100 Data Science Resources

Bonus: Excel, data analytics, and business analytics resources

Author

Shushant Lakhyani
September 30, 2024

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I hope you are well.

Here, you’ll get 100 data science resources diversified across newsletters, YouTube channels, blogs, podcasts, courses, and tools to use.

Here is a diversification of everything covered in data science:

Here is a diversification of subjects covered in data analysis, business analysis, and business analytics:

100 Excel Resources 

For people who want only Excel resources, you can get them all here: 👇

100 Microsoft Excel Resources

Get to know 100 Microsoft Excel resources with additional tools to use

technobizzvault.beehiiv.com/p/100-microsoft-excel-resources

Newsletters

  1. Data Elixir: A curated weekly digest of the latest data science news, tools, and resources.
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  2. Data Science Weekly: Comprehensive roundup of data science articles, tutorials, and job listings.
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  3. KDnuggets News: Long-running newsletter covering a wide range of data science topics.
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  4. DataTalks Club: Community-driven newsletter with updates on free courses, events, and projects.
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  5. Data Is Plural: Weekly newsletter featuring interesting and unique datasets.
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  6. The Analytics Engineering Roundup: The internet's most useful articles on analytics engineering and its adjacent ecosystem.
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  7. Data Science Central: A community for big data practitioners.
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  8. Data Engineering Weekly: The Weekly Data Engineering Newsletter.
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  9. Data News: A free weekly data engineering newsletter.
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  10. Metadata Weekly: Get your weekly dose of readings, resources, podcasts, memes, and some food for thoughts on the modern data stack.
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YouTube Channels

  1. FreeCodeCamp: In-depth coding tutorials including data science topics.
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  2. 365 Data Science: Structured courses on various data science topics.
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  3. codebasics: Programming tutorials with a focus on data science.
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  4. Alex The Analyst: Practical tutorials for aspiring data analysts.
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  5. Data Professor: Tutorials on data science, machine learning, and bioinformatics.
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  6. Sentdex: Python tutorials with a focus on machine learning, data, and AI.
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  7. Andreas Kretz: Data engineering and big data tutorials.
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  8. Programming with Mosh: Get the complete data science roadmap.
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  9. IBM Technology: Get to know the basics of what is data science.
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  10. Simplilearn: Get the data science full course for beginners.
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Blogs

  1. Towards Data Science: Diverse articles on data science topics.
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  2. KDnuggets: News, tutorials and opinions on data science.
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  3. Data Science Central: Community platform with wide-ranging content.
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  4. Analytics Vidhya: Tutorials and resources for data science learning.
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  5. R-bloggers: Aggregator of R-related articles and tutorials.
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  6. Machine Learning Mastery: Practical guides to machine learning.
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  7. Simply Statistics: Academic perspectives on data science and statistics.
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  8. Data Science Plus: Tutorials on R, Python, and data visualization.
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  9. Dataquest Blog: Data science career advice and learning resources.
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  10. Flowing Data: Data visualization techniques and examples.
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  11. Data Science 101: Introductory concepts for data science beginners.
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  12. Kaggle Blog: Competition insights and data science best practices.
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  13. Data School: Video tutorials and guides on data analysis.
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  14. FiveThirtyEight: Data-driven stories on politics, sports, and more.
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  15. FastML: Quick takes on machine learning algorithms.
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  16. No Free Hunch: Official Kaggle blog with competition analyses.
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  17. Variance Explained: Statistical analysis and R programming.
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  18. Probably Overthinking It: Data science concepts explained simply.
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  19. Dataconomy: News and trends in data science and AI.
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  20. Data Science Dojo: Tutorials and resources for aspiring data scientists.
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Podcasts

  1. Data Skeptic: Explores data related topics with a skeptical lens.
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  2. Linear Digressions: Covers machine learning and data science concepts.
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  3. DataFramed: Interviews with data science and tech leaders across industries.
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  4. Talking Machines: Discussions on machine learning and AI.
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  5. Data Stories: Focuses on data visualization and communication.
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  6. O'Reilly Data Show: Conversations with leaders in AI, big data, and data science.
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  7. Learning Machines 101: Introductory machine learning concepts.
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  8. Data Science at Home: Practical data science and AI applications.
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  9. SuperDataScience: Career advice and technical skills for data scientists.
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  10. Data Engineering Podcast: Focus on data engineering topics.
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Courses

  1. Data Science Specialization (Johns Hopkins University): Comprehensive introduction to data science.
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  2. Applied Data Science with Python Specialization (University of Michigan): Practical Python skills for data science.
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  3. IBM Data Science Professional Certificate: Complete data science curriculum from IBM.
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  4. Machine Learning (Stanford University): Foundational machine learning course by Andrew Ng.
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  5. Data Science MicroMasters (UC San Diego): Graduate-level introduction to data science.
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  6. Statistics and Data Science MicroMasters (MIT): Rigorous introduction to statistics and data science.
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  7. Professional Certificate in Data Science (Harvard University): Comprehensive data science program from Harvard.
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  8. Python for Data Science and Machine Learning Bootcamp (Udemy): Practical Python skills for data science and ML.
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  9. The Data Science Course 2024: Complete Data Science Bootcamp (Udemy)- Comprehensive data science bootcamp.
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  10. Data Science: Foundations using R Specialization (Johns Hopkins University)- Data science fundamentals with R.
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  11. Introduction to Data Science in Python (University of Michigan): Python basics for data science.
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  12. Data Science: R Basics (Harvard University)- Introduction to R for data science.
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  13. Introduction to Computer Science and Programming Using Python (MIT)- Python programming fundamentals.
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  14. Data Science Essentials (Microsoft)- Core concepts and tools in data science.
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  15. Data Science: Visualization (Harvard University) - Data visualization techniques and best practices.
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  16. Machine Learning with Python (IBM)- Practical machine learning using Python.
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  17. Deep Learning Specialization- Comprehensive deep learning curriculum.
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  18. Data Science: Probability (Harvard University)- Probability theory for data science.
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  19. SQL for Data Science (UC Davis): SQL fundamentals for data analysis.
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  20. Data Science: Inferential Statistics (Harvard University)- Statistical inference for data science.
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  21. Data Science: Machine Learning (Harvard University)- Machine learning fundamentals for data science.
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  22. Applied Machine Learning in Python (University of Michigan)- Practical machine learning with Python.
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  23. Data Science: Productivity Tools (Harvard University)- Essential tools for data science workflows.
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  24. Data Science: Linear Regression (Harvard University)- Linear regression techniques for data analysis.
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  25. Data Science: Wrangling (Harvard University)- Data cleaning and preparation techniques.
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  26. Data Science: Capstone (Harvard University)- Culminating project in data science.
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  27. Machine Learning Specialization (University of Washington)- Comprehensive machine learning curriculum.
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  28. Data Science Math Skills (Duke University)- Essential math for data science.
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  29. Introduction to Data Science (IBM)- Overview of data science concepts and applications.
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  30. Data Science: Inference and Modeling (Harvard University): Statistical modeling and inference.
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  31. Applied Data Science Capstone (IBM)- Hands-on data science project.
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  32. Data Science Methodology (IBM): Structured approach to data science projects.
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  33. Data Analysis and Interpretation Specialization (Wesleyan University)- Comprehensive data analysis curriculum.
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  34. Practical Machine Learning (Johns Hopkins University)- Applied machine learning techniques.
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  35. Data Science: Productivity Tools (Harvard University)- Essential tools for data science workflows.
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  36. Machine Learning for Data Science and Analytics (Columbia University)- ML fundamentals for data science.
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  37. Statistical Learning (Stanford University)- Statistical methods for machine learning.
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  38. Fundamentals of Data Analysis and Decision Making Specialization (Rice University - Data analysis for decision-making.
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  39. Data Science: Computational Thinking and Programming (UC Berkeley)- Computational approaches in data science.
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  40. Applied AI with DeepLearning (IBM)- Practical deep learning applications.
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  41. Data Mining Specialization (University of Illinois)- Comprehensive data mining curriculum.
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  42. Big Data Specialization (UC San Diego)- Big data concepts and technologies.
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  43. Data Visualization with Tableau Specialization (UC Davis)- Data visualization using Tableau.
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  44. Advanced Machine Learning Specialization (HSE University)- Advanced ML techniques and applications.
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  45. Probabilistic Graphical Models Specialization (Stanford University)- Graphical models in machine learning.
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  46. Data Structures and Algorithms Specialization (UC San Diego)- Essential CS concepts for data science.
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  47. Business Analytics Specialization (University of Pennsylvania)- Data analysis for business decisions.
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  48. Data Science at Scale Specialization (University of Washington)- Scalable data science techniques.
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  49. Applied Data Science with Python Specialization (University of Michigan)- Practical Python skills for data science.
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  50. Data Analysis and Presentation Skills: the PwC Approach Specialization- Data analysis and visualization skills.
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Tools

  1. Rows AI: Access the power of AI to analyze, summarize, and transform data. Build better spreadsheets, faster.
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  2. Julius AI: A powerful AI data analyst that helps you analyze and visualize your data. Chat with your data, create graphs, build forecasting models, and more.
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  3. Bricks: Create dashboards, reports, presentations & visuals in seconds — powered by AI.
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  4. DataLab: Write code, run analyses, and share your data insights. Go from data to insights in seconds, all from the comfort of your own web browser.
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  5. DataLine: AI-driven open source and privacy-first platform for data exploration.
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  6. Commabot: Use AI to view, edit and process your data with ease.
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  7. DataGPT: Ask DataGPT any question and get analyst-grade answers in seconds.
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  8. Numerous AI: Provides the simplest, most powerful and cost-effective solution for using ChatGPT inside Google Sheets and Excel.
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  9. Formula Bot: The single platform to analyze, visualize, transform and enrich your data, and so much more - powered by AI.
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  10. Turbular: Self-serve your company's data insights with AI. Get instant insights, build dashboards, generate reports, and ultimately make better-informed decisions for your business.
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I hope you’ve found a helpful resource here.

Have a great day!