Data Science for Business. MAGoLEGO course.

Spring 2020. Module 4

Department of Data Analysis and Artificial Intelligence, School of Computer Science.

Instructors: Prof. Leonid Zhukov, Ilya Makarov, Anvar Kurmukov

Course Outline

1. Introduction to data science
2. Exploratory data analysis
3. Data mining and machine learning
4. Case studies
• Sales forecasting
• Customer churn modeling
• Customer segmentation
• Personalization
5. Creating business impact

Youtube channel with lectures

Lectures

1. [10.04.2020] Introduction to data science. [Lecture 1] [Video 1]
   Introduction to data science and its role in industry. Examples of real world use cases.
2. [17.04.2020] Exploratory data analysis [Lecture 2] [Video 2]
   Data cleaning and preparation. ETL process. Exploratory analysis and visualization.
3. [24.04.2020] Predictive andalytics and machine learning [Lecture 3] [Video 3]
   Fundamentals on machine lerning, types of ML algorithms, applicability, training and testing, solution quality.
4. [15.05.2020] Case study 1: Sales forecast [Lecture 4] [Video 4]
   The goal of the case is to predict sales at a retail store. Algorithms: supervised learning - linear and non-linear regression, predicting continuos variable
5. [29.05.2020] Case study 2: Customer churn modeling [Lecture 5] [Video 5]
   The goal of the case is to predict which customers are going to leave the service within a given time. Algorithms: Supervised learning – logistic regression, decision trees, random forest; predicting a binary outcome
6. [22.05.2020] Case study 3: Customer segmentation [Lecture 6] [Video 6]
   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 [Lecture 7] [Video 7]
   The goal of the case is to build a recommender system. Algorithms: association rules and collaborative filtering
8. [12.06.2020] Creating business impact [Lecture 8] [Video 8]
   Measuring impact on business from analytics. A/B testing.

Labs & Homeworks

Labs and homeworks wiki

Textbooks

• Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, 2013.
• Kotu, Vijay, Deshpande, Bala. Data Science: Concepts and Practice. Morgan Kaufmann, 2018.
• Siegel, Eric. Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons, 2016.

Modeling Software

• RapidMiner