Hacker Stats in Python (Part 2)

Join us for this live, hands-on training where you will sharpen your skills in statistical inference by exploring multiple facets of a rich data set. You will perform exploratory data analysis, make informative graphics, compute confidence intervals, and perform hypothesis tests, all using a "hacker stats" approach with Python. The session will run three hours, taking you from start to finish of a statistical analysis. There will be breaks and plenty of opportunities to ask questions throughout the training. Attendance in the Hacker Stats in Python (Part I) live training is useful but not necessary.

What will I learn?

You will learn how to:

• Create effective visualizations of empirical distributions

• Use random number generation as an engine for statistical inference ("hacker stats")

• Compute confidence intervals of relevant summary statistics

• Perform null hypothesis significance testing

What should I prepare?

Please note, a Gmail account is required in order to use Colaboratory, a free Jupyter notebook environment. You can join the webinar from your web browser following the instructions you receive in your registration email. All required data/resources will be provided in the training.

Who should attend?

This course is open to all DataCamp Premium learners, looking to hone their skills in statistical inference.

• Statistical Thinking in Python I

• Statistical Thinking in Python II

• Case Studies in Statistical Thinking

Resources

• Slides

• Student Notebook

• Solution Notebook

Justin Bois

Lecturer at the California Institute of Technology