30E Data Science for Business (6cr)

Transcription

1 30E Data Science for Business (6cr) SYLLABUS Version 2.3 ( ) Instructors contact information Names: Pekka Malo, Johanna Bragge Teaching Assistants: Anton Frantsev, Bikesh Upreti E- mail: firstname.surname@aalto.fi Office: CG Instructors webpages: Course information Status of the course: Advanced Studies in Master s degree program in Information and Service Management (DR2013); Business application course in the Aalto level minor on Analytics and Data Science Academic Year, Period: , Period IV Location: Töölö, C- 332 Language of Instruction: English Course Website: 1. OVERVIEW Data Science for Business What You Need to Know About Data Mining and Data Analytic Thinking The objective of the course is to provide an introduction to practical data science from the perspective of business analysts. The course takes a problem-based approach for teaching data-analytic thinking and fundamental concepts/tools that are needed for data-driven decision making in business. As such, the course is not a replacement for algorithm-centered courses that give deeper insights to the data mining techniques. The course consists of two modules: Module I: Fundamentals of Predictive Analytics Topics covered during the first module include basics of predictive modeling, classification, shopping-basket analysis, evaluation of models, expected value framework, problem of over-fitting and its avoidance. Application examples range from modeling marketing responses to prediction of credit risks, and mining of transaction datasets. To make this introductory module accessible also for students with limited prior knowledge in programming/algorithms, we have chosen SPSS Modeler as the main tool for the demonstrations and assignments. SPSS Modeler is an extensive predictive analytics platform that provides a convenient GUI for utilizing advanced data analysis algorithms and data preparation techniques. Module II: Data Science Tools for Business Analysts The objective of the second module is to introduce R programming for business analysts. The module does not require prior knowledge in R but benefits from prior experience in scripting/programming. In addition to fundamentals of data analysis with R, the module offers an opportunity to experiment with more advanced techniques such as solving of high-dimensional regression problems via regularization techniques. Given the diverse background of course participants, parts of the module are designed to be suitable for self-study to allow progress at different paces and at varying levels of challenge. Towards the end of the course, we will also briefly discuss techniques for handling big data with Spark and Hadoop using IBM Bluemix cloud resources.

2 2. TARGET GROUP AND PREREQUISITES The course is intended for participants with diverse backgrounds: Business analysts and developers who will be implementing and evaluating data science solutions Aspiring future data scientists Business people who will be working with data scientists, managing data science-oriented projects, or investing in data-driven ventures The course has a strong focus on empirical assignments, which require prior knowledge in statistics and basic skills in programming/scripting (or at least willingness to learn). However, more theoretical or mathematical aspects of data analytics are beyond the scope of the course. 3. LEARNING OUTCOMES After completing the course, the students will be able to identify the role of data as a business asset understand the principles of predictive modeling recognize how different data science methods can support business decision-making learn basic data analytic techniques for solving business problems understand the promises and limitations of big data gain some experience in using data analytic tools (both commercial as well as open source) that are widely used in companies. Upon completion of the course, the students will also receive a certificate from IBM/Big Data University stating their completion of the " Predictive Modeling Fundamentals I" and "Introduction to R DataCamp Course". 4. ASSESSMENT, ASSIGNMENTS AND GRADING The course assessment is comprised of the following three parts: Exam in computer lab 30% Team case (course project) 50% Class activity (tutorials, lectures, exercises) 20% All assignments must be completed to pass the course. Late assignments will not be accepted. All the assignments are assessed on a 0-5 scale based on the rubrics that are available in the course workspace in Aalto MyCourses. Note that the starting level of the student teams will be taken into account in grading, and thus special attention is paid to the teams development in knowledge sharing and learning. 5. READINGS Course book: Provost, F. and Fawcett, T. (2013) Data Science for Business: What you need to know about data mining and data-analytic thinking. O Reilly Media, 1st Edition. Complementary reading: James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013): An Introduction to Statistical Learning: with Applications in R. Springer Texts in Statistics. Miller, T. (2013): Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R. FT Press, 1st Edition. Rajaraman, A. and Ullman, J. (2011): Mining of Massive Datasets. Cambridge University Press.

3 6. PRELIMINARY SCHEDULE Module I: Fundamentals of Predictive Analytics Week Dates Topic Introduction to Predictive Analytics Fundamental concepts and definitions Data mining as a process (CRISP-DM) Big Data vs. Small Data Data mining tasks and popular algorithms o Supervised vs. unsupervised learning Classification as a predictive modeling task o Learning with decision trees (C5.0 algorithm) o Model evaluation and risk of over-fitting Learning with C5.0 in SPSS Modeler Group formation and course project kick-off Creation of account at Big Data University (BDU) and registration to the course: Predictive Modeling Fundamentals I, which provides an introduction to SPSS Modeler Data Driven Decision-Making Guest lectures by Reaktor Descriptive, diagnostic, predictive, and prescriptive analytics Story telling in predictive analytics Measuring value from predictive analytics o Expected value framework for model building o Visualizing model performance o Further examples on classification (e.g. Support Vector Machines) Customer response prediction case Credit risk modeling case Pattern Mining and Shopping Basket Analysis What is pattern recognition? Association rule mining as a predictive modeling task: o Shopping basket analysis o Apriori algorithm Supermarket groceries case Association mining case

4 One-page plan for course project Team case presentation date selection Module II: Data Science Tools for Business Analysts Week Dates Topic R for Data Science What is R? Why is it so popular? Elements of statistical learning with R o Fundamentals of R programming o Visualizing and tabulating data o Intermediate topics in R: Linear regression modeling Logistic regression Multinomial logistic regression Complete BDU-course: Introduction to R DataCamp Course, which covers the fundamentals of R programming and data visualization Optional Intermediate / advanced assignments (TBA) High-Dimensional Regression Techniques with R Solving ultra high-dimensional regression problems Techniques for variable selection Introduction to LASSO-based regressions Learning with LASSO (TBA) Team case presentations (alternatively during week 6) Tue 14-19, Dash of Big Data: Hadoop, Spark, and IBM Bluemix Guest lectures by IBM Introduction to Hadoop and Spark Cloud computing with Bluemix Optional assignments: Become a certified professional! Complete BDU-course: Hadoop Fundamentals I Complete BDU-course: Spark Fundamentals I

5 7. COURSE WORKLOAD Contact sessions Lectures and tutorials (1-2 x 3h / week) Exercise demos and workshops (2 x 3h / week) Class preparation Assignments Team case (course project) Total 18h 36h 12h 48h 46h 160h (6 op) 8. ETHICAL RULES Aalto University Code of Academic Integrity and Handling Thereof: 9. OTHER ISSUES Attendance of all sessions is mandatory (max 2 absences are allowed for compelling reasons) Registration to course via WebOodi Students will be divided into working teams by the teachers in charge Evaluation rubrics will be available in MyCourses