Semester.ly

Johns Hopkins University | BU.520.601

Business Analytics

2.0

credits

Average Course Rating

(4.65)

Business analytics makes extensive use of data and modeling to drive decision making in organizations. To become a leader in a data driven world, it is therefore critical to acquire hands-on experience of both data-related (statistical) and modelling skills. This class focuses on the latter: it introduces students to analytical frameworks used for decision making to make sense of the data. The methodologies covered include Linear and Integer Linear Programming, Decision Analysis, Foundations of utility and risk, and Monte Carlo Simulation. For each topic/methodology students are first exposed to the basic mechanics of the framework, and then apply the methodology to several business problems using software.

Fall 2012

(4.41)

Fall 2013

(4.75)

Fall 2014

(4.8)

Fall 2012

Professor: Daniel e Tarraf

(4.41)

Students said the professor was well prepared and presented the material clearly. They also said the chal enging homework assignments helped them learn the material well. Some students felt that the professor went over the material too quickly and did not explain difficult concepts thoroughly. Students suggested going over fewer proofs in class and instead focusing more on applications or more detailed 107 explanations of theorems. Students should know that the class is demanding and wil be very difficult if they have not taken linear algebra.

Fall 2013

Professor: Daniel e Tarraf

(4.75)

Students thought that the best aspects of this course were the interesting topics and the professor’s thorough lecturing style. Students found the materials chal enging but refreshingly so, although students did complain that the homework load was intense and the difficult materials sometimes led to confusion. Students suggested that the course be broken up into multiple shorter sessions to give them time to review the materials. They also wanted solutions to homework and the exams posted so they could use these materials when studying. Prospective students should have a solid understanding of probability, statistics, and signal processing and be prepared to take on a heavy course load.

Fall 2014

Professor: Pablo Iglesias

(4.8)

Students praised this course for having an ‘easy-going’ instructor who presented the work in a relevant and interesting manner. Perceived issues with the course varied. Multiple students found that the subject matter was so theoretical that it was difficult for students to comprehend. Suggestions to improve the course varied greatly; one student wanted the course to have a better textbook while multiple students couldn’t find an issue with the course. Prospective students should know that students found the course chal enging and required a solid background in linear algebra.

Lecture Sections

(31)

No location info
R. Djavanshir
No class times info

(X2)

No location info
Y. Li
14:30 - 17:30

(X3)

No location info
Y. Li
08:15 - 11:15

(X4)

No location info
Y. Li
08:15 - 11:15

(X5)

No location info
Dept. Faculty
08:15 - 11:15

(T1)

No location info
Dept. Faculty
08:15 - 11:15

(32)

No location info
R. Djavanshir
No class times info

(33)

No location info
Dept. Faculty
No class times info

(41)

No location info
E. Kagan
18:00 - 21:00

(T2)

No location info
Dept. Faculty
14:30 - 17:30

(X1)

No location info
Y. Li
14:30 - 17:30