Master
2021/2022
Big Data (Managerial Aspect)
Type:
Elective course (Master in International Management)
Area of studies:
Management
Delivered by:
Department of Business Informatics
Where:
Graduate School of Business
When:
1 year, 4 module
Mode of studies:
distance learning
Online hours:
2
Open to:
students of one campus
Instructors:
Leonid Smelov
Master’s programme:
Международный менеджмент
Language:
English
ECTS credits:
3
Contact hours:
2
Course Syllabus
Abstract
The specialization “Executive Data Science” consists of four intensive courses: 1) A Crash Course in Data Science 2) Building a Data Science Team 3) Managing Data Analysis 4) Data Science in Real Life. You will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.
Learning Objectives
- • to understand how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. • to be able to navigate the structure of the data science pipeline by understanding the goals of each stage and keeping your team on target throughout. • to understand at a conceptual level key concepts such as 1. Experimental design, randomization, A/B testing 2. Causal inference, counterfactuals, 3. Strategies for managing data quality. 4. Bias and confounding 5. Contrasting machine learning versus classical statistical inference
Expected Learning Outcomes
- After completing this course you will know how to: 1, Describe the “perfect” data science experience 2. Identify strengths and weaknesses in experimental designs 3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls. 4. Challenge statistical modeling assumptions and drive feedback to data analysts 5. Describe common pitfalls in communicating data analyses 6. Get a glimpse into a day in the life of a data analysis manager.
- After completing this course you will know how to…. 1. Describe the basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations
- After completing this course you will know. 1. How to describe the role data science plays in various contexts 2. How statistics, machine learning, and software engineering play a role in data science 3. How to describe the structure of a data science project 4. Know the key terms and tools used by data scientists 5. How to identify a successful and an unsuccessful data science project 3. The role of a data science manager
- After completing this course you will know. 1. The different roles in the data science team including data scientist and data engineer 2. How the data science team relates to other teams in an organization 3. What are the expected qualifications of different data science team members 4. Relevant questions for interviewing data scientists 5. How to manage the onboarding process for the team 6. How to guide data science teams to success 7. How to encourage and empower data science teams
Course Contents
- A Crash Course in Data Science
- Building a Data Science Team
- Data Science in Real Life
- Managing Data Analysis
Interim Assessment
- 2021/2022 4th modulethe result reflected in the certificate will be taken into account
Bibliography
Recommended Core Bibliography
- A practitioner's guide to business analytics : using data analysis tools to improve your organization's decision making and strategy, Bartlett, R., 2013
- Jarrett Goldfedder. (2020). Building a Data Integration Team : Skills, Requirements, and Solutions for Designing Integrations. Apress.
- Popescu Cristian-Constantin. (2018). Improvements in business operations and customer experience through data science and Artificial Intelligence. Proceedings of the International Conference on Business Excellence, 12(1), 804–815. https://doi.org/10.2478/picbe-2018-0072
- Qurban A Memon, & Shakeel Ahmed Khoja. (2019). Data Science : Theory, Analysis and Applications. [N.p.]: CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2198593
Recommended Additional Bibliography
- Advanced statistics in research : reading, understanding, and writing up data analysis results, Hatcher, L., 2013
- Kirill Dubovikov. (2019). Managing Data Science : Effective Strategies to Manage Data Science Projects and Build a Sustainable Team. Packt Publishing.
- Kotu, V., & Deshpande, B. (2019). Data Science : Concepts and Practice (Vol. Second edition). Cambridge, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1866160
- Seetharaman, M. (2019). Best practices for building a data science dream team. Information-Management.Com, N.PAG.