Course syllabus

Admission to the course requires, a bachelor's or university engineering degree in a technical field, 180 credits or 60 credits completed in technology or industrial economics and managemente. At least 20 credits must consist of courses in industrial economics and/or management, at least 6 credits in programming and at least 5 credits in statistics. English 6.

The course aims to develop students’ knowledge of the end-to-end business data analytics process, from identifying business challenges (defining a business question) to effectively presenting business data-driven solutions across diverse industries and the public sector.

The course also aims to develop students’ skills in effectively communicating data-driven insights to stakeholders using data visualization and storytelling techniques.

Along the course, students learn critical concepts that affect data analysis quality, such as how to generate specific, well-defined business questions that drive focused analytical efforts, acquire data from diverse sources, ensure its reliability and cleanliness, and identify patterns, regularities, and other trends hidden in the data. Furthermore, the course is oriented to the most important descriptive, diagnostic, predictive, and prescriptive business analytics methods to extract insights from complex datasets. Additionally, the course includes discussions on ethical business practices and the role of data in promoting social welfare, economic equality, and environmental sustainability. In brief, the course content includes:

• Business analytics frameworks and concepts'
• Data mining process model for business and management
• Accessing and collecting data in business, economics, and policy analysis
• Data wrangling, data tidying, and exploratory data analysis
• Descriptive data mining
• Diagnostic analytics
• Predictive Data Mining (regression and classification tasks)
• Data visualization and dashboarding for business analytics
• The role of data from an ethical and societal perspective

On completion of the course, the student will be able to:

• describe the end-to-end business data analytics process, from identifying business challenges to presenting data-driven solutions.
• demonstrate an understanding of key concepts in descriptive, diagnostic, predictive, and prescriptive analytics.
• explain the importance of data quality, including sourcing, cleaning, and preparing data for analysis.
• discuss the ethical, societal, and legal implications of using business analytics in decision-making processes.

On completion of the course, the student will be able to:

• formulate well-defined and actionable business questions to guide data analysis efforts.
• acquire, clean, and prepare data from diverse sources.
• apply analytical methods to extract insights from data.
• utilize analytical tools and technologies to solve real-world business problems.
• interpret results from data analyses to provide actionable data-based insights for business decision-making and developing data-driven solutions.
• communicate data-driven insights to stakeholders effectively using visualization, dashboards and storytelling techniques.

On completion of the course, the student will be able to:

• evaluate the ethical implications of collecting, analyzing, and using data, data privacy, and sustainability in business contexts.