Understanding and Visualizing Data with Python

This "Understanding and Visualising Data with Python" training offers:

1. lecture videos teaching you concepts

2. graded quizzes

3. a graded assignment where you have to create a survey design

4. Jupyter notebooks with exercises for you to explore statistical concepts in Python

5. walkthrough videos on Jupyter notebook exercises if you need some help to unblock yourself or when you want to understand why certain things were done

The training was alittle lengthy but well worth the time. At times, because concepts can be explained in long sentences, you may need to rewind and revisit certain parts of the videos to get the full meaning of what has been explained.

Overall, this training refreshed my understanding of:

1. basic statistical concepts - statistical measures, population, sampling

2. using numpy, matplotlib, seaborn, scipy packages in Jupyter notebooks (which was good because I currently dont code in Python at work)

This training also explained practical ideas such as:

1. stratifying, clustering, why these concepts are important when sampling

2. issues with certain sampling approaches

3. useful ways to turn a non-probability sample into a probability sample, so that the analysis/claims you present would be grounded in a more solid basis.

Points 2 and 3 in the list above were neither covered in school nor statistics texts in the past. So like me, you may get the chance to learn something new to apply to your work.