Learning outcomes

At the end of the course, students will be able to: 

  • Use Python for data analytics.
  • Manipulate complex datasets and apply a set of various processes
  • Interrogate data and provide answers using statistics and graphical visualizations, examples: 
    • Does the number of rooms have a bigger impact than the total square footage on the house price? 
    • To what extent are bigger houses more expensive? Is the relationship linear or quadratic? 
    • Which is the chicest neighborhood in the city? Was it always the case? 
  • Develop a predictive model: 
    • A model to predict the price of a house based on different characteristics such as its square footage, its age, its neighborhood, etc. 

Goals

The objectives of this course are to provide students with the bases of the Python language for data analytics: (i) for the preparation of data, (ii) for the statistical analysis of data, and (iii) for the visualization of data. 

Content

Table of Contents (Tentative - Subject to Minor Changes)

Part 1. Introduction

  • Introduction to Data Analytics and Python
  • Getting Started with Python (Refresh)

Part 2. Statistics with Python

  • Introduction to Pandas
  • Data Visualization
  • Descriptive Statistics
  • Working with Dates
  • Case Study - Cycle Sharing Scheme

Part 3. Introduction to Machine Learning

  • Classification
  • Classification - Case Study
  • Regression
  • Clustering

Assessment method

The assessment consists in:

  • A group project (2 students), based on the materials studied in class (60%). 
  • An oral exam (in group), based both on the project handed in by the students and on the topics covered in class (40%). 

The data for the project will be provided by the teacher. 

Language of instruction

English
Training Study programme Block Credits Mandatory
Bachelor in Business Engineering Standard 0 4
Bachelor in Business Engineering Standard 3 4