• What do Hiring Managers Look For in a Data Scientist’s CV?
• Interesting books:
  • Python Data Science Handbook; Python Data Science Handbook @ SFU Library
  • Python for Data Analysis; Python for Data Analysis @ SFU Library
  • Data Science from Scratch; Data Science from Scratch @ SFU Library
  • Think Like a Data Scientist; Think Like a Data Scientist @ SFU Library
• Python tutorials (for non-Python programmers):
  • Python for Programmers, collection of links from the Python wiki.
  • Learn Python Programming: The Definitive Guide.
  • Python for Programmers, in “Python 3 Patterns, Recipes and Idioms”.
• Python Data Science Handbook at GitHub

References

• NumPy reference
• Pandas documentation
  • Pandas API Reference
• SciPy Reference
• StatsModels documentation
• Scikit-learn user guide
  • Scikit-learn reference
• Spark DataFrames guide
  • Spark SQL/DataFrames reference

Data Sets

Some data sources that may be interesting:

Available on the Cluster

• Reddit Comments and Submissions
• GHCN global weather data
• OpenStreetMaps planet data dump
• Wikidata dump
• Wikipedia database downloads

Other Data Sets and Sources

• Stack Exchange Data Dump [25GB]
• Yelp Academic Data Set
• Statistics Canada Developer resources
• Public data sets on AWS S3
• Archive.org data set collection
• Academic Torrents: “making [many] TB of research data available”
• Open Science Data Cloud: “Repository for public data sets of scientific interest”
• Great Github list of public data sets
• Canada Open Government
• Opendatasoft
• Machine learning data sources:
  • mldata.org
  • UCI Machine Learning Repository
  • KDnuggets dataset list
  • Kaggle

Extras

• Data Science Workflow: Overview and Challenges
• For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights
• 2017 The State of Data Science & Machine Learning
• One Data Science Job Doesn’t Fit All (at LinkedIn)
• Andrew Ng's Coursera course "Machine Learning"
• Berkeley DS 100, Principles and Techniques of Data Science