Data Science for Business - MyCourses [PDF]

Feb 22, 2016 - Profit chart. Expected value framework association rules. Story telling with data. Apache Spark. R text m

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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.

Introduction to Data Science for Business 22.02.16 11

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

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