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Course Syllabus

Bachelor 2019/2020

Information Management

Type: Compulsory course (International Business and Management Studies)
Area of studies: Management
Delivered by: Department of Informatics
Where: St.Petersburg School of Economics and Management
When: 4 year, 1, 2 module
Mode of studies: distance learning
Instructors: Andrei Ternikov
Language: English
ECTS credits: 6
Contact hours: 26

Course Syllabus

Full Syllabus

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

  • Student is able to choose methods adequately corresponding to the objectives of a research project
  • Student is able 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.
  • Students should know how to: work together with other team members, develop personal knowledge and skills.
  • Students are able to 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 are able 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 are able to realize planning and beginning to perform a research project requires an open and innovative mindset
Course Contents

Course Contents

  • Introduction to Python
    Information. Data Types. Basic operations in Python
  • Beginner Data Analysis in Python
    Managing files. Functions and Loops
  • Intermediate Data Analysis in Python
    Dictionaries. Numpy. Datasets. Pandas. Data Preparation
  • Advanced Data Analysis in Python
    Getting Data from the Internet. Exploratory Data Analysis. Regression. Forecasting. Classification. Clustering
Assessment Elements

Assessment Elements

  • non-blocking Lab 1 in Python
    Each Lab lasts 60 minutes. The student gets an integer grade for each task of a Lab. If the answer on the particular question in the Lab 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 Labs (use of mobile devices, paper-based materials, cheatsheets, 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 particular Lab.
  • non-blocking Lab 2 in Python
    Lab lasts 60 minutes. The student gets an integer grade for each task of a Lab. If the answer on the particular question in the Lab 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 Labs (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 particular Lab.
  • 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 MOOC should be finished, and the progress should be submitted 7 days before the first day of the winter exam week (or earlier). 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 Group Project in Python
    The final grade for the Group Project is the sum of all points, according to provided criteria. Each group member of the certain group gets the same grade (the Group Project grade). Any kind of plagiarism is assessed as 0 (zero) points for the whole project.
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.25 * Group Project in Python + 0.25 * Lab 1 in Python + 0.25 * Lab 2 in Python + 0.25 * MOOC
Bibliography

Bibliography

Recommended Core Bibliography

  • 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

  • Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.