== 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 [https://t.me/joinchat/ENzQEhr-hra2WhEjxvgayw Data science for business.]

===Instructors===

[https://www.hse.ru/staff/lzhukov Prof. Leonid Zhukov]

[https://www.hse.ru/staff/iamakarov Ilya Makarov]

[https://www.hse.ru/staff/intergalactic_admiral/ Anvar Kurmukov]

===Links===
* Alternative Course website [weblink]
* Lectures link https://zoom.us/j/7723819319 Fridays, 6.10pm - 7.30pm
* Seminars link https://zoom.us/j/636910206 Fridays, 7.40pm - 9.00pm

===Course outline===

* Introduction to data science
* Data mining, statistics, machine learning, optimization
* Case studies
* Increasing business impact

===Content===

{| class="wikitable"
|-
! № !! 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: Pricing|| The goal of the case is to compute price elasticity.
'''Algorithms''': Supervised learning: linear and non-linear regression, predicting continuous variable. 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: Customer segmentation ||The goal of the case is to group customers into clusters based on some customer similarity metrics.
'''Algorithms''': clustering – k-means, agglomerative, dimensionality reduction - PCA.
|-
| 7 || 05.06.2020||Case study 4: Personalizaton ||The goal of the case is to build a recommender system.
'''Algorithms''': association rules and collaborative filtering.
|-
| 8 || 12.06.2020 || Case study 5. Demand forecasting ||The goal of the case is to develop demand forecasting model.
'''Algorithms''': ARIMA, sliding window regression.
|-
| 9 || 19.06.2020 || Case study 6. Fraud detection ||The goal of the case is to find abnormal customer transactions.
'''Algorithms''': anomaly detection.
|-
| 10 || 26.06.2020 ||Impacting the business ||How to create a visible impact on business with analytics
|-
|}

===Seminar's materials===

[https://yadi.sk/i/eJm4z1dwLOAXhw Seminar 1. video. ]

[https://yadi.sk/d/aAYt0omgokKuRg Seminar 2. video.], RM processes [https://yadi.sk/d/ij1bgNTd7VFJQQ COVID regression], [https://yadi.sk/d/XvLpO7frlD_l-A COVID], [https://yadi.sk/d/NhmsEpnWYQgshg Fisher's Iris], [https://yadi.sk/d/RO3RX1PPP2XoSw COVID days since 50 confirmed cases], [https://yadi.sk/d/TdmY-qZt_Uu-pQ Iris depivot example]

[https://yadi.sk/i/l2LhZSooi8RkEA Seminar 3. video.] [https://yadi.sk/d/BJw8HSYJMoC5qQ Handling categorical values]. [https://yadi.sk/d/AmL3HXa-fuxS9g Handling missing values]. [https://yadi.sk/d/STx2V0-rgYlybA Titanic prediction on train-test setting.]

RM processes[https://yadi.sk/d/BVRGT0fqpwdUTQ Walmart preprocessing] [https://yadi.sk/d/nw76wzn1x7e1VQ Walmart regression] [https://yadi.sk/d/ig5j_tHesiRg0A GridSearch]

===Home assignments===
 Google doc with [https://docs.google.com/document/d/1Sk-nr5owlKf8MYgdmItit47WLBvWVzMCyc3G_sRLxfs/edit?usp=sharing Q&A] about Home Assignment tasks (contributed by students).

1. Analyze COVID dataset
 due to Monday, April 27, <s>8 am</s> 23:59 Moscow time. 
* Home assignment [https://docs.google.com/document/d/1WMIyh9opbYrK7cWfwHiTcnhOud3l--Sh2VPeVF7QUbc/edit?usp=sharing description].
* Starter [https://yadi.sk/d/jWZDCPhOq_CbuA process.]
* [https://yadi.sk/d/SST3aJE6e4nSzQ Total Cases], [https://yadi.sk/d/pFx64HkmX93UWw Deaths], [https://yadi.sk/d/2cixg4bpOfKbMQ Recovered] on April 19, 2020.
* [https://docs.google.com/forms/d/e/1FAIpQLScCQkT4XB9z74r2mZMttY9IFqlQe0RTVgIU8pjj-u3bUBwGig/viewform?usp=sf_link Google form] to submit your solution.
* [https://yadi.sk/d/VwfTrIUMltfabA HA 1. Solution]

2. Analyze Titanic dataset
<s> due to Friday, May 8, 8 am Moscow time. (there will be no extensions)</s> May 11, 8am, Moscow time.
* Home assignment [https://docs.google.com/document/d/1WMIyh9opbYrK7cWfwHiTcnhOud3l--Sh2VPeVF7QUbc/edit?usp=sharing description], starting with page 3.
* [https://yadi.sk/d/1d6rarln1ybqNQ Titanic dataset]
* [https://docs.google.com/forms/d/e/1FAIpQLSecCpGrn6e_j5KS8rRoKCMKy0Zy0f2wIaiMFxwHRWecxHH1Nw/viewform?usp=sf_link Google form] to submit your solution.
* [https://yadi.sk/d/5aCD_7VGo8GUdw HA 2. Solution]

===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.

===Software===

*For online lectures and seminars. [https://zoom.us/ zoom]
*Modelling package. [https://rapidminer.com/ RapidMiner]
 Apply for educational version https://rapidminer.com/get-started-educational/ 

*Email: Enter your university email (end with @edu.hse.ru)
*Job Function: Student
*University: Higher School of Economics
*Course Name: Data Science for Business
*Course Number: https://www.hse.ru/edu/courses/341840822
*Course Term: Summer Term
*Professor: Leonid Zhukov