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What is business analytics? Using data to improve business outcomes

CIO Business Intelligence

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

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Defining data science in 2018

Data Science and Beyond

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. This article is a short summary of my understanding of the definition of data science in 2018. It’s not all about machine learning. In a recent article , HernĂ¡n et al.

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Top Data Science Tools That Will Empower Your Data Exploration Processes

datapine

To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. There are a number of tools available on the market, and knowing which one to choose to increase performance can be time-consuming, and often confusing. Source: RStudio.

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

The demand for real-time online data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistical analysis and management at the top of the priorities list. Prescriptive analytics goes a step further into the future.

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Proposals for model vulnerability and security

O'Reilly on Data

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling 227
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Humans-in-the-loop forecasting: integrating data science and business planning

The Unofficial Google Data Science Blog

Nor can we learn prediction intervals across a large set of parallel time series, since we are trying to generate intervals for a single global time series. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends.

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Techniques for Collecting, Prepping, and Plotting Data: Predicting Social Media-Influence in the NBA

Domino Data Lab

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. What drives the valuation of teams more: attendance or the local real estate market? 05) in predicting changes in attendance. The lower the RMSE, the better the prediction. Does winning bring more fans to games?