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 […]
  • 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

What makes an expert data scientist?

The data scientist role has been described as “part analyst, part artist.” Anjul Bhambhri, vice president of big data products at IBM, says, “A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.” The Data Science Beagle defines this as an expert.

Data Scientists ExplainedExpert vs. Automation

Harvard Business Review

Excerpt “It all made for messy data and unwieldy analysis, but as he began exploring people’s connections, he started to see possibilities. He began forming theories, testing hunches, and finding patterns that allowed him to predict whose networks a given profile would land in. He could imagine that new features capitalizing on the heuristics he was developing might provide value to users.”


Excerpt “Data scientists use their data and analytical ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings. They are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and to get/present results with dashboards (displays of current values) rather than papers/reports, as statisticians normally do.”


Excerpt “What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.”

Thinking of hiring a data scientist?

How to Interview a Data Scientist

By Chris Pearson Big Excerpt “Having spent the last year interviewing a large number of Data Scientists, I’ve developed a simple set of questions that help me to understand the what, the why and the how of what they do.” ……….”It’s worth pointing out that if you find someone who has nailed all of the above questions and you have that gut feel that they may do wonders for your business, please don’t get too precious about culture, team fit, etc. Don’t get me wrong, these things are important, but people like this can be incredibly hard to find…sometimes harder than finding that missing piece of Mr Tatum. ”

Here’s why they may be difficult to manage.


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