Master 2023/2024

Basic Statistics

Type: Compulsory course (Master of Data Science)

Area of studies: Applied Mathematics and Informatics

Delivered by: Big Data and Information Retrieval School

Where: Faculty of Computer Science

When: 1 year, 4 module

Mode of studies: distance learning

Online hours: 52

Open to: students of one campus

Instructors: Довгополый Иоанн Алексеевич

Master’s programme: Master of Data Science

Language: English

ECTS credits: 3

Contact hours: 10

Course Syllabus

Abstract

We begin studying Statistics, a branch of mathematics that uses probability theory to analyze data.

Learning Objectives

• After completion of this course you will be able to do basic data science work, for example, answer in a statistically rigorous way question like "is it true that implementing of special offer increased value per one customer in a segment of young females?" or "is it true that people are more willing to pay for service when they become older?". You also will be prepared to study more sohpisticated Applied Statistics course.

Expected Learning Outcomes

• A​pply Python and pandas to dataset analysis
• A​nalyze datasets, distinguish between different variable types
• Summarize and visualize data
• M​ake conclusion based on data that takes into account random factors, test statistical hypotheses
• Estimate values with point and interval estimators, understand their properties

Course Contents

• 1. D​ata in statistics
• 2. W​orking with pandas
• 3. S​tatistical hypothesis testing (part 1)
• 4. S​tatistical hypothesis testing (part 2)
• 5. S​tatistical estimates
• 6. C​orrelations

Assessment Elements

• Quizzes
  Every week contains graded quizzes. You are expected to submit all graded quizzes during the current week. Your submission attempts are limited: you’ll have only 2 attempts.
• SGA
  Every week except Weeks 1 and 2 contains Staff Graded Assignments (SGAs), that are manually evaluated by one of the course's professors. You can submit your answers to an SGA only once.
• Programming Assignments
  Week 2 contains programming assignments. You are expected to submit all graded PA during the current week.
• F​inal project
  Final project has three parts: Programming Assignment, Quiz and Staff Graded Assignments. It has a strict deadline. After that you could not submit any changes.

Interim Assessment

• 2023/2024 4th module
  0.3 * F​inal project + 0.1 * Programming Assignments + 0.3 * Quizzes + 0.3 * SGA

Bibliography

Recommended Core Bibliography

• Rohatgi, V. K., & Saleh, A. K. M. E. (2015). An Introduction to Probability and Statistics (Vol. 3rd edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1050364
• Seemon Thomas. (2014). Basic Statistics. [N.p.]: Alpha Science Internation Limited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1663598

Recommended Additional Bibliography

• Larsen, R. J., & Marx, M. L. (2015). An introduction to mathematical statistics and its applications. Slovenia, Europe: Prentice Hall. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.19D77756

Authors

• Боднарук Иван Иванович