Semester.ly

Johns Hopkins University | EN.661.380

Decision Analytics

3.0

credits

Average Course Rating

(3.95)

Decision analytics encompasses the systematic process of inspecting, cleaning, transforming, and modeling data to uncover valuable insights, draw conclusions, and facilitate effective decision-making. This course serves as an introduction to fundamental statistical and machine learning techniques, equipping students with managerial perspectives on leveraging both small and big data for problem identification, analysis, and decision-making. The curriculum covers a range of topics, including data analysis techniques, the utilization of computing tools for applying these methods, and effectively communicating findings to non-experts through numerical and graphical data presentations. Students are required to use Microsoft Excel (the course does not teach Excel, so prior experience with Excel will be helpful) and IBM SPSS (Statistical Package for the Social Science which will be taught in the class). The teaching approach will encompass case studies and the Socratic method to illustrate each concept effectively. By the end of this course, students will have gained essential skills in data analysis, machine learning, and decision-making, empowering them to make informed business decisions and communicate their findings in clear and concise reports.

Fall 2014

(4.07)

Spring 2015

(4.38)

Spring 2023

(3.41)

Fall 2014

Professor: Sinan Ozdemir

(4.07)

Students praised this course for covering engaging subject matter and for introducing math and coding concepts to students who may not have a strong background in either subject. Students found that the course wasn’t particularly well organized and that it seemed as though some lessons were developed on the fly. Suggestions for improvement varied. Multiple students wanted the course to be streamlined so that it covered fewer topics and the syl abus was better defined. Prospective students should know that students found the course to be a good introduction to Python, R, and statistics. They also felt the course was useful for business students and those who didn’t have a background in engineering.

Spring 2015

Professor: Sinan Ozdemir

(4.38)

The best aspects of the class were the practical applications, interesting and relevant material, and enthusiastic professor. Some students felt that not enough time was dedicated to business analytics. Students also felt that the scheduled class time was too late at night and that the instructor and TAs were not available to students. Suggestions for improvement included having a more structured course with hands-on elements, dedicating less class time to statistics by having it as a prerequisite, and having more homework on coding. Prospective students are encouraged to take this course as expectations are clearly explained and the information learned is beneficial to have before interviewing for jobs. Background in statistics and programming is not necessary.

Spring 2023

Professor: Shadi Esnaashari

(3.41)