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Course Syllabus

Bachelor 2020/2021

Information Management

Type: Compulsory course (International Business and Management Studies)
Area of studies: Management
Delivered by: Department of Management
Where: St.Petersburg School of Economics and Management
When: 3 year, 1, 2 module
Mode of studies: distance learning
Instructors: Andrei Ternikov
Language: English
ECTS credits: 3
Contact hours: 26

Course Syllabus

Full SyllabusAsk Question

Abstract

During this practically oriented data analysis module students will learn how computer programs are used for running predictive models and analytics. The main principal is to explore existing data to build new knowledge, forecast future behavior, anticipate outcomes and trends. Explore theory and practice, and work with tools like Python to solve advanced data science problems in management sphere.
Learning Objectives

Learning Objectives

  • Collect, store, process and analyze data automatically with the use of scripting languages.
  • Develop and apply new research methods of basic machine learning algorithms and ways to collect information using data mining techniques.
  • Solve economic, financial and managerial problems using best practices of data analysis using modern computational tools
  • Can identify the data needed for addressing the financial and business objectives.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students should know how to: work together with other team members, develop personal knowledge and skills.
  • Collect, store, process and analyze data automatically with the use of scripting languages; develop and apply new research methods and ways to collect information using data mining techniques
  • Students should apply planning and beginning to perform a research project requires an open and innovative mindset
  • Ability to choose tools, modern technical means and information technologies for processing information in accordance with the assigned scientific task in the field of management
  • Students should know how to: use ICT solutions in solving real-life problems, work together with other team members, develop personal knowledge and skills.
  • Choose methods adequately corresponding to the objectives of a research project
Course Contents

Course Contents

  • Introduction to Data Analysis with Python
  • Intermediate Data Analysis in Python
  • Advanced Data Analysis in Python
Assessment Elements

Assessment Elements

  • non-blocking HW1
  • non-blocking HW2
  • non-blocking HW3
  • non-blocking HW4
  • non-blocking HW5
  • non-blocking LAB1
    In case of extraordinary external issues, concerning managing offline classes - the LAB can transfer to be assessed with proctoring system.
  • non-blocking LAB2
    In case of extraordinary external issues, concerning managing offline classes - the LAB can transfer to be assessed with proctoring system.
  • non-blocking MOOC
    The MOOC lasts for 4 weeks. Each student should register in the MOOC strictly within his/her corporate e-mail address (ending on @edu.hse.ru or @hse.ru) and your real First & Last names. The progress check and submission procedure are organized in LMS, where the student should attach both: 1. The screenshot of the MOCC grade page with progress and percentage which is given for each assignment. Screenshot should also capture in the same moment the top bar of the Coursera site interface with your profile name (real First and Last names). 2. The *.gif-file or small (up to 10 seconds) video-file where you capture the following path in real time in your account: your profile settings screen (with name and e-mail) -> the screen with your courses -> the screen with the MOOC -> screen with grades of the MOOC (all grades should be also visible: ensure the proper quality of the file). In case of late submit or not attaching at least one of the previous files in time or improper quality (non-readable grades page) of *.gif / video-file or fabrication of results (the lecturer and the study office manager can ask the particular student to log-in in his/her account in real time from the particular computer in order to check the trustworthy of the results): the student gets 0 (zero) points for the MOOC grade.
  • non-blocking TEST
    Test lasts 60 minutes. The student gets an integer grade for each task of a Test. If the answer on the particular question in the Test is not full (not all requirements of the task are done), then the student gets 0 (zero) points for such a task/question. Moreover, the cheating is strongly prohibited during the Test (use of mobile devices, the Internet/LAN connection, talking with the other students and looking at the other screen or paper). In case of cheating - the student gets 0 (zero) points for the whole Test. In case of extraordinary external issues, concerning managing offline classes - the Test can transfer to be assessed with proctoring system.
  • non-blocking BIG-HW
  • non-blocking FINAL PROJECT
    The final grade for the Final Project is the sum of all points, according to provided criteria. Each group member of the certain group gets the same grade (the Final Project grade). Any kind of plagiarism is assessed as 0 (zero) points for the whole project. After the stage of submission, the best projects are transferred to public voting in Slack (among the course participants). The most voted project group gains one extra point (1 point per the whole project-group [precisely, each project-group participant gets 1 point divided by the number of project-group members]) that he/she can redistribute among all students (from the same course) in any proportion to their final grades (before rounding).
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.29 * BIG-HW + 0.15 * FINAL PROJECT + 0.03 * HW1 + 0.03 * HW2 + 0.03 * HW3 + 0.05 * HW4 + 0.05 * HW5 + 0.035 * LAB1 + 0.035 * LAB2 + 0.01 * MOOC + 0.29 * TEST
Bibliography

Bibliography

Recommended Core Bibliography

  • Parker, J.R. (2016). Python: An Introduction to Programming, Mercury Learning & Information
  • Vanderplas, J. T. (2016). Python Data Science Handbook : Essential Tools for Working with Data (Vol. First edition). Sebastopol, CA: Reilly - O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1425081

Recommended Additional Bibliography

  • Cuesta, H. (2016). Practical Data Analysis - Second Edition: Vol. Second edition. Packt Publishing.
  • Mueller, J. (2018). Beginning Programming with Python For Dummies (Vol. 2nd edition). Hoboken, NJ: For Dummies. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1689584