Segmentando en RRHH como si fueran clientes

Segmentado a los empleados como si fueran clientes. Un caso de inmobiliaria
┬┐Es posible segmentar a los empleados para que las acciones de, por ejemplo compensaciones y desarrollo del talento tengan mejores resultados y se adapten a los perfiles de cada empleado?

Customer Analytics: Veni, Vidi, Vici

Customer Analytics
We show you how Big Data technology enables us to obtain a 360-degree customer view and thus to offer what the market really demands. In this way, we can reduce the risk of abandonment and satisfy and retain customers more easily.

ADAM: Automated Discovery and Analysis Machine

ADAM: Automated Discovery and Analysis Machine
As data scientists, we have to deal with bad formatting data sets with missing or wrong values, and many other problems that hamper our progress. Some studies, such as the Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says, found that data scientists spend 60% of their time on cleaning and organizing data. Once this process is completed, data analysis phase is performed. In this task, values, histograms, variables distribution, and correlations between them are studied. Most of the time, modeling phase involves repetitive analysis tasks, such as selecting the best algorithm by using automated procedures (for example, GridSearchCV in scikit-learn), or features selection process applying different predefined techniques. In the end, all Data Analytics projects are very similar regarding methodology and techniques applied. ADAM (Automated Discovery and Analysis Machine) system is developed in order to optimize our time and focus more on intellectual labors and techniques to solve the specific problem. ADAM is a framework that helps us to perform an automated analysis of the data set by applying Data Science techniques.

CRISP-DM Phase V: Evaluation

Evaluation
In this post, we continue describing the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, after our previous post Phase IV: Modeling. In this case, we discuss the fifth phase of the data analysis project, known as Evaluation.

Case study in Psychology: analysis of human personality profiles

In this case study, we continue describing how Data Analytics techniques can be applied to analyze human personality based on the Big Five model. The aim of the study is to analyze questionnaires in order to detect personality profiles, based on the Big Five model. In a previous post, we described the first three phases of the CRISP-DM methodology: Business Understanding, Data Understanding, and Data Preparation. In that post, we described the context of the case study, the available data and its analysis in detail. Now, we discuss the next phase of the analysis according to CRISP-DM methodology, Phase IV: Modeling. The objective is to identify personality profiles according to the Big Five model by applying data analytics techniques, especially, clustering with different algorithms.

CRISP-DM Phase IV: Modeling

Modeling
In this post, we continue describing the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, after our previous post Phase III: Data Understanding. In this case, we discuss the fourth phase of the data analysis project, known as Modeling.

What is Real World Evidence and why does it matter?

What is Real World Evidence and why does it matter?
Real World Evidence comes from data used for decision making that are not collected in conventional randomized controlled trials (RCTs). Investment in studies that demonstrate the real value of drugs can have many benefits for the pharma industry. Real World Data is the right tool to streamline the use of resources, to listen to the authentic voice of patients and to facilitate collaboration between the pharmaceutical industry and the public sector.

Case Study: analysis of surveys among computer programming students

Coder Survey
Free Code Camp is an open source community aiming at teaching people to program and develop projects for non-profit organizations. CodeNewbie is an international community that helps people who are learning to program. Together, Free Code Camp and CodeNewbie designed a survey and distributed it through Twitter and mailing lists to more than 15000 people who have been learning to program. The objective was to understand their motivations towards programming and how they are learning to program, crossing this information with demographic data (gender, age, etc.) and socio-economic situation. The methodology and the main results are described in "We asked 15,000 people who they are, and how they're learning to code". The data set of the answers collected has been released under Open Database License and in Kaggle they have proposed it as a case study: 2016 New Coder Survey. In this post, we describe the basic exploratory analysis we have carried out on this data set.

CRISP-DM Phase III: Data Preparation. Data analysis and features selection

Data Preparation
In this post, we continue describing the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, after our previous post Phase II: Data Understanding. In this case, we discuss the third phase of the data analysis project, known as Data Preparation.

Case Study: IoT and the recognition of a person's activity

IoT - Internet of Things
With the rise of the Internet of things, IoT, always-connected electronic sensors can be combined with machine learning techniques to control, manage and study numerous phenomena. In this post, we present a use case consisting of the application of Data Analytics techniques using the data provided by the DrivenData competition of the SPHERE (Sensor Platform for Healthcare in Residential Environment) project. This project was created with the aim of monitoring health conditions of elderly people through a series of low-cost sensors installed in their homes; in this way, the mentioned monitoring activities can be performed unobtrusively and respecting the privacy of the patients. We describe the tasks we have carried out to address the issue raised in this scenario.