Data Science Resources
Table of Contents
Programming Resources
Statistics & Mathematics
Machine Learning & Deep Learning
Data Wrangling & Cleaning
Data Visualization
Big Data & Cloud Computing
Model Deployment
Other Resources
Contributing
Programming Resources
Python: Tutorials, data science libraries (NumPy, pandas), and Jupyter Notebooks.
R: Guides for statistical analysis and data visualization.
SQL: Syntax, joins, aggregations, and advanced querying.
Data Structures & Algorithms: Essential foundations for data manipulation.
Statistics & Mathematics
Probability and Statistics: Resources for statistical inference, hypothesis testing, and regression.
Linear Algebra: Concepts and matrix operations applied in machine learning.
Calculus for Machine Learning: Tutorials on differential calculus and optimization.
Machine Learning & Deep Learning
Machine Learning Basics: Supervised and unsupervised learning, decision trees, and model evaluation.
Deep Learning: Neural networks, CNNs, RNNs, and transfer learning.
Hands-on Projects: Scikit-Learn, TensorFlow, and PyTorch projects.
Data Wrangling & Cleaning
Data Cleaning: Dealing with missing values and data transformations.
Data Wrangling: Handling CSV, JSON, SQL, and merging datasets.
Data Visualization
Matplotlib & Seaborn: Graphing techniques for insights.
Plotly & Tableau: Interactive visualizations and dashboards.
Best Practices: Storytelling with data and choosing the right visuals.
Big Data & Cloud Computing
Big Data Frameworks: Resources on Hadoop, Spark, and Hive.
Cloud Platforms: AWS, Google Cloud, and Azure for scalable data processing.
Model Deployment
Model Serving: Deploying with Flask, Django, and FastAPI.
MLOps: CI/CD for ML, Docker, and version control.
Other Resources
Online Courses: Top-rated courses from Coursera, edX, and Udacity.
Books & Articles: Covering fundamentals, theory, and applied practices.
Communities & Forums: Kaggle, Reddit, and Stack Overflow for networking and help.