Understanding data collections content: a quantitative vision
Keep in mind that for performing data analytics you are willing to make sense of data and this implies acquiring Data Literacy. Have a look at this reference for background.
Michel Bowen, Anthony Bartley, The Basics of Data Literacy: making your students (and you!) make sens of data, NST Press, Arlington Virginia
Useful information: cheat sheets
- Some of the following hands on will be done in Python. So here a memento of the language [PDF]
- Imbalanced Data in Classification Cheat Sheet
Data and experimental lab: Kaggle & Colab
- Access your Kaggle account (https://www.kaggle.com/)
- Prepare your Kaggle environment following the instructions here
- Create a gmail account for using Colab (https://colab.research.google.com/) and follow instructions in class.
- Working Locally on your computer (why not?) If you are willing to use your own computer outside the course, using a self contained Data Science environment follow two steps (requires medium technical skills):
ENSE3 Big Data Storage and Analytics exercises
Hands-on Datathon for ENSE3 – engineering program
During a 4-hour datathon, students develop a mini-project implemented through three 1-hour challenges. The project focuses on intelligent energy consumption and fairness-aware analytics for smart cities, combining data cleaning, clustering, fairness evaluation, SQL-based sampling, and machine learning modelling.