Data Science Detailed Roadmap
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| | |-- Fundamentals
| |-- Introduction to Data Science
| | |-- What is Data Science?
| | |-- Roles: Analyst vs Scientist vs Engineer
| | |-- Data Science Workflow
| |-- Math and Statistics
| | |-- Descriptive & Inferential Statistics
| | |-- Probability Theory
| | |-- Linear Algebra & Calculus Basics
| |-- Programming for Data Science
| |-- Python
| | |-- Variables, Loops, Functions
| | |-- NumPy, Pandas, Matplotlib, Seaborn
| |-- R Programming (Optional but Useful)
| | |-- Data Manipulation with dplyr, tidyr
| | |-- Visualization with ggplot2
| |-- SQL
| | |-- SELECT, WHERE, GROUP BY, JOINS
| | |-- Subqueries and Window Functions
| |-- Data Wrangling & Preprocessing
| |-- Cleaning and Handling Missing Data
| |-- Data Transformation & Encoding
| |-- Feature Engineering
| |-- Working with APIs and Web Scraping
| |-- Data Visualization
| |-- Exploratory Data Analysis (EDA)
| |-- Visualization Tools
| | |-- Python: Seaborn, Plotly
| | |-- BI Tools: Power BI, Tableau
| |-- Machine Learning
| |-- Supervised Learning
| | |-- Linear Regression
| | |-- Classification (Logistic Regression, Decision Trees, SVM)
| |-- Unsupervised Learning
| | |-- Clustering (K-Means, DBSCAN)
| | |-- Dimensionality Reduction (PCA, t-SNE)
| |-- Model Evaluation
| | |-- Cross-validation, Confusion Matrix
| | |-- ROC-AUC, Precision, Recall, F1 Score
| |-- Deep Learning & Neural Networks
| |-- Introduction to Neural Networks
| |-- Frameworks: TensorFlow, Keras, PyTorch
| |-- CNNs for Image Data
| |-- RNNs & LSTMs for Time Series / Text
| |-- Projects & Real-World Applications
| |-- End-to-End ML Projects
| |-- Kaggle Competitions
| |-- Case Studies (Retail, Finance, Healthcare)
| |-- Big Data & Cloud Tools | |-- Introduction to Big Data
| | |-- Hadoop, Spark
| |-- Cloud Platforms
| | |-- AWS, GCP, Azure (S3, EC2, BigQuery, SageMaker)
| |-- Data Engineering Basics
| |-- ETL Pipelines
| |-- Workflow Automation with Airflow
| |-- Data Warehousing (Snowflake, Redshift)
| |-- Natural Language Processing (NLP)
| |-- Text Preprocessing
| |-- Bag of Words, TF-IDF
| |-- NLP Libraries (NLTK, spaCy)
| |-- Transformers (BERT, GPT)
| |-- Time Series Analysis
| |-- Trends, Seasonality, Forecasting
| |-- ARIMA, Prophet
| |-- LSTM for Time Series
| |-- Model Deployment
| |-- Building Web Apps (Streamlit, Flask)
| |-- Model Serialization (Pickle, joblib)
| |-- Deploy to Cloud (Heroku, AWS, GCP)
| |-- Soft Skills & Career Prep
| |-- Resume Projects and Portfolio
| |-- Git and GitHub for Version Control
| |-- Interview Preparation
| |-- Communication & Storytelling with Data
| |-- Bonus Topics
| |-- Reinforcement Learning Basics
| |-- Ethics in AI & Data Privacy
| |-- MLOps and CI/CD for Data Science
| |-- Community & Growth
| |-- Kaggle, GitHub, LinkedIn
| |-- Contributing to Open Source
| |-- Blogging / Sharing Your Learnings

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