Course Syllabus

Instructor

Joan Bruna, joan.bruna@berkeley.edu, 419 Evans

office hours: Fri 1pm-3pm

GSIs

201 and 203: Rebecca Barter, rebeccabarter@berkeley.edu, 446 Evans

office hours: Wed 9am-11am

202: Jonathan M Levy, jlstiles@berkeley.edu,  444-446 Evans 

office hours: Mon, Wed 4pm-6pm 

 

 

Class information

MWF 11am-noon, Evans 60 

Course Objectives

The course will introduce fundamental concepts in Mathematical Statistics and Statistical Inference,
and its applications to modern Data Analysis. We will pay special attention to the following aspects:

  • Survey Sampling: how to quantify the amount of information and uncertainty extracted from data.
  • Parametric Estimation: how to adjust parametric models to the data.
  • Hypothesis Testing: how to quantify the uncertainty of our estimates.
  • Linear Regression Models: how to model and explain (linear) dependencies in the data.

 

Materials

Reference books:

The theory will mostly follow John Rice's Mathematical Statistics and Data Analysis, 3rd edition, 2007. We will mostly cover Chapters 7 to 14. 

Other recommended books and notes are:

Slides and Course Notes:

They will be available here. 

Software 

either R or Python.

Prerequisites

Calculus and Probability at the level of Stat 134

Basic computer skills (coding and data manipulation).

 

Grading

Grades will be based on a midterm, a final exam, homework, and labs.

The midterm will count for 20% of the grade; the score on the midterm will be replaced by the score on the final if the latter is higher. If you miss the midterm, the score on it will be your score on the final (no alternative dates for the midterm).

The final exam will count 50%. There will be no alternative dates, so if you can't take the exam at this time, don't take the course. 

Homework will be assigned every week and will count 10%. Your two lowest homework grades will be dropped. Assignments will be posted in this page. Homework will be collected in class on Fridays. 

There will be 3 lab assignments (note that these are distinct from the exercises covered in the lab sections); this component of the course will count 20%. They require data analysis using the statistical software R and perhaps Python.

 

Exam dates 

Midterm: Friday, March 13th. 

Final: Tuesday, May 12th, 7-10pm.

 

Academic Honesty

You are encouraged to work together with others on the homework, but you must write up your own solutions.  The same applies to labs -- you must ultimately do your own coding.  So, for example, if a lab assignment involved taking a random sample, your random sample had better not be identical to any other in the class.   No collaboration is allowed on exams.  Cheating will be taken seriously and the penalties will be severe.

 

Course Summary:

Date Details