RSS Featured Blog Posts
  • An overview of feature selection strategies
    Introduction Feature selection and engineering are the most important factors which affect the success of predictive modeling. This remains true even today despite the success of deep learning, which comes with automatic feature engineering. Parsimonious and interpretable models provide simple insights into business problems and therefore they are deemed very valuable. Furthermore, in many occasions […]
    Burak Himmetoglu
  • Helping Non-Profit Organizations as a Data Scientist
    Data Scientists are considered to be highly technical professionals and are typically seen exercising their talent in conventional business industries. However, Data Science is a problem-solving field. Therefore, it can be applied in any field that uses set of data and determines patterns to make decisions. For this reason, Data Scientists have the ability to […]
    VAMSI NELLUTLA
  • Free Book: Introduction to Statistics
    Online Statistics Education: A Multimedia Course of Study.  Project Leader: David M. Lane, Rice University. Content: Introduction Graphing Distributions Summarizing Distributions Describing Bivariate Data Probability Research Design Normal Distributions Advanced Graphs Sampling…
    Capri Granville
  • Introduction to Deep Learning
    Guest blog post by Zied HY. Zied is Senior Data Scientist at Capgemini Consulting. He is specialized in building predictive models utilizing both traditional statistical methods (Generalized Linear Models, Mixed Effects Models, Ridge, Lasso, etc.) and modern machine learning techniques (XGBoost, Random Forests, Kernel Methods, neural networks, etc.).…
    Vincent Granville
  • The Fourth Way to Practice Data Science – Purpose Built Analytic Modules
    Summary:  Purpose Built Analytic Modules (PBAMs) such as those for Fraud Detection represent a fourth way to practice data science, a new model for the good use of Citizen Data Scientists, and a new market for AI-first companies.   It appears that data science has…
    William Vorhies

Data Science Research

According to McKinsey Global Institute research, by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.

There are many great resources available to advance your data career. These are just a few that Data Science Beagle finds helpful and targeted to technology professionals. If you find something brilliant, please share.

Get Data on Data.gov

The home of the U.S. Government’s open data. Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more.

Dallas Ft. Worth

Divergence Academy a private institution approved to operate by the Texas Workforce Commission Career Schools & Colleges

Master of Science Business Analytics at UNT

DataScience@SMU

University Programs, Online, Part-Time and Full-Time

Master of Information and Data Science at Berkeley

23 Great Schools with Master’s Programs in Data Science

Hands-on Bootcamps in Dallas, NYC and San Francisco

12-week Immersive in Dallas with a career services program

12-week Intensive in NYC with an immersive job placement program

6-week Intensive  in NYC Data Engineering Bootcamp

Metis NYC and SF accelerates your career in data science

Programming Skills

HackReactor

Skill Crush

Galvanize

Continuing Education

SMU CAPE Data Analytics Courses

Explore Data Science at Coursera

Certificate Programs

Syracuse University

The Certificate of Advanced Study (CAS) in Data Science at the Syracuse University School of Information Studies (iSchool) is a 15-credit graduate-level certificate that can be taken as a stand-alone certificate or as part of a graduate degree program.

Harvard Extension School

Learn how to analyze data to gain insights, develop new strategies, and make decisions in areas as diverse as product design, marketing, and finance.

University of Washington

Learn to apply data science in fields such as marketing, business intelligence, scientific research and more.

 

 

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