Syllabus

photo credits: https://abracd.org/

Description

From government surveys to mobile apps, from smart devices to social networks, data has been around us for all human history and its generation has skyrocketed in the past 15 years making it a vital aspect of our daily life when we are monitoring our health, we are choosing our clothes, our food, our entertainment, our representatives, our partners, our routine. There is almost not a single industry that is not using it constantly nowadays.

With this in perspective learning how to deal with data is not a matter of choice anymore. 

The Digital Tools I course aims to introduce some key aspects of the vast world of data science and how to use them in our main objective in MaCT: understanding and designing cities.

To do that we will learn some of the various types of data sources and how to deal with them, how to analyse a data set, manipulate it, visualize it and extract useful information from it using one of the most used programming languages at the moment: Python.

By the end of this course, the students will be able to enrich their projects with comprehensive data analysis and transform data into knowledge and new insights.

 

Learning Objectives

At course completion the student will:

  • Understand the basic concepts of programming, being able to read, write, understand and apply Python codes in daily tasks;
  • Understand basic concepts of data analysis, data mining, data manipulation and visualization;
  • Understand and recognize various types of data and data sources and be able to use them as a key source for academic daily life;
  • Get used to the most common Python tools for data science such as Jupyter, Pandas, Numpy, Plotly, Leaflet and others;

 


Faculty


Projects from this course

Annual and Cumulative Analysis of Pesticide Concentration in Multiple Locations: Understanding Trends and Patterns

Introduction: Understanding the presence and distribution of pollutants like pesticides is crucial. In this blog post, I’ll delve into a Python-based analysis exploring pesticide concentration trends across diverse locations. I’ll use Python’s data analysis libraries to decipher annual trends and gain an overarching understanding of pesticide presence. Interpretation and Conclusion: My Python-based analysis shed light … Read more

Coding Nature

Investigating the Impact of Open Green Spaces on Airbnb Rates with Python 1. Introduction The industry of tourism in Barcelona has seen a major hike in the past couple years. Post-covid Barcelona has also become an educational hub for students from across the globe. The increase in Inflow of people has also made platform urbanism … Read more

The CourtForge

Enlarging the great… Emanuel Ginobili is one of the most -if not the most- accomplished Argentinian basketball players. During his 16 season basketball career in the NBA, that went on from 2002 to 2018, he won 4 championships as one of the San Antonio Spurs “Big Three”, was named an All-Star twice, was selected for … Read more

Visualizing GeoSpatial Data in Python – Going from Csv to Graph

Visualizing dataframes using Geopandas and Plotly in Python introduction Python is a versatile and easy-to-learn programming language. GeoPandas extends the data manipulation capabilities of pandas to spatial data, providing a familiar and convenient environment for working with both tabular and geographical data. GeoPandas makes it easy to load, explore, and analyze geographical data. You can … Read more

Visualizing data using Python

Using python as a digital tool to visualize and arrange datasets; trying to view household costs by accumulating various costs per week. Using the dataset and visualizing the various indices and topics of concern and further pointing out the accumulated average cost of all expenses in a household. Further visualizing the desired data as plotting … Read more

Navigating GeoPandas and the Digital Wilderness

“Start the machine” …was the first operation in mind when asked to perform a task with Python script. One of the motivations to learn Python as an urban designer is to organize geospatial data with accuracy and legibility. Before we dive into the digital wilderness, of data trees and data frames, it’s important to note … Read more

Embarking on Data Analysis (Python)

Embarking on the Python journey, I began by grasping the fundamental programming logics. This provided the basis for understanding program structure and execution, akin to learning the grammar of the language. Variables were introduced as the basic units for storing and manipulating data, laying the groundwork for more complex coding tasks. The concept of variables … Read more

From Guest to Spatial Data Analyst

Introduction Airbnb is a well know marketplace that connects people who want to rent out their property with people who are looking for accommodations, typically for short stays (Investopedia, 2023). As a powerful tool to connect people to hospitality supply around the world, and provide them quality spaces to stay, it heavily relies on the … Read more

Data Visualisation using Python

Introduction In today’s data-driven world, the ability to visualize data effectively is a key skill. This blog post introduces the fundamentals of data visualization using Python, a powerhouse in the data science toolkit. We’ll explore essential libraries like GeoPandas, Plotly and Contextily, guiding you through the process of transforming raw data into insightful, visually appealing … Read more

Note Palette

Music is visualized everyday – but is there any accuracy to its representation? Can sound be accurately represented? If so, what does it look like? How is accuracy measured? While not fully satisfied with the answers found to these questions, Python’s mido package shed light on the how sound and color may be linked through … Read more

Data Visualization Using GeoPandas in Python

1.Geospatial Mapping using GeoPandas in Python In Python, GeoPandas plays a crucial role in facilitating geospatial analysis through its powerful functions. I will illustrate the concise process of leveraging GeoPandas to create maps of Barcelona. This involves a systematic application of GeoPandas’ functions for effective geospatial data synthesis and mapping. Step 1: Loading GeoDataFrame from … Read more

Visualization of Complex Innovation Data using Python

Innovation is a buzzword these days, and everyone seems to be talking about it. However, truly understanding it is a different ball game. Take the Global Innovation Index by the World Intellectual Property Organization (WIPO), for example. It’s a massive framework that pulls data from top-tier organizations worldwide, scoring countries on 100+ indicators. It’s a … Read more