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30E03500 - Data Science for Business II, 19.02.2018-04.04.2018

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Syllabus

Week 1: Predictive Modeling

  • Week 1: Predictive Modeling

    Week 1: Predictive Modeling

    •  Course introduction File
    •  L1: Modern Regression Analysis File
      • T1: Sparse regression modelsT1: Sparse regression models
        • hitters.csvhitters.csv24.8KB
        • sparse-regression-tutorial.htmlsparse-regression-tutorial.html2.8MB
      • T2: Forecasting Brazilian inflationT2: Forecasting Brazilian inflation
        • brazil_dataset.txtbrazil_dataset.txt104.7KB
        • brazil-inflation-tutorial.htmlbrazil-inflation-tutorial.html2.9MB
        • ic.glmnet.utils.Ric.glmnet.utils.R4.9KB
    •  A1: Predictive modeling with time series Assignment

      Deadline: March 2nd, 2018 @ 23:59:59 (extended from February 28th, 2018 @ noon)

      The first assignment is meant to be a bridge to the concepts that you have already studied in Data Science for Business I as well as to prepare you for the concepts studied during Week 2.

      The assignment is worth 10 points. Late submissions will suffer a 20% penalty for being up to a day late and a 50% penalty for being more than a day late. Assignments more than 2 days late will not be graded.

      Please submit your solution as a single document. We recommend using an R Markdown file, since it can nicely display the R code you used along with its output, and exporting it to PDF (or HTML). Name your file studentNum-lastname-firstname-a1.pdf (or .html).

    • Restricted Not available unless: You are a(n) Student
       A1: Solutions File
    •  General Feedback on Assignment 1 File
◄MaterialsWeek 2: Prescriptive Modeling►
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30E03500 - Data Science for Business II, 19.02.2018-04.04.2018
Course home page
Materials
Week 1: Predictive Modeling
Week 2: Prescriptive Modeling
Week 3: Sport Analytics
Week 4: Prescriptive Analytics in Finance
Week 5: From Data to Business Opportunities
Week 6: Team Case Presentations
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    • –sGuides for students (CHEM)
    • – Instructions for report writing (CHEM)
    • School of Electrical Engineering (ELEC)
    • School of Engineering (ENG)
    • School of Science (SCI)
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    • Open University
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    • Aalto university pedagogical training program
    • Sandbox
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    • - Instructions for Teachers
    • - Digital tools for teaching
    • - Personal data protection instructions for teachers
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    • - Workspace for thesis supervision
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    • Courses.aalto.fi
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    • - Imagoa / Open science and images
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    • - Search spaces and see opening hours
    • Restaurants in Otaniemi
    • ASU Aalto Student Union
    • Aalto Marketplace
  • ALLWELL?
    • Study Skills
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    • About AllWell? study well-being questionnaire
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