Data Science for Business 2020 — различия между версиями
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== About the Course == | == About the Course == | ||
Data Science for Business. MAGoLEGO course. | Data Science for Business. MAGoLEGO course. | ||
| − | Spring 2020. Module 4 | + | Spring 2020. Module 4. |
Department of Data Analysis and Artificial Intelligence, School of Computer Science. | Department of Data Analysis and Artificial Intelligence, School of Computer Science. | ||
| + | <br><span style="color:#DC143C">Join our telegram channel </span> [https://t.me/joinchat/ENzQEhr-hra2WhEjxvgayw Data science for business.] | ||
===Instructors=== | ===Instructors=== | ||
| − | [https://www.hse.ru/staff/lzhukov Prof. Leonid Zhukov] | + | [https://www.hse.ru/staff/lzhukov Prof. Leonid Zhukov] Alternative Course website [weblink] |
[https://www.hse.ru/staff/iamakarov Ilya Makarov] | [https://www.hse.ru/staff/iamakarov Ilya Makarov] | ||
Версия 21:04, 28 марта 2020
Содержание
About the Course
Data Science for Business. MAGoLEGO course.
Spring 2020. Module 4.
Department of Data Analysis and Artificial Intelligence, School of Computer Science.
Join our telegram channel Data science for business.
Instructors
Prof. Leonid Zhukov Alternative Course website [weblink]
Course outline
- Introduction to data science
- Data mining, statistics, machine learning, optimization
- Case studies
- Increasing business impact
Content
| № | Date | Title | Abstract |
|---|---|---|---|
| 1 | 10.04.2020 | Introduction to data science. | Introduction to data science and its role in industry. Examples of real world use cases. |
| 2 | 17.04.2020 | Working with data. | Data cleaning and preparation. ETL process. Basic data analysis and visualization. |
| 3 | 24.04.2020 | Data mining, machine learning, statistics | Types of ML algorithms, applicability, training and testing, solution quality. |
| 4 | 15.05.2020 | Case study 1: Customer segmentation | The goal of the case is to group customers into clusters based on some customer similarity metrics.
Algorithms: Unsupervised learning. Clustering: k-means, agglomerative; Dimensionality reduction: PCA. |
| 5 | 22.05.2020 | Case study 2: Churn modeling | The goal of the case is to predict which customers are going to leave the service within a given time.
Algorithms: Supervised learning. Classification: Logistic regression, Decision trees, Random forest. |
| 6 | 29.05.2020 | Case study 3: Pricing | The goal of the case is to determine the optimal pricing for goods and services.
Algorithms: Supervised learning. Regression: linear and non-linear models. |
| 7 | 05.06.2020 | Case study 4: Industrial analytics | The goal of the case is to predict an output of the production line and find optimal parameter setting.
Algorithms: Supervised learning. Regression: non-linear optimization. |
| 8 | 12.06.2020 | Case study 5. Sales territory design | The goal of the case is to select locations of the sales offices to maximize the coverage under constrained resources.
Algorithms: clustering and geo-analytics approaches. |
| 9 | 19.06.2020 | Impacting the business | How to create a visible impact on business with analytics |
Textbooks
- Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.", 2013.
- James, G. et al. An introduction to statistical learning. Springer, 2013.
- Siegel, E. Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons, 2016.
