Магистратура
2021/2022![Learning Objectives](/f/src/global/i/edu/objectives.svg)
![Expected Learning Outcomes](/f/src/global/i/edu/results.svg)
![Course Contents](/f/src/global/i/edu/sections.svg)
![Assessment Elements](/f/src/global/i/edu/controls.svg)
![Interim Assessment](/f/src/global/i/edu/intermediate_certification.svg)
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Предиктивная аналитика логистики и цепей поставок
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Лучший по критерию «Новизна полученных знаний»
Статус:
Курс по выбору (Стратегическое управление логистикой и цепями поставок в цифровой экономике)
Направление:
38.04.02. Менеджмент
Где читается:
Высшая школа бизнеса
Когда читается:
1-й курс, 4 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
16
Охват аудитории:
для всех кампусов НИУ ВШЭ
Прогр. обучения:
Стратегическое управление логистикой и цепями поставок в цифровой экономике
Язык:
английский
Кредиты:
3
Контактные часы:
24
Course Syllabus
Abstract
This is an elective course, offered during the 2nd year of the MSc program “Strategic Logistics Management”. The goal of this course is to provide an overview of classical and modern methods for solving business problems that can be addressed with predictive analytics and to extend the practical skills of predictive modeling. The course builds upon knowledge gained in the research seminar and extends it towards modern modeling methods such as ensemble models and deep learning. The topics of model selection, feature selection and feature engineering are covered in greater detail. All classes run in a computer lab. A class includes an overview of theoretical principles, a guide to implementation of analysis methods in software and a hands-on example for solving a practical problem using software tools. This is followed by a discussion of practical application of predictive methods in business analytics. In order to pass the course, all participants are required to give a short presentation on application of predictive analytics tools and to do an individual or group project for application of predictive analytics tools to solve a real-world problem of their choice (students can join a data science competition, for example). Pre-requisites The participants should have basic knowledge of data processing tools and technologies, as well as understanding of modeling methods such as regression. Research Seminar “Methods and tools of Data Analysis” offered in the 3rd year of the BSc program “Logistics and Supply Chain Management” would be a good background. For students who didn’t have the opportunity to take that course several competences are required: • working knowledge of descriptive statistics and exploratory data analysis (graphs and descriptive statistics); • working knowledge of analytical statistics (statistical hypotheses testing, correlation and regression); • familiarity with programming languages for statistical data analysis (R/tidyverse is preferable); • basic understanding of operations with relational data, such as filtering, sorting, grouping and joining data tables.
Learning Objectives
- To explain the application of predictive models in a business analytics context
- To describe the steps required to develop an analytical solution
- To define predictive analytics task types
- To elaborate on methods for solving regression and classification type problems
- To explain methods and approaches for assessing and improving the quality of predictive models
- To provide tutorial on implementing data analysis steps using R
- To discuss business cases and opportunities of predictive modeling
Expected Learning Outcomes
- to apply resampling strategies for model evaluation
- to apply the MLR framework for constructing, benchmarking and tuning predictive models
- to apply variable selection procedures for model specification
- to construct ensemble-based models using R
- to construct linear predictive models using R
- to construct regularized linear models using R
- to construct tree-based regresssion and classification models using R
- to definde the concept of statistical/machine learning
- to define the predictive analytics concept
- to develop a prototype analytical solution to a business problem using R and to present its outcomes to decision makers
- to explain the tradeoff between model bias and variance
- to identify a predictive task type based on a problem formulation
- To implement deep learning models using Keras and R
- to implement linear classifiers using R
- to interpret model accuracy metrics for regression models
- to interpret performance metrics for classification models
- to justify an appropriate model type and specification using model benchmarking techniques
- to list the steps in implementation of a predictive analytics solution
- to name use cases of predictive modeling in Logistics and Supply Chain Management
- to prepare data for a predictive model
Course Contents
- Topic 1. Predictive analytics defined. Use-cases. Lifecycle of an analytical solution.
- Topic 2. The concept of statistical/machine learning
- Topic 3. Regression using linear models
- Topic 4. Classification using logistic regression
- Topic 5. Resampling for model performance assessment
- Topic 6. Variable selection and model regularization.
- Topic 7. Tree-based classifiers
- Topic 8. Modeling frameworks
- Topic 9. Ensemble models
- Topic 10. Deep Learning
Assessment Elements
- ParticipationThe grade is computed by dividing the sum of points for participation obtained by the student divided by the total points for all assignments posted and multiplied by 10. The result is posted into the gradebook without rounding.
- Presentation
- Project Defense
Interim Assessment
- 2021/2022 4th module0.5 * Project Defense + 0.29 * Participation + 0.21 * Presentation
Bibliography
Recommended Core Bibliography
- Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, & Maintainer Trevor Hastie. (2013). Type Package Title Data for An Introduction to Statistical Learning with Applications in R Version 1.0. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.28D80286
- Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction (Vol. Second edition, corrected 7th printing). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=277008
- James, G. et al. An introduction to statistical learning. – Springer, 2013. – 426 pp.
- Lantz, B. (2019). Machine Learning with R : Expert Techniques for Predictive Modeling, 3rd Edition (Vol. Third edition). Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2106304
- Larose, D. T., & Larose, C. D. (2015). Data Mining and Predictive Analytics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=958471
- Taweh Beysolow II. (2017). Introduction to Deep Learning Using R. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sprbok.978.1.4842.2734.3
Recommended Additional Bibliography
- Hastie, T., Tibshirani, R., Friedman, J. The elements of statistical learning: Data Mining, Inference, and Prediction. – Springer, 2009. – 745 pp.
- Larose, D. T., Larose, C. D. Discovering knowledge in data: an introduction to data mining. – John Wiley & Sons, 2014. – 336 pp.