20564 - BIG DATA FOR BUSINESS DECISIONS

Data is growing faster than ever and more data has been created in the past two years than in the history of the human race. As data becomes cheap and easily obtainable, it is only valuable to an organization when knowledge can be derived from it. This course has the mission to introduce students to a new setting where that Big Data is much more than simply storage of large datasets but instead a set of techniques that rely on the abundance of very detailed data to produce knowledge. This course aims to change the way students think about data and its role in business.

• Definition of Big Data.
• Data Warehousing/Data Engineering.
• Simulation for models applied to accounting.
• Business Intelligence.
• Data Visualization.
• Scrapping with Python programming.
• Internet of things.
• General Data Protection Regulation.
• Ethics and big data.

• Recognize business problems where Big Data can be applicable.
• Distinguish the features of the main tools used in Big Data.
• Explain key estimation methods used in artificial intelligence.
• Define the main principles of ethics and regulation imposed to Big Data.

• Solve business problems by data-analytic thinking.
• Use several tools and techniques to practically implement solution methods.
• Know how to store and access huge amounts of data.
• Use Python for Big Data tasks.

• Face-to-face lectures
• Guest speaker's talks (in class or in distance)
• Exercises (exercises, database, software etc.)
• Case studies /Incidents (traditional, online)
• Group assignments
• Participation in external competitions

• Speakers from practice are brought to class to discuss about the challenges and costs involved in implementing Big Data solutions.
• Home programming exercises are given to students.
• Harvard Business Cases are discussed in class.
• A comprehensive practical group assignment is presented in the end of the semester.
• Students are encouraged  to submit their group assessment to national and international competitions in innovation (e.g., startup grants and innovation awards).

• One assignment representing 25% of final grade.
• One final multiple-choice written exam representing 75% of final grade.

• One final multiple-choice written exam representing 100% of final grade.

• ANIL MAHESHWARI (edition by), Data Analytics Made Accessible: 2017.
• F. PROVOST, T. FAWCETT (edition by) Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, 1st Edition.