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Unleashing the Power of MLOps and DataOps in Data Science
Organizations trying to move forward with analytics and data science initiatives -- while floating in an ocean of data -- must enhance their overall approach and culture to embrace a foundation on DataOps and MLOps. Leveraging these operational frameworks are necessary to enable the data to generate real business value.
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Data Scientists are from Mars and Software Developers are from Venus
Within the broad universe of IT in the business world, the approaches for deploying solutions by traditional software engineers and trendy, new data scientists couldn't be more different. However, appreciating these differences are incredibly important because great business value can be gained by integrating both worlds of development into driving more efficiency and effectiveness into an organization.
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In-Warehouse Machine Learning and the Modern Data Science Stack
As your organization matures its data science portfolio and capabilities, establishing a modern data stack is vital to enabling such growth. Here, we overview various in-data warehouse machine learning services, and discuss each of their benefits and requirements.
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Data Careers in Demand: Crowd Solutions Architect Explained
How can crowdsourcing support the applications of data teams at an organization? With an ever-increasing demand for more and higher quality data, a new role of the Crowd Solutions Architect (CSA) can leverage the potential of the masses to bring an advantage to a business's capability to deliver effective AI-driven solutions.
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 Analytics Engineering Everywhere
Many new roles have appeared in the data world ever since the rise of the Data Scientist took the spotlight several years ago. Now, there is a new core player ready to take center stage, and we may see in five years, nearly every organization will have an Analytics Engineering team.
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Data storytelling: brains are built for visuals, but hearts turn on stories
Today, we need much more than just numbers about our organization to understand, gain insights, and take relevant actions. While visualizations of the data are important, making an emotional connection with the stories behind the data is key. If you want to sell a story, send a missile to the heart.
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An introduction to Explainable AI (XAI) and Explainable Boosting Machines (EBM)
Understanding why your AI-based models make the decisions they do is crucial for deploying practical solutions in the real-world. Here, we review some techniques in the field of Explainable AI (XAI), why explainability is important, example models of explainable AI using LIME and SHAP, and demonstrate how Explainable Boosting Machines (EBMs) can make explainability even easier.
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Get Interactive Plots Directly With Pandas
Telling a story with data is a core function for any Data Scientist, and creating data visualizations that are simultaneously illuminating and appealing can be challenging. This tutorial reviews how to create Plotly and Bokeh plots directly through Pandas plotting syntax, which will help you convert static visualizations into interactive counterparts -- and take your analysis to the next level.
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Five types of thinking for a high performing data scientist
The way you think about a problem and the conceptual process you go through to find a solution may be guided by your personal skills or the type of problem at hand. Many mental models exist representing a variety of thinking patterns -- and as a Data Scientist, appreciating different approaches can help you more effectively model data in the business world and communicate your results to the decision-makers.
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Feature Selection – All You Ever Wanted To Know
Although your data set may contain a lot of information about many different features, selecting only the "best" of these to be considered by a machine learning model can mean the difference between a model that performs well--with better performance, higher accuracy, and more computational efficiency--and one that falls flat. The process of feature selection guides you toward working with only the data that may be the most meaningful, and to accomplish this, a variety of feature selection types, methodologies, and techniques exist for you to explore.
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Analytics Engineering Everywhere
Get Interactive Plots Directly With Pandas