1.Introduction to Data Science

What is Data Science

Applications and Real-world Use Cases

Data Science vs Data Analytics vs Machine Learning vs AI

Required Skills and Roadmap

2.Python for Data Science or R if preferred

Python Basics including Syntax Variables and Data Types

Control Flow including Loops Conditionals and Functions

Object-Oriented Programming OOP

File Handling including CSV JSON and Excel

3.Libraries for Data Science

NumPy for Arrays and Numerical Computing

Pandas for Data Manipulation and Analysis

Matplotlib and Seaborn for Data Visualization

4.Data Handling and Preprocessing

Data Collection including APIs and Web Scraping

Data Cleaning including Handling Missing Data Outliers and Duplicates

Data Transformation including Encoding Categorical Data Scaling and Normalization

Feature Engineering including Creating New Features

Exploratory Data Analysis EDA

5.Mathematics and Statistics for Data Science

Descriptive Statistics including Mean Median Mode Variance and Standard Deviation

Probability Theory including Bayes Theorem and Probability Distributions

Hypothesis Testing including T-test and Chi-square Test

Inferential Statistics including Regression Analysis and Correlation

Linear Algebra including Matrices Vectors Eigenvalues and PCA

Calculus including Derivatives and Gradients for Optimization

6.Machine Learning including Supervised and Unsupervised

Supervised Learning

Linear Regression and Multiple Regression

Logistic Regression

Decision Trees and Random Forest

Support Vector Machines SVM

Naïve Bayes Classifier

K-Nearest Neighbors KNN

Evaluation Metrics including RMSE R2 Precision Recall and F1-score

Unsupervised Learning

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis PCA

Anomaly Detection

Model Optimization and Tuning

Feature Selection

Hyperparameter Tuning including Grid Search and Random Search

Cross-Validation Techniques

7.Deep Learning and Neural Networks

Introduction to Neural Networks

Activation Functions including ReLU Sigmoid and Softmax

Backpropagation and Optimization including Gradient Descent and Adam

Deep Learning Frameworks including TensorFlow PyTorch and Keras

Convolutional Neural Networks CNNs

Image Classification and Object Detection

Transfer Learning using Pre-trained Models like VGG and ResNet

Recurrent Neural Networks RNNs and NLP

Sequence Modeling including LSTM and GRU

Sentiment Analysis

Text Generation and Chatbots using Transformers BERT and GPT

8.Time Series Analysis

ARIMA and SARIMA

Exponential Smoothing

LSTMs for Time Series Forecasting

9.Natural Language Processing NLP

Tokenization and Text Preprocessing

Word Embeddings using Word2Vec and GloVe

Named Entity Recognition NER

Sentiment Analysis

Transformer Models including BERT and GPT

10.Big Data and Cloud Computing

Introduction to Big Data Technologies

Hadoop and Spark for Data Processing

Cloud Platforms including AWS Google Cloud and Azure

Deploying Models on Cloud

11.Deployment & Model Productionization

Saving and Loading Models using Pickle and Joblib

API Creation using Flask or FastAPI

Model Deployment using Docker and Kubernetes

MLOps including CI and CD for Machine Learning

12.Advanced Topics optional but useful

Reinforcement Learning

Generative Adversarial Networks GANs

AutoML

Ethical AI and Bias in Machine Learning

13.Real-World Projects and Case Studies

• End-to-End Data Science Project
• Kaggle Competitions
• Business Intelligence and Decision Making

This roadmap covers everything from beginner to expert level in Data Science Let me know if you need further details or a structured learning plan