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Бакалаврская программа «Программа двух дипломов НИУ ВШЭ и Лондонского университета "Прикладной анализ данных"»

05
Декабрь

Business Analytics, Applied Modelling and Prediction

2020/2021
Учебный год
ENG
Обучение ведется на английском языке
7
Кредиты
Статус:
Курс обязательный
Когда читается:
3-й курс, 1-4 модуль

Преподаватели

Course Syllabus

Abstract

The course extends and reinforces existing knowledge and introduces new areas of interest and applications of modelling in the ever-widening field of management. Topics covered Introduction to data analysis and decision-making. Time series data. Outliers and missing values. Pivot tables. Probability distributions. Decision making under uncertainty. Methods for selecting random samples. Nonparametric tests. Stepwise regression. Time series forecasting. Regression-based trend models. The random walk model. Autoregressive and moving average models. Exponential smoothing. Seasonal models. Introduction to linear programming. Product mix models. Sensitivity analysis. Monte Carlo simulation. Applied simulation examples.
Learning Objectives

Learning Objectives

  • Students will study analysis in the business theoretical background
  • Students will discuss and analyse business processes based on a case of bakery manufacturing
  • The goal of the course is to give students a better grasp of quantitative subjects.
  • This course provides students with an ability to handle a range of mathematical and statistical models which helps them be more inquisitive, more precise, more accurate in their statements, more selective in their use of data.
  • The course extends and reinforces existing knowledge and introduces new areas of interest and applications of modelling in the ever-widening field of management.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students will create and discuss financial plan for a start-up
  • At the end of the course and having completed the essential reading and activities students should be able to apply modelling at varying levels to aid decision-making.
  • Students should be able to understand basic principles of how to analyse complex multivariate datasets with the aim of extracting the important message contained within the large amount of data which is often available.
  • Students should be able to demonstrate the wide applicability of mathematical models while, at the same time, identifying their limitations and possible misuse.
Course Contents

Course Contents

  • Data. Structured data types. CSV, XML, JSON, YAML. Excel simplest applications. Bakery component description in YAML.
  • Course structure. Requirements and deliverables. Semantic model of problem. Ontology of Case 1. Bakery (COS Block 1)
  • Introduction to the course, overview, assessment, Monty Hall problem (LSE Block 1)
  • ABCD analysis in excel. Histogram in Excel.
  • Processes, actions and events. BPMN.
  • Tracks arriving schedule analysis and simulation
  • Events in the time. Poisson point process.
  • Tracks arriving schedule analysis and simulation.
  • Descriptive statistics, relationships between variables, Tableau (LSE Blocks 2, 3 and 4)
  • Probability, distributions, decision trees (LSE Blocks 5, 6 and 7)
Assessment Elements

Assessment Elements

  • non-blocking Business Monthly Unit 1
  • non-blocking Business Monthly Unit 2
  • non-blocking Business Monthly Unit 3
  • non-blocking Business Course Work and Exam
    The exam may be carried out online via distance learning platforms.
  • non-blocking Business Course Final Work
  • non-blocking UoL Exam
  • non-blocking UoL Exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    Grade=Business Course Work and Exam*0.4 + (Business Monthly Unit 1+Business Monthly Unit 2+Business Monthly Unit )*0.2
  • Interim assessment (4 module)
    BU2=Business Course Final Work2*0.7 + (Business Monthly Unit4+Business Monthly Unit5+Business Monthly Unit6)*0.1
Bibliography

Bibliography

Recommended Core Bibliography

  • Nabavi, M., & Olson, D. L. (2019). Introduction to Business Analytics. New York: Business Expert Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1922612

Recommended Additional Bibliography

  • Rao, U. H., & Nayak, U. (2017). Business Analytics Using R - A Practical Approach. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1406793