Business Analytics and Big Data

 

Instructor:

Vladimir Gorovoy, Can.Sc., Senior Lecturer, Department of Information Technologies in Management, Graduate School of Management, St.Petersburg University
e-mail: vladimir.gorovoy@gmail.com

Workload:

6 ECTS,

45 contact hours

Prerequisites:

No prerequisites

Course Description:

Business Development officers, consultants, other knowledge workers exploit new opportunities with BI. They have a wide portfolio of standard reports published on one hand, ad-hoc reporting on the other. They connect to multidimensional OLAP cubes to run dozens of business queries a day. As simple as the famous LEGO bricks... And so speedy!

You start observing sales figures by product line, after lunch you look through each subsidiary breakdown by quarter, comparing it to last parallel period. Then you check the best selling strategy and point out the best line managers

Carrying out a strategic business decision never comes easy. Intuits and commercial luck are not enough. Sum up past period experience, get clear figures and provide a solid foundation for decision making. Somehow these figures could uncover more. With the help of modern computation power, vast data storage opportunities and good theory (math statistics, probability,...) you get knowledge from your data assets: patterns, trends, deviations help you in diverse business domains and scenarios.

Course Content (Topics and subtopics):

Topic 1. IT in the organization

  • The need for IT in the organization
  • IT toolkit as a new driver for business efficiency. Traditional examples of doing business opposed to digital economy. Benefits of deploying IT solutions: tangible and intangible. 
  • Introducing IT department
  • Communication and collaboration with IT professionals. IT common trends. IT department structure according to ITIL

Topic 2. Information Systems

  • Information Systems Fundamentals
  • Information Systems concepts and definitions. Environment and infrastructure assessment for an information system. Databases and transaction processing. Functional Applications: Logistics, Operations, Sales and Marketing, Finance and Accounting, Human Resources
  • Enterprise wide Applications classification
  • Supply chain automation essentials. Resource planning. MRP, ERP, CSRP. CRM. Integration and global issues.

Topic 3. Business Intelligence

  • Business Intelligence Fundamentals. 
  • Historical review. BI solution architecture. BI process. Deployment issues
  • Datawarehousing concepts. 
  • Transactional Information Systems and relational databases opposed to Analytical Information systems – addressing the needs for decision making. Choosing a DW architecture. Data extraction and upload. Data integration models. Usage of metadata. 
  • Reporting concepts.
  • Deploying an enterprise wide reporting solution. 

Topic 4. OLAP and Data Mining

  • OLAP.
  • Building up multidimensional cubes. Non-relational and denormalized databases physical design. Defining measures and dimensions. Introducing ad-hoc reporting. 
  • Data Mining
  • KDD (Knowledge discovery from databases) process definition. Types of interesting and potentially useful output patterns, common algorithms. Use cases in different industries and knowledge domains.
  • KPI and Balanced Scorecards
  • A modern paradigm for strategic management. A key to long term success and business development. Common steps for implementing a BSC. Simple toolkit for data engineer and business analyst: take the most of BI at your enterprise and make it simple and convincing.

Course Organization:

The course is based on interactive teaching style with intensive student participation. As a part of the course, students should prepare a software BI solution during their lab works.

Course Reading (the full list):

Required reading

  • 1. Information Technology for Management: Transforming Organizations in the Digital Economy (6th edition). Efraim Turban , Dorothy Leidner , Ephraim McLean , James Wetherbe. 2008. 
  • 2. Decision Support and Business Intelligence Systems (9th Edition). Efraim Turban , Jay E. Aronson , Ting-Peng Liang , Ramesh Sharda. 2010. 

Supplementary reading

  • Data Mining: Concepts and Techniques (3rd edition). Jiawei Han , Micheline Kamber . 2011.
  • Building the Data Warehouse. W. H. Inmon. 1995.
  • Data Mining: Overview and Optimization Opportunity. P.Bradley, U.Fayyad, O.Mangasarian. (1998), http://www.research.microsoft.com/datamine/.
  • http://www.cs.uregina.ca/~dbd/cs831/notes/ml/dtrees/c4.5/tutorial.html
  • Providing OLAP (On-Line Analytical Processing) to User-Analysts: An IT Mandate. Codd E. F., Codd S. B., Salley C. T. E. F. Codd & Associates, 1993.
  • U.M.Fayyad, G.Piatetsky-Shapiro, P.Smyth. (1995), From Data Mining to Knowledge Discovery: An Overview. In "Advances in Knowledge Discovery and Data Mining" (Eds. U.M.Fayyad, G.Piatetsky-Shapiro, P.Smyth), Cambridge, Mass: MIT Press, pp. 1-34.

Exam format:

Length: 90 minutes
Format: online
Final test structure:

Part #  Questions type # of questions Weight in the course grade
1 Questions on a case study 6 50%

Grading Policy (% or points):

Control forms

Weight in the course grade

Number of points in the course grade structure

Final test

0,50 (50%) 50
Assessment of academic progress 0,50 (50%) 50

 

Assessment of academic progress:

Assignment

Weight in the course grade

Labwork 1

7%

Labwork 2 

7%

Labwork 3

7%

Labwork 4

7%

Labwork 5

7%

Discussion

5%

Data analysis project

10%

Totally:

0,50 (50%)

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