This course, advanced DS 401, provides intensive instruction and participation. The meticulously selected latest edition of the course combines a robust set of resources including archives, exercises, interactive activities and engaging lectures, making it a stimulating and engaging learning experience. The course includes a comprehensive review of basic concepts, particularly the central limit theorem, confidence intervals, regression analysis, model building, and several other related topics. In addition, the course seamlessly combines in-depth theoretical understanding with practical skills through comprehensive Jupyter notebook implementations and hands-on applications in a variety of fields.

It is anticipated that you should spend on average 3 - 6 hours of work per week beyond the classroom time to be competitive in this class.

Here some extra information:

Find this course in Moodle.

Bibliography and references

[1] Diez, D. OpenIntro Statistics. Edition 4th. Source. Github

Extra material

[1] Harvard Github