Idea Transcript
Data Science for Business Instructors’ contact informa/on Names: Pekka Malo, Johanna Bragge Teaching Assistants: Anton Frantsev, Bikesh Upre9 E-‐mail: firstname.surname@aalto.fi Office: CG-‐4.18 Instructors’ webpages: hEps://people.aalto.fi/pekka_malo hEps://people.aalto.fi/johanna_bragge
Course informa/on Status of the course: Advanced Studies in Master’s degree program in Informa9on and Service Management (DR2013); Business applica9on course in the Aalto level minor on Analy9cs and Data Science Academic Year, Period: 2015-‐2016, Period IV Loca/on: Töölö, C-‐332 Language of Instruc/on: English Course Website: hEps://mycourses.aalto.fi/course/view.php?id=3571
Pekka Malo, Assist. Prof. (statistics) Aalto BIZ / Department of Information and Service Economy Lecture 1, Mon 22.2.2016
What is Data Science for Business? association rules
unsupervised
business strategy CRISP-DM
supervised Predictive modeling
Data analytic thinking
Story telling with data
Data Science for Business
Dash of programming
classification pattern mining
SVM
Profit chart Expected value framework
R
decision trees
IBM Bluemix
Model evaluation and validation
accuracy crossvalidation
text mining
Apache Spark
Introduction to Data Science for Business 22.02.16 2
Course overview and prerequisites “Data Science for Business – What You Need to Know About Data Mining and Data Analytic Thinking” •
Module I: Fundamentals of predictive analytics • Basics of predictive modeling and introduction to commonly used data mining algorithms (e.g., classification, shopping basket analysis) • Evaluation of models, expected value framework, and avoidance of overfitting • Module II: Data Science tools for Business Analysts • Learning data analytics with R programming language • Basics of Apache Spark • Learning to deploy models on cloud with IBM Bluemix • Prerequisites: • •
Fundamentals of statistics (e.g., inference, regression analysis, and logistic regression) Basic skills in programming / scripting (or at least willingness to learn) Introduction to Data Science for Business 22.02.16 3
Toolkit
Introduction to Data Science for Business 22.02.16 4
Learning objectives and 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 certificates from IBM/Big Data University stating their completion of the “Predictive Modeling Fundamentals I" and "Introduction to R – DataCamp Course". Introduction to Data Science for Business 22.02.16 5
Introduction to Predictive Analytics 22.02.16 6
Completing the course Contact sessions • Lectures and tutorials (1-2 x 3h / week) 18h • Exercise demos and workshops (2 x 3h / week) 36h Class preparation
12h
Assignments
48h
Team case (course project)
46h
Total
160h (6 op)
NOTE: There are compulsory contact sessions 3 x 3 hours per week (max two 3-hour sessions can be missed) Introduction to Data Science for Business 22.02.16 7
Course timeline 22.2.2016 Week 1. Introduction to Predictive Analytics
29.2.2016 Week 2. Data Driven DecisionMaking
7.3.2016
14.3.2016
Week 3. Pattern Mining and Shopping Basket Analysis
Fundamentals of predictive analytics
Formation of teams (based on pre-survey)
Week 4. R for Data Science
21.3.2016
29.3.2016
Week 5. Advanced Analytics with R
Week 6 Dash of Big Data with Spark and Bluemix
Data Science tools for business analysts
Submission of project proposal (1-page summary)
Team Case Presentations on Week 6
Team case = modeling assignment to be reported within 1 week from publication = BDU course assignment (due dates announced separately) Introduction to Data Science for Business 22.02.16 8
Lectures (L) and tutorials (T) # L-1
Date 22.2.
Topic Introduction to Predictive Analytics
Assignment
T-1
23.2.
Decision Tree Models
L-2
29.2.
Data Driven Decision-Making (Reaktor)
T-2
01.3.
Measuring value from predictive analytics
L-3
07.3.
Pattern mining and shopping baskets
T-3
08.3.
Supermarket transactions with Apriori
Modeling case 2
L-4
14.3.
R for data science
BDU assignment
T-4
15.3.
R for data science (cont’d)
L-5
21.3.
High-dimensional Regression Techniques
T-5 L-6 T-6
22.3. 29.3. 30.3.
Learning with LASSO in R Dash of Big Data (IBM) Cloud computing with IBM Bluemix
BDU assignment
Modeling case 1
Team case presentations
In addition to weekly lectures and tutorials, there will be 3-hour exercise sessions on each Wednesday during 9 24.2. - 23.3.2016
Grading The course assessment is comprised of the following three parts: • Exam in computer lab • Team case (course project) • Class activity (tutorials, lectures, exercises)
30% 50% 20%
All assignments must be completed to pass the course. Evaluation criteria are separately specified in each assignment. When evaluating work done in teams, starting level of the student teams will be taken into account in grading. Special attention is paid to the teams’ development in knowledge sharing and learning.
Introduction to Data Science for Business 22.02.16 10
Assessment and grading of team case (50% of total grade) • The grading of team cases is based on a combination of peer evaluation and a corresponding evaluation by teachers. • Evaluation rubrics will be provided separately. • To conduct the peer evaluation, you will be provided with a separate observation form. Each student will be able to evaluate each member of the team. All peer evaluations will be confidential.
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Course material All course communication, materials and exercises, as well as submission of exercises, will be available on the course home page in MyCourses
Introduction to Data Science for Business 22.02.16 12