Predictive Analytics (BAN 805)

2021 Fall
Sabancı Business School
Business Analytics(BAN)
3
10
Mustafa Hayri Tongarlak tongarlak@sabanciuniv.edu,
Click here to view.
English
Doctoral, Master
--
Click here to view.

CONTENT

This course introduces basic concepts and models of supervised and unsupervised statistical learning models. The topics include, multiple regression, logistic regression, classfication, resampling methods, subset selection, the ridge, the lasso, tree- based methods, support vector machines, principal component analysis, and clustering.

PROGRAMME OUTCOMES


1. Develop the ability to use critical, analytical, and reflective thinking and reasoning 5

2. Reflect on social and ethical responsibilities in his/her professional life. 3

3. Gain experience and confidence in the dissemination of project/research outputs 4

4. Work responsibly and creatively as an individual or as a member or leader of a team and in multidisciplinary environments. 5

5. Communicate effectively by oral, written, graphical and technological means and have competency in English. 5

6. Independently reach and acquire information, and develop appreciation of the need for continuously learning and updating. 4


1. Develop, interpret and use statistical analyses in decision making. 5


1. Demonstrated understanding of data-driven decision modeling and analysis concepts/frameworks. 5

2. Knowledge of and hands-on experience with fundamentals of business analytics, management information systems, statistical and prediction models. 5

3. Ability to transform complex data into valuable insight and resulting value-adding actions. 4

4. Skills in hands-on data-mining tools and techniques. 5

5. Exposure to the analytical methods in basic business disciplines such as marketing, operations, and finance 5