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
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