The course will discuss the principles and ideas underlying the current practice of business analytics, as well as introduce a broad collection of useful data analytics tools (such as text mining, web analytics, network analytics, etc.). It covers the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement predictive models for a variety of practical business applications (such as direct marketing, cross selling, customer retention, delinquency and collection analytics, fraud detection, machine failure detection, insurance underwriting). The course emphasizes hands-on learning with a focus on dealing with real business problems. The course will use analytics software Python for hands-on experimentation with various analytics techniques. Some additional topics covered in this course are as follows:

Data warehouse and OLAP: Companies store and collect large amounts of data during day-to-day transactions. To analyze long-term trends and patterns in the data and provide actionable intelligence to managers, this data needs to be consolidated in a data warehouse. A data warehouse is “a repository of subject-oriented, time-variant data from multiple sources, used for information retrieval and decision support”. It provides a single consolidated interface to the entire corporate data. Data analysis for enterprise-wide business intelligence can then be performed on such consolidated data.

Data visualization: In this course, students will learn visual representation methods and techniques that increase the understanding of complex data and models. Emphasis is placed on the identification of patterns, trends and differences from datasets across categories, space, and time.

(Tentative) Process Mining: Process mining bridges the gap between traditional model-based process analysis (e. g. simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. The course explains the key analysis techniques in process mining. Students will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.

MIS, Database

Grades will be based on a weighted average of the following activities:
- Midterm Examination: 20%
- Final Examination: 20%
- Assignment: 25%
- Term project: 25%
- Attendance/Participation: 10%

References:
Python for Data Analysis, The First Edition, by Wes McKinney, O’Reilly
Deep Learning with Python, by Francois Chollet, Manning
Process Mining: Discovery, Conformance and Enhancement of Business Processes by W.M.P. van der Aalst, Springer Verlag, 2011 (ISBN 978-3-642-19344-6). e-book
Data Mining: Practical Machine Learning Tools and Techniques, The Third Edition, by Witten, Frank, and Hall

1. Introduction
2. Introduction to Python
4. Numpy Basics
5. Getting started with pandas, Data Loading, Storage, and File Format
7. Data Aggregation and Group operation, Time Series
8. Financial Data Analysis, Midterm Exam
9. Machine Learning Basic
10. Machine Learning Classification
11. Building Good Training Sets – Data Preprocessing, Compressing data via Dimension Reduction
12. Learning Best Practices for Model Evaluation and Hyperparameter tuning, Combining Different Models for Ensemble Learning
13. Applying Machine Learning to Sentiment Analysis, Clustering Analysis
14. Image Recognition
15. Term project presentation
16. Final Exam