Semester.ly

Johns Hopkins University | EN.550.211

Probability and Statistics for The Life Sciences

4.0

credits

Average Course Rating

(3.26)

This is an introduction to statistics aimed at students in the life sciences. The course will provide the necessary background in probability with treatment of independence, Bayes theorem, discrete and continuous random variables and their distributions. The statistical topics covered will include sampling and sampling distributions, confidence intervals and hypothesis testing for means, comparison of populations, analysis of variance, linear regression and correlation. Analysis of data will be done using Excel.

Spring 2013

(3.08)

Spring 2014

(3.18)

Spring 2015

(3.52)

Spring 2013

Professor: Bruno Jedynak

(3.08)

The best aspects of the course included the helpful teaching assistants, wide-range of statistic applications lessons, and the supplementary lecture notes. The worst aspects of the course included the hefty homework assignments, as well as the ineffective textbook. The professor didn’t effectively teach the material and there were inconsistencies between the homework assignments and the types of content students were tested on. The course would improve if there was a better textbook for the class, more in-class review problems, and practice homework assignments that were equivalent to the type of problems student would encounter on exams. Prospective students should know that the course is chal enging and in addition to going to the lectures and section meetings, they should put forth lots of effort to understand the content.

Spring 2014

Professor: Bruno Jedynak

(3.18)

The best aspects of this course were the high energy instructor, helpful TAs, and the tools available for student success. Homework was not overly cumbersome, the text is helpful, and the lectures were made available online. The worst aspect of the course was the low attendance. The lectures were so closely related to the books that many students did not find it necessary to attend class. Some students also found the instructor’s accent difficult to understand. Some suggestions for improvement include giving better practice questions for exams, example problems in class, and making homework better aligned with the exams. Prospective students should know that the book is a valuable tool, prior knowledge of the programing language R is helpful but not necessary, and keeping up with the work is crucial.

Spring 2015

Professor: Bruno Jedynak

(3.52)

The best aspects of the class included the professor’s enthusiastic and engaging personality. Many students appreciated the abundant study resources that the professor provided. However, some found these resources to be confusing as they reported discrepancies in content and format between class lectures, homework assignments, and exam questions. Students were also confused at times by what seemed to be random grading methods on assignments, and disappointed by the lack of feedback. Prospective students can expect a heavy homework load with lots of independent textbook learning, and may fare better with some background in statistics, as this course moves quickly through statistical concepts. This course also provides an introduction to R programming language.