Data is the first step to any analysis and this article from Forbes reminds us that having good data is also a critical step to developing and maintaining an artificial intelligence capability.
These days, data warehousing feels like something from ages ago, but Computer Weekly connects the two.
Here’s a ZDNet summary of a McKinsey article about artificial intelligence and the highlight is that most of us have a lot of work to do to first build the base on which our artificial intelligence can work.
Hackernoon highlights the top 10 roles involved in AI and data science:
- data engineer
- decision maker
- expert analyst
- applied machine learning engineer
- data scientist
- analytics manager
- qualitative expert
See what FastAI has to say about how to structure your data science and data engineering teams.
Here’s an article from Science Alert about the scientific method.
Check out this list from Mode Analytics for some suggestions about Python libraries to help with formatting and cleaning data.
Here’s an article from Tableau about data prep tools.
Here’s an article from Digiday about how the Financial Times does data visualization.
Check out this article about how to boost your data munging with R.