Магистратура 2020/2021

Большие данные (управленческий аспект)

Статус: Курс по выбору (Международный менеджмент / Master in International Management)

Направление: 38.04.02. Менеджмент

Кто читает: Департамент бизнес-информатики

Где читается: Высшая школа бизнеса

Когда читается: 1-й курс, 4 модуль

Формат изучения: с онлайн-курсом

Преподаватели: Сахнюк Павел Анатольевич

Прогр. обучения: Международный менеджмент

Язык: английский

Кредиты: 3

Контактные часы: 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. 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
• 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 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.

Course Contents

• A Crash Course in Data Science
  By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials. This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.
• Building a Data Science Team
  Data science is a team sport. As a data science executive it is your job to recruit, organize, and manage the team to success. In this one-week course, we will cover how you can find the right people to fill out your data science team, how to organize them to give them the best chance to feel empowered and successful, and how to manage your team as it grows. This is a focused course designed to rapidly get you up to speed on the process of building and managing a data science team. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.
• Data Science in Real Life
  Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses. This is a focused course designed to rapidly get you up to speed on doing data science in real life. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.
• Managing Data Analysis
  This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.

Assessment Elements

• tests upon finishing this online course
• practical tasks

Interim Assessment

• Interim assessment (4 module)
  the 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.