Data Science Roadmap | |-- Fundamentals | |-- Mathematics | | |-- Linear Algebra | | |-- Calculus | | |-- Probability and Statistics | | | |-- Programming | | |-- Python | | |-- R | | |-- SQL | |-- Data Collection and Cleaning | |-- Data Sources | | |-- APIs | | |-- Web Scraping | | |-- Databases | | | |-- Data Cleaning | | |-- Missing Values | | |-- Data Transformation | | |-- Data Normalization | |-- Data Analysis | |-- Exploratory Data Analysis (EDA) | | |-- Descriptive Statistics | | |-- Data Visualization | | |-- Hypothesis Testing | | | |-- Data Wrangling | | |-- Pandas | | |-- NumPy | | |-- dplyr (R) | |-- Machine Learning | |-- Supervised Learning | | |-- Regression | | |-- Classification | | | |-- Unsupervised Learning | | |-- Clustering | | |-- Dimensionality Reduction | | | |-- Reinforcement Learning | | |-- Q-Learning | | |-- Policy Gradient Methods | | | |-- Model Evaluation | | |-- Cross-Validation | | |-- Performance Metrics | | |-- Hyperparameter Tuning | |-- Deep Learning | |-- Neural Networks | | |-- Feedforward Networks | | |-- Backpropagation | | | |-- Advanced Architectures | | |-- Convolutional Neural Networks (CNN) | | |-- Recurrent Neural Networks (RNN) | | |-- Transformers | | | |-- Tools and Frameworks | | |-- TensorFlow | | |-- PyTorch | |-- Natural Language Processing (NLP) | |-- Text Preprocessing | | |-- Tokenization | | |-- Stop Words Removal | | |-- Stemming and Lemmatization | | | |-- NLP Techniques | | |-- Word Embeddings | | |-- Sentiment Analysis | | |-- Named Entity Recognition (NER) | |-- Data Visualization | |-- Basic Plotting | | |-- Matplotlib | | |-- Seaborn | | |-- ggplot2 (R) | | | |-- Interactive Visualization | | |-- Plotly | | |-- Bokeh | | |-- Dash | |-- Big Data | |-- Tools and Frameworks | | |-- Hadoop | | |-- Spark | | | |-- NoSQL Databases | |-- MongoDB | |-- Cassandra | |-- Cloud Computing | |-- Cloud Platforms | | |-- AWS | | |-- Google Cloud | | |-- Azure | | | |-- Data Services | |-- Data Storage (S3, Google Cloud Storage) | |-- Data Pipelines (Dataflow, AWS Data Pipeline) | |-- Model Deployment | |-- Serving Models | | |-- Flask/Django | | |-- FastAPI | | | |-- Model Monitoring | |-- Performance Tracking | |-- A/B Testing | |-- Domain Knowledge | |-- Industry-Specific Applications | | |-- Finance | | |-- Healthcare | | |-- Retail | |-- Ethical and Responsible AI | |-- Bias and Fairness | |-- Privacy and Security | |-- Interpretability and Explainability | |-- Communication and Storytelling | |-- Reporting | |-- Dashboarding | |-- Presentation Skills | |-- Advanced Topics | |-- Time Series Analysis | |-- Anomaly Detection | |-- Graph Analytics | |-- *PH4N745M* └-- Comments |-- # Single-line comment (Python) └-- /* Multi-line comment (Python/R) */