This is Part Three in a three-part series examining how to become a data scientist. Supported by extensive research and expert opinions, it aims to provide a comprehensive guide to anyone interested in this field, irrespective of background and level of experience. The topic of Part Three is: 'The Job Market'.
This is Part Two in a three-part series examining how to become a data scientist. Supported by extensive research and expert opinions, it aims to provide a comprehensive guide to anyone looking to move into this field, irrespective of background and experience. The topic of Part Two is: "Learning".
This is Part One in a three-part series examining how to become a data scientist. Supported by extensive research and expert opinions, it aims to provide a comprehensive guide to anyone looking to move into this field, irrespective of background and experience. The topic of Part One is: "What is Data Science?".
This fun-to-read post describes the similarities between implementing a data-driven culture within a sports team and within a company.
Soccer's dynamic nature makes it a notoriously difficult game to predict. But if you have enough data and attributes, you can increase the accuracy of your predictions by using advanced machine learning and artificial intelligence methods.
As the volume of employee data at organizations' disposal increases, novel ways to make use of this data can be defined. By using simple metrics to define your current, past and potential employees, you can increase retention and make sure you hire people who are a good fit culturally.
This post gives an extensive overview of the development process and the capabilities of Infobarris, a tool developed to support the analysis of health and its determinants in the neighborhoods of the city of Barcelona.
There's significant growth in the number of data scientists in the past four years, but this growth is not matched with a growth in the number of data-savvy managers. Will this mismatch lead to problems in the near future?
Data simulators can be an important asset to a company because they can emulate the data you don't have access to, and help you gauge the compatibility of your tools accordingly.
This post dives into different types of text analytics and gives three different examples from the industry to better convey the ideas behind these methods.
Predictive analytics can save lives, and Majid Khattak demonstrates how to do just that by using an index of the El-Nino-Southern Oscillation phenomenon to predict weather patterns that lead to severe storms.
Analyzing traveler feedback on a newly opened airport in Asia, Ravi gives us a walkthrough of his approach to a specific sentiment analysis problem.
Forecasting demand for electricity is of immense importance both for researchers and for industry experts. By accurately forecasting demand, power suppliers can prevent overloads, and can operate more efficiently with a higher profit margin.
A detailed article on HR Analytics, explaining what it is, how it works, and how organizations can leverage it to improve their culture and effectiveness.
Linear programming is a mathematical problem solving technique widely used in various industries to optimize operations and is widely recognized as one of the core concepts of Operations Research. In this post, Professor Lahiri gives a summary of the technicalities of this method.
How Facebook's Ad Matching algorithm works and how marketers can use this knowledge to their advantage.
A Predictive-Descriptive Artificial Intelligence-based expert computer system predicts a Democratic landslide victory in the race for the White House in 2016.
K means clustering is a method that is often used in sentiment analysis. This post gives an overview of how to implement this method.
Machine learning is becoming a ubiquitous characteristic in all industries. As the world makes this transition, we explore the role of services, the applications of open algorithms, and the creation of IP in developing data products for diverse markets.
A brief overview of digital analytics and steps that digital analytics experts take to leverage data to improve business conversions.
How organizations can effectively hire and leverage Big Data talent, and how the internal structure of an organization can be tailored to fit the needs of a data-driven enterprise.
In this post, Jonathan Bloom gives a summary of the history of Artifiicial Intelligence, followed by a brief overview of how the underlying mechanics work.
An analysis on Hospital Inpatient Discharges data released by New York State in 2012. This post shows that open data can be as useful as proprietary data.
How the emerging sensor technologies and accurate GPS location determination is changing the way we drive, all with the help of machine learning.