Curated Resources

My personally vetted collection of data science tools, learning materials, and industry resources. These are the resources I actually use and recommend based on 8+ years of experience bridging academic research and production ML systems.

🛠️ Essential Tools

Programming Languages

• Python - Most popular language for data science
• R - Excellent for statistics and data analysis
• SQL - Essential for data querying
• Scala - For big data processing with Spark

Data Manipulation & Analysis

• Pandas - Data manipulation in Python
• NumPy - Numerical computing
• dplyr - Data manipulation in R
• Apache Spark - Big data processing

Machine Learning Libraries

• Scikit-learn - General-purpose ML library
• TensorFlow - Deep learning framework
• PyTorch - Research-focused deep learning
• XGBoost - Gradient boosting framework

Data Visualization

• Matplotlib - Basic plotting in Python
• Seaborn - Statistical visualization
• Plotly - Interactive visualizations
• Tableau - Business intelligence platform
• Power BI - Microsoft’s BI tool

Development Environment

• Jupyter Notebooks - Interactive development
• Google Colab - Cloud-based notebooks
• VS Code - Versatile code editor
• PyCharm - Python IDE
• RStudio - R development environment

📊 Datasets

Public Dataset Repositories

• Kaggle Datasets - Wide variety of datasets
• UCI ML Repository - Classic ML datasets
• Google Dataset Search - Search engine for datasets
• AWS Open Data - Public datasets on AWS
• Data.gov - US government open data

Domain-Specific Datasets

• Papers With Code - Research datasets
• Financial Data - Stock market data
• Healthcare - Health and medical data
• Climate Data - Environmental datasets
• Social Media APIs - Real-time social data

Sample Datasets for Learning

• Boston Housing - Regression
• Iris Dataset - Classification
• Titanic - Binary classification
• MNIST - Image classification

📚 Learning Resources

Books

Beginner

• “Python for Data Analysis” by Wes McKinney
• “Hands-On Machine Learning” by Aurélien Géron
• “Data Science from Scratch” by Joel Grus

Intermediate

• “The Elements of Statistical Learning” by Hastie, Tibshirani & Friedman
• “Pattern Recognition and Machine Learning” by Christopher Bishop
• “Feature Engineering for Machine Learning” by Alice Zheng

Advanced

• “Deep Learning” by Ian Goodfellow
• “Bayesian Data Analysis” by Andrew Gelman
• “Causal Inference: The Mixtape” by Scott Cunningham

Online Courses

• Coursera Data Science Specialization
• Fast.ai - Practical deep learning
• edX MIT Introduction to Data Science
• Udacity Data Scientist Nanodegree

YouTube Channels

• StatQuest - Statistics explained simply
• 3Blue1Brown - Math visualization
• Two Minute Papers - Latest AI research
• Sentdex - Python tutorials

Blogs & Publications

• Towards Data Science
• KDnuggets
• Distill - Clear explanations of ML concepts
• Google AI Blog
• OpenAI Blog

🎯 Practice Platforms

Competitive Programming

• Kaggle - Data science competitions
• DrivenData - Social good competitions
• Analytics Vidhya - Hackathons and challenges

Coding Practice

• LeetCode - Algorithm challenges
• HackerRank - Programming skills
• Codewars - Coding kata

Project-Based Learning

• GitHub - Open source projects
• GitLab - Alternative to GitHub
• Jupyter Notebooks - Browse shared notebooks

🏢 Industry Resources

Job Boards

• Indeed - General job search
• LinkedIn - Professional networking
• AngelList - Startup positions
• Glassdoor - Company reviews and salaries

Professional Communities

• Reddit r/MachineLearning
• Stack Overflow
• Data Science Central
• Meetup - Local data science groups

Conferences & Events

• KDD - Knowledge Discovery and Data Mining
• ICML - International Conference on Machine Learning
• NeurIPS - Neural Information Processing Systems
• Strata Data Conference - Industry-focused

📈 Staying Updated

Newsletters

• Data Science Weekly
• O’Reilly Data Newsletter
• The Batch by deeplearning.ai

Podcasts

• Linear Digressions
• Data Skeptic
• Not So Standard Deviations
• The Data Science Podcast

Research Papers

• arXiv - Preprint repository
• Google Scholar - Academic search
• Papers With Code - Papers with implementations

💡 Tips for Using These Resources

1. Start with fundamentals - Don’t jump to advanced topics too quickly
2. Practice regularly - Consistent practice is better than intensive bursts
3. Build projects - Apply what you learn to real problems
4. Join communities - Learn from others and get help when stuck
5. Stay current - Follow industry trends and new developments
6. Focus on quality - Better to master a few tools well than know many superficially

🎯 My Personal Recommendations

Having worked with these tools in both academic research (University of Melbourne) and high-stakes production environments at a leading Australian sports technology company, here are my top picks:

• For Statistical Computing: R + RStudio for complex statistical analysis, Python for production
• For Production ML: Azure ML + Docker + Kubernetes for scalable systems
• For Research: Jupyter notebooks + Git + scientific Python stack
• For Learning: Start with Python → pandas → scikit-learn → gradually add specialized tools

Questions about any of these resources? Feel free to reach out - I’m happy to share more detailed experiences.