Remove Data Transformation Remove Predictive Modeling Remove Testing Remove Visualization
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12 data science certifications that will pay off

CIO Business Intelligence

The exam tests general knowledge of the platform and applies to multiple roles, including administrator, developer, data analyst, data engineer, data scientist, and system architect. Candidates for the exam are tested on ML, AI solutions, NLP, computer vision, and predictive analytics.

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What is business analytics? Using data to improve business outcomes

CIO Business Intelligence

Research firm Gartner defines business analytics as “solutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states.”. Business analytics also involves data mining, statistical analysis, predictive modeling, and the like, but is focused on driving better business decisions.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. What are the four types of data analytics? Data analytics and data science are closely related.

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Best BI Tools For 2024 You Need to Know

FineReport

Harnessing the power of advanced APIs, automation, and AI, these tools simplify data compilation, organization, and visualization, empowering users to extract actionable insights effortlessly. Key features include comprehensive data connectors, user-friendly report-building tools, and web-based sharing options.

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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? Building Models. A common task for a data scientist is to build a predictive model. You know the drill: pull some data, carve it up into features, feed it into one of scikit-learn’s various algorithms.

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What Is Embedded Analytics?

Jet Global

This is in contrast to traditional BI, which extracts insight from data outside of the app. We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. Plus, there is an expectation that tools be visually appealing to boot. Their dashboards were visually stunning.

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Manual Feature Engineering

Domino Data Lab

The problem is that a new unique identifier of a test example won’t be anywhere in the tree. Feature extraction means moving from low-level features that are unsuitable for learning—practically speaking, we get poor testing results—to higher-level features which are useful for learning. Separate out a hold-out test set.

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