Data discovery for business decisions (2018/2019)

Course code
4S007982
Name of lecturer
Claudio Zoli
Coordinator
Claudio Zoli
Number of ECTS credits allocated
2
Other available courses
Academic sector
NN - -
Language of instruction
English
Period
not yet allocated

Learning outcomes

This is an optional module for the equivalent of 2 credits and 12 hrs of classes/laboratory activities. The module is coordinated with SDG Consulting Italia with the contribution of SDG experts in Business Analytics.
It will introduce students to the Corporate Performance Management landscape and aims at developing skills on Business Intelligence tools management.
It will be an opportunity for student for experiencing data analysis closely related to the main activities of a consulting company working on business analytics, Students will be involved in studies of business cases with mapping of data-intensive processes, business model analysis and assessment of business needs.
At completion of the module students should be able to:
- Understand the main tasks and information relevant for designing analytical solutions supporting business processes and decisions
- Master the main techniques for predictive analytics to support business decisions.

Syllabus

The program includes class lecures on one of the most relevant Business Intelligence tools and its main functionalities, followed by a lab workshop (over two days) were students will simulate a business case starting from the customer needs up to the translation into a data model and the connected reporting analytics (KPI, Variances,..).
Classes will cover the subjects of:

1. Scope and methodology for Business Intelligence initiatives
2. Modelling and implementing solutions supporting business processes and decisions
3. How to Navigate Master data, Choose Metrics and Generate Insights
4. Predictive Analytics: using many techniques from datamining, statistics, modelling, machine learning, and artificial intelligence to analyse current data to make predictions about future.

5. Workshop:
a. How to analyse the dimensional context and collect data
b. Decision making: understand the situation, generate and evaluate alternatives, simulate decisions
c. Sales Analysis (by Customer, Area, Channel, Product,..)
d. Profitability calculation (by Product, Channel, Top Customers)
e. Variance Analysis (Quantity, Price, Mix)
f. Brand Performance
g. P&L Forecast and Budget simulation

Classes and labs activities will be integrated by coursework activitites based on the the elaboration and critical analysis of the material developed in lab.
Reference material will be made available during the laboratory activities.

Reference books
Author Title Publisher Year ISBN Note
Piegorsch, Walter Statistical data analytics Chicester: Wiley 2015

Assessment methods and criteria

A final assessment of the coursework will be based on the evalutaion of a report submitted by students and based on the analysis of the business cases discussed in the workshop on “Multidimensional Data Analysis for Business Decision Making”. The report will have to include comments and output results from the workshop analysis as well as critical assessments of the process and will have to adress methodological questions related to the content of the module.
Attendance and a positive final assessment of the report are required in order to be entitled to the 2 credits associated with the module.

STUDENT MODULE EVALUATION - 2017/2018