineering

✅ Data visualization techniques

✅ Matplotlib & Seaborn

✅ Pattern identification

✅ Insight generation

✅ Data storytelling

✅ Database fundamentals

✅ SQL queries (CRUD operations)

✅ Joins & subqueries

✅ Connecting SQL with Python

✅ Machine Learning concepts

✅ Supervised vs Unsupervised learning

✅ Model training & testing

✅ Evaluation metrics

✅ Linear Regression

✅ Logistic Regression

✅ Decision Trees

✅ Random Forest

✅ K-Nearest Neighbors (KNN)

✅ K-Means clustering

✅ Hierarchical clustering

✅ Principal Component Analysis (PCA)

✅ Feature selection

✅ Hyperparameter tuning

✅ Cross-validation

✅ Overfitting & underfitting

✅ Neural network basics

✅ TensorFlow / Keras

✅ Simple deep learning models