Below I share a list of resources I find valuable to get necessary skills for Data Analysts and Data Scientists. I got many questions from people about it and I hope these resources will be useful for those who want to get started in data science or data analytics.
Last update: 2022-03-25.
- Data Analyst Nanodegree by Udacity: covers the data analysis process of wrangling, exploring, analyzing, and communicating data, separate part on practical statistics and experimentation. Lots of practical cases with python.
- It costs money, but there are discount periods.
- Intro to Data Analysis by Udacity: intro to data analysis with numpy and pandas, free.
- Master Reporting Automation with Google Sheets: course by colleague from Bolt – learn business reporting and modeling with spreadsheets.
- Use code #KNOWLEDGEISFREE to get 3 days free access to the course
- Machine Learning by Anrdew Ng on Coursera: Old, but Gold, gives a good theoretical background on ML.
- Open Machine Learning CourseOpen Machine Learning Course by OpenDataScience:
- Open ML course, the Russian version of the previous course with active session (not self-paced)
- Basic SQL on codecademy, free.
- Analyzing Big Data with SQL by Coursera: for those with basic knowledge of SQL and looking forward to elevating their skills
- Data Elixir: more analytics focused.
- Data Science Weekly: Data Science news, articles.
- The batch: Andrew Ng and his team share recent research, more advanced.
Advanced Data Science topics
- Introduction to Causal Inference: basics of causality theory.
- Machine Learning with Graphs by Stanford: fundamentals of applying ML to Graph theory and represantation learning. Includes applications in different areas: robustness and fragility of food webs and financial markets, algorithms for the World Wide Web, identification of functional modules in biological networks, disease outbreak detection.