Data Science for Business
- Providing students with essential knowledge of data mining methods and algorithms and experience in converting business problems into analytical and solving them.
- Students know basic notation and terminology used in data science.
- Students understand basic principles behind analysis algorithm.
- Students visualize, summarize and analyze datasets.
- Students formulate and solve analytical problems for given business problem.
- Introduction to Data Science for BusinessIntroduction to a new discipline Data Science. Its place in academic world and industry. Examples of real world problems.
- Dealing with dataSkills needed to work with data. Data cleaning and preparation. Basic data analysis.
- Data mining, machine learning, statistics.Major classes of algorithms, applicability, solution quality metrics.
- Case study 1. Customer segmentationThe goal of the case is to group customers into clusters based on some customer similarity metrics. Algorithms: clustering – k-means, agglomerative, dimensionality reduction - PCA.
- Case study 2. Customer churn modelingThe 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.
- Case study 3. PricingThe goal of the case is to determine the optimal pricing for goods and services. Algorithms: supervised learning – regression (linear and non-linear models)
- Case study 4. Production optimizationThe 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.
- Case study 5. Sales territory designThe 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.
- Dealing with big and fast dataHandling data in real world – big data and data streams.
- Impacting the businessHow to create a visible impact on business with analytics
- Interim assessment (4 module)0.2 * Homework 1 + 0.2 * Homework 2 + 0.2 * Homework 3 + 0.2 * Homework 4 + 0.2 * Homework 5
- James, G. et al. An introduction to statistical learning. – Springer, 2013. – 426 pp.
- 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.
- Siegel, E. Predictive analytics: The power to predict who will click, buy, lie, or die. – John Wiley & Sons, 2016. – 338 pp.