2025/2026
Лучшие практики бизнес-аналитики
Статус:
Маго-лего
Где читается:
Факультет экономических наук
Когда читается:
3 модуль
Онлайн-часы:
30
Охват аудитории:
для своего кампуса
Преподаватели:
Ивашковская Ирина Васильевна
Язык:
английский
Кредиты:
3
Контактные часы:
10
Course Syllabus
Abstract
This course is structured around two interconnected pillars essential for modern data-driven decision-making: Core Business Analytics and Data Visualization & Communication. The integrated approach ensures that students not only develop technical analytical competencies but also acquire the communication skills necessary to deliver impactful, data-driven recommendations. Industry expert insights incorporated throughout the course provide real-world context, preparing students for diverse business analyst roles in data-intensive environments.
Part I: Core Business Analytics provides students with a solid foundation in different analytical methodologies used across diverse business domains. The curriculum covers topics including digital transformation landscapes, Agile methodologies, Gartner's analytical approaches, and data management basics. Students will explore performance evaluation techniques, Social Network Analysis (SNA) applications in organizational contexts, contemporary text mining and Natural Language Processing (NLP) methods, and the complete analytical process from problem identification to solution development.
Part II: Data Visualization & Communication addresses the critical need to transform analytical insights into actionable business intelligence. This segment covers the history and principles of effective data visualization, chart creation methodologies, and Gestalt principles of visual perception. Students will master presentation design techniques, dashboard development (using both Excel and Power BI), and specialized network visualization tools. The module emphasizes storytelling with data, enabling students to create compelling, visually-driven narratives that facilitate decision-making across marketing, sales, HR, finance, and other functional areas.
Learning Objectives
- The course aims to provide students with a comprehensive understanding of fundamental business analytics methodologies and modern data visualization techniques. Students will learn to apply analytical frameworks (including performance evaluation, social network analysis, and text mining) across business functions and master the skills to transform analytical insights into actionable visual communications through dashboards and presentations.
Expected Learning Outcomes
- Identify key digital trends and major analytical frameworks (including Gartner's classification) in the modern digital landscape.
- Apply Agile principles and fundamental data management practices throughout the analytical workflow.
- Execute a complete data-driven analytical cycle from defining a business problem to proposing and justifying a solution.
- Differentiate between and apply approaches for measuring business performance, distinguishing between effectiveness and efficiency.
- Build and interpret basic efficiency evaluation models, such as Data Envelopment Analysis (DEA) in R.
- Apply the core concepts and metrics of Social Network Analysis (SNA) to business cases and visualize networks using specialized tools (e.g., Gephi).
- Identify opportunities and apply contemporary text mining and Natural Language Processing (NLP) techniques to practical business tasks.
- Utilize open data sources and extract data via APIs to support analytical projects.
- Apply key principles of Business Process Management (BPM), including basic process modeling using BPMN 2.0 notation.
- Recognize the role of financial modeling and the main areas of consumer and market research in the data-driven decision-making process.
Course Contents
- Introduction to Business Analytics
- Performance Evaluation
- Social Network Analysis (SNA): Applications
- Network Visualization for Better Decision-Making
- Analytical Process: From Problem to Solution
- Fundamentals of Data Visualization
- Contemporary Text & Natural Language Processing (NLP)
- Business Process Management & Financial Modelling
- Dashboards & Visual Analytics
- Data Analysis & Visualization with Power BI
- Business Reporting
- Creating Effective Presentations
Assessment Elements
- QuizThere will be 10 Quizzes (according to 10 course topics). You have 1 attempt to submit the test.
- ProjectThis comprehensive final project is designed to assess students' ability to apply business analytics concepts across diverse domains while demonstrating professional communication skills. The project is divided into two distinct yet complementary parts. Part 1: analytical sprint. A rapid-fire assessment of core analytical competencies through five diverse business micro-cases. This section tests students' ability to quickly understand business contexts, select appropriate analytical approaches, and draw logical conclusions from data. Part 2: Real-life project. A comprehensive case study requiring students to transform raw survey data into a compelling business narrative. Students act as lead analysts for a financial literacy research center, tasked with convincing government stakeholders to fund a national financial education program.
Bibliography
Recommended Core Bibliography
- Business analytics : data analysis and decision making, Albright, S. C., 2020
- Data analysis and decision making with Microsoft Excel, Albright, S. C., 2006
- Data mining and data visualization, , 2005
- Presenting in English : how to give successful presentations, Powell, M., 2002
Recommended Additional Bibliography
- Decision modeling with Microsoft Excel, Moore, J., 2001