Colleen M. Farrelly is a data scientist whose industry experience includes positions related to healthcare, education, biotech, marketing, and finance. Her areas of research include topology/topological data analysis, ensemble learning, nonparametric statistics, manifold learning, and explaining mathematics to lay audiences (see her page on Quora). When she isn’t doing data science, she is a poet and author.
An overview of quantum computing and quantum algorithm design, including current state of the hardware and algorithm design within the existing systems.
We take a hard look at diversity within the tech industry, root causes, and potential solutions and highlight resources/initiatives that can connect readers with programs aiding their professional development.
We look at typical questions in a data science interview, examine the rationale for such questions, and hope to demystify the interview process for recent graduates and aspiring data scientists.
This article provides a list of resources for data scientists who are transitioning from early-career/entry-level positions to more established roles. Surveys have shown a sharp decrease in satisfaction starting around 4 years into the profession, and resources are less obvious and readily available for professionals who have a good handle on the basics of data science than they are for beginners.
This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.
This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.
We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.
This article provides a short overview of emerging data scientist types and their unique skillsets, as well as a guide for HR professionals and analytics managers who are looking to hire their first data scientists or build a data science team. Included are an overview of skills for each type and specific questions that can be asked to assess candidates.
We review recent developments and tools in topological data analysis, including applications of persistent homology to psychometrics and a recent extension of piecewise regression, called Morse-Smale regression.
Colleen M. Farrelly is a data scientist whose industry experience includes positions related to healthcare, education, biotech, marketing, and finance. Her areas of research include topology/topological data analysis, ensemble learning, nonparametric statistics, manifold learning, and explaining mathematics to lay audiences (see her page on Quora). When she isn’t doing data science, she is a poet and author.
Data Scientist Interviews Demystified
Key Algorithms and Statistical Models for Aspiring Data Scientists