30E03500 - Data Science for Business II, 25.02.2019-11.04.2019

Topic outline

• Week 1 - Predictive Modeling
  • Select activity Course introduction (2019)

    

    Course introduction (2019) File PDF

    Updated (fixed some errors)

  • Select activity L1: Time series modelling & predictive analytics

    

    L1: Time series modelling & predictive analytics File PDF
  • Select activity T1: Predicting Brazil inflation rates

    

    T1: Predicting Brazil inflation rates Folder

    In this first tutorial, you will work with time series data and learn how to perform variable selection for predicting Brazil inflation rates. This folder contains the files for the  .ipynb file, the data and some images. Download from here and run the notebook on your own machine. Alternatively, we have made the files available in JupyterHub. 

  • Select activity Assignment 1 – Predictive Modeling and a Dash of Time Series

    

    Assignment 1 – Predictive Modeling and a Dash of Time Series

    Completion

    Students must

    

    Make a submission

    Deadline: Friday, March 8th, at 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 16 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 a Jupyter notebook file, since it can nicely display the Python code you used along with its output, and exporting it to PDF (or HTML). Name your file studentNum-lastname-firstname-a1.pdf (or .html).

    
    

  • Select activity Week 1 model solution

    

    Week 1 model solution File HTML

    This is one possible way to solve the first assignment. If you have further questions, please don't hesitate to ask.