Introduction to Data Science

2022/2023

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of General and Strategic Management (Nizhny Novgorod) (Faculty of Management (Nizhny Novgorod))

Course type:

Compulsory course

When:

2 year, 4 module

Instructor

Privalov, Igor

Course Syllabus

Abstract

The objectives of the discipline "Introduction to Data Science" are to build knowledge, skills and competencies as well as to develop the competencies necessary for future managers to develop data-analytic thinking with which they can gain knowledge and insight from data. The fundamental principles of Data Science are examined from a business problem-solving perspective. The discipline allows you to master the methods and software tools of business information processing.

Learning Objectives

• Teach students basic Python programming skills. To teach students basic data extraction and processing techniques necessary for further study and work in the specialty. Introduce students to the basic concepts and techniques of data analysis, statistics, and machine learning.

Expected Learning Outcomes

• Identify big data problems and be able to recast problems as data science questions
• By the end of this session students should be able to recognize and describe data;
• Importing data from various sources
• Can work with data science team to refine product
• A student can choose proper tools and instruments for data visualization
• A student can prepare his data for visualization
• Basic knowledge about the field of data science.
• Demonstrate knowledge of basic concepts of data science
• Student understands the role of Data Science in a strategic development of a business.
• Cleaning, transforming, and loading the data.

Course Contents

• Intro
• Introduction to Tools
• Introduction to Data Visualization
• Introduction to Machine Learning

Assessment Elements

• Python practical assignment
• Visualization practical assignment
• Тест

Interim Assessment

• 2022/2023 4th module
  0.3 * Python practical assignment + 0.4 * Тест + 0.3 * Visualization practical assignment

Bibliography

Recommended Core Bibliography

• Frederick J Gravetter, Lori-Ann B. Forzano, & Tim Rakow. (2021). Research Methods For The Behavioural Sciences, Edition 1. Cengage Learning.
• 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

• Dr. Ossama Embarak. (2018). Data Analysis and Visualization Using Python : Analyze Data to Create Visualizations for BI Systems. Apress.
• McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925
• Rogers, S., & Girolami, M. (2016). A First Course in Machine Learning (Vol. 2nd ed). Milton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1399490