Case study in Psychology: Data Analytics applied to analyze human personality based on Big Five model

Big Five
Psychologists have been studying human personality and behavior for decades. Several analytic models have been developed in order to measure and classify people's profiles according to personality models. In this post, we describe a case study in personality analysis using Data Analytics techniques. The data that was used is taken from online questionnaires for personality evaluation. The objective is to cluster different profiles or types of person according their personality. We will use the Big Five model detailed below. This model suggests five broad dimensions to describe human personality. In this post, we’ll discuss preliminary data analysis

Case study in smart cities: Modeling noise in the city of Madrid (Spain)

Noise pollution
In this post we describe a new case study of the application of Data Analytics, specifically, for predictive modeling of pollution in the sustainable city. Our study, carried out in 2013 in the framework of Ciudad2020 as part of our work about pollution predictive modeling in the city of the future, an essential component for the integrated environmental information management system, focuses on noise pollution in the city of Madrid (Spain). Our work carries a real and full analysis of noise pollution in the city of Madrid, using historical data from 2012 provided by the Department of Acoustic Control, headed by Madrid’s Environment and Mobility Office. The provided dataset consists of periodic measures, from 1/January/2012 to 31/December/2012, gathered by the 28 automatic measuring stations of the Air Quality Surveillance Network of Madrid’s City Council.

CRISP-DM Phase II: Data Understanding

Data Understanding
In this post, we continue describing the CRISP-DM (Cross Industry Standard Process for Data Mining), after our previous post Phase I: Business Understanding. In this case, we focus on the second phase of the data analysis project, known as Data Understanding.

Case study in People Analytics: exploratory analysis of TAFE exit interviews

Exit Interview
In this post, we describe a People Analytics case study. In this case, we will carry out an exploratory analysis of the data collected from employee’s exit interviews on the education industry. Most Human Resource departments are aware of the importance of an effective talent management within the organization. They normally make exit interviews to employees who resign. Since companies spend a lot of money and resources in recruiting, information about reasons for leaving collected in those interviews is very useful to issue talent retention problems in the company. Those problems should be detected and addressed as soon as possible in order to avoid future renunciations. Data used in our case study has been provided by TAFE (Technical and Further Education) Employment Department in Australia. TAFE exit surveys were developed to effectively canvass the opinions and attitudes of departing employees. The aim was to identify a wide range of operational, organizational and personal variables affecting the decision to leave. Information is used to inform attraction and retention initiatives and to improve work practices across TAFE in order to ensure it is considered an employer of choice.

CRISP-DM Phase I: Business Understanding

Data Understanding
In a previous post, we introduced the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, which provides an overview of the life cycle of a data mining project in the same manner as it is done in software engineering with life cycle of software development. This post is the first of a series of articles on CRISP-DM methodology. In this case, we will introduce the phase I, Business Understanding or defining clients’ needs.

Case study: forecasting blood donation

Blood Donation
Blood donation is a solidarity act that saves many lives every day. Therefore, many awareness campaigns are held for the citizens, especially when there is shortage of blood reserves at hospitals. In this post, we describe a case study based on Data Analytics techniques to predict the number of blood donors in a specific scenario and period of time. In our analysis, we have used donation data taken from a DrivenData competition. This study adopted the donor database of the surveys carried out in a mobile blood donation vehicle in Taiwan that drove to different universities.

CRISP-DM: The methodology to put some order into Data Science projects

In an attempt to establish standards in the area, in the same manner as it is done in software engineering with software development, two methodologies came up in the late nineties: CRISP-DM (Cross Industry Standard Process for Data Mining) and SEMMA (Sample, Explore, Modify, Model, and Assess). Both define a set of sequential steps to guide the process, assigning specific tasks and defining the results that are expected to be obtained in each stage. CRISP-DM was more complete and was applied from a business perspective, thus it was popularly adapted over SEMMA. In this post, we describe CRISP-DM methodology, with its objectives, phases and tasks, derived from the consortium of companies that proposed the methodology.

Case study: forecasting of city bike sharing demand

Bike sharing
In this post we describe a case study on bikes renting forecast or bike sharing competition offered in the Kaggle platform in 2015. This case study is based on Capital Bikeshare program in Washington, D.C., in United States. However, same analysis could be performed in Spain, in the cities that already have the bike sharing program, and which city data is opened, such as Madrid, Zaragoza, Bilbao, Málaga, Gijón, etc. The aim of this study is to show that Data Science techniques allow the analysis and interpretation of a data set, in this case, related to the use of bikesharing program in the cities.

Evolution of the talent pool in the workforce. People Analytics for analyzing talent (IV)

Talent analysis
Another important contribution of People Analytics is the analysis of the professional evolution of a company’s personnel. If we compare the evolution from one year to the next, we can analyze which employees have evolved in their professional career, both at an individual level and collectively, and this information can be used to evaluate the impact of development measures taken by the organization.

Case study in smart cities: Modeling Air Pollution in the city of Santander (Spain)

This post describes the case study of application of Data Analytics technologies in the context of smart cities, specifically, a full analysis of real application in the modeling of air pollution in the city of Santander (Spain).