Data science, data engineering, data analysis, and machine learning are part of the recent massive growth of Python.

But really what is data science?

Vicki Boykis helps me understand questions like:

• No really, what is data science?
• What does a data pipeline look like?
• What is it like to do data science, data analysis, data engineering?
• Can you do analysis on a laptop?
• How big does data have to be to be considered big?
• What are the challenges in data science?
• Does it make sense for software engineers to learn data engineering, data science, pipelines, etc?
• How could someone start learning data science?

Also covered:

• A type work (analysis) vs B type work (building)
• data lakes and data swamps
• predictive models
• data cleaning
• development vs experimentation
• Jupyter Notebooks
• Kaggle
• ETL pipelines

I learned a lot about the broad field of data science from talking with Vicki.

Special Guest: Vicki Boykis.

Links:

• How to Lie with Statistics : Darrell Huff
• Should you replace Hadoop with your laptop?
• Kaggle
• Project Jupyter
• Soviet Art Bot — A bot that finds socialist realism paintings and tweets them out