Hi everyone!
I wanted to share an update on our study group which is working on the IBM AI Enterprise Workflow specialization on Coursera. We recently completed Course 2 of the sequence.
The focus of the first week of the course is exploratory data analysis (EDA) and data visualization, with the objectives that learners:
- Understand the key steps in exploratory data analysis
- Refresh ourselves on key Python tools for EDA (pandas, matplotlib, and Jupyter)
- Explore strategies for dealing with missing data
- Appreciate the role of communication in EDA
The information on strategies for handling missing data was particularly interesting.
If you missed the session, you can catch the recording here:
The second week of the course focused on estimation and null hypothesis testing, as well as:
- Creating simple dashboards in Watson Studio
- Employing common distributions to answer questions about event probabilities
- Applying null hypothesis testing as an investigative tool using Python
- Explaining several methods for dealing with multiple testing
The video from our session covers all these and more!
We're currently working on course 3 in the sequence and we would love for you to
join us.
I'm happy to answer any questions in the comments.
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Sam Charrington
Founder, TWIML
Host, TWIML AI Podcast fka This Week in Machine Learning & AI
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