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Given the above in the Analytics Strategy course we will learn ‘how to introduce formulation of a winning big data analytics strategy’; ‘how to form questions for big data analysis’; ‘how to pick the “right” data sources’; ‘to distinguish between actionable data and exploratory data’; ‘importance of data visualizations’; ‘role of business advantages of real-time analytics’; ‘big data value delivery and ROI’; ‘rising trends in the wake of a collaborative economy and how to get the best from these’; ‘complexities in of data ownership and development of meaningful privacy laws and rules’.

We will also analyze case studies on organizations that successfully deployed these techniques and tackle two key questions at the intersection of strategy analytics:
(a) How can we use data to make better strategy decisions? (b) How can we make sure the conclusions we can draw from available data answer the question?

2. Student Learning Outcomes

On completion of the course participants will be able to:
• Understand the basic big data analytics concepts such as descriptive, predictive and prescriptive analytics
· Appreciate the role of real time analytics in rolling out strategy, and the big data value proposition
· Aid you to think critically about data and the analyses based on those data
· Enable you to identify opportunities for creating value using business analytics.

3. Required Text Books and Reading Material
a. Articles from Elsevier and other reputed references will be provided as study materials
b. Cases will be provided as reading materials
c. Links to Internet sources will also be provided

4. Tentative Session Plan

Session	Topic	Reading/Case Discussions
1	Intro to Big Data Analytics	Reading materials
2	Keys to create a Data driven Strategy	-do-
3	How to formulate a Big Data Strategy	-do-
4	How to form questions for big data analysis	-do-
5	How to pick the right data sources	-do-
6	Actionable data and exploratory data	-do-
7	Role of data visualizations	-do-
8	Business advantages of real-time analytics	-do-
9	Big data value delivery and ROI	-do-
10	Emerging trends in the wake of a collaborative economy	-do-
MID-TERM
11-12	Predictive Analytics: HR	Case: Application of Supervised Learning Algorithm
13-14	Predictive Analytics: Finance	Case: Fraud Detection- Classification Problem
15-16	Predictive Analytics: Marketing	Case: Personalization and Recommender Systems
17-18	Prescriptive Analytics: Operations	Case: Optimization Problem
19-20	Prescriptive Analytics: Supply Chain	Case or Practitioner Session
END-TERM

5. Evaluation
a. Quiz/Case Assignment – 30%
b. Mid Term – 30%
c. CP – 10%
d. End Term - 30%
6. Academic Integrity:

Broad Course Guidelines
Your attitude toward learning the course material will influence your learning and performance. The skills you will acquire in this course will be very useful irrespective of your future roles in any business. You are responsible for all of the materials covered in class. You are free to meet your instructor to clarify any concepts from the lecture.

Class Participation

Class participation is essential to the success of the class. Obviously, you must attend classes in order to participate. You should provide meaningful contribution to the class through comments and discussion. Attendance will also be used to decide on the marks for your class participation component.

Students are expected to maintain ethical standards conforming to regulations as per the Student Manual of Procedures for your program.

Note: Your instructor reserves the right to alter any of the above. All change/s will be announced in class, via e-mail, or will be updated on the AIS.

Created By: Alora Kar on 07/16/2019 at 02:11 PM
Category: MBA(Exe.)BA-2019-20 T-IV Doctype: Document