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3 Things Citizen Data Scientists Need in Predictive Analytics!

Smarten

When combined with Citizen Data Scientist initiatives, the adoption and use of predictive modeling and forecasting techniques can be a boon to any enterprise. Team members who have access to augmented analytics and assisted predictive modeling can plan better, predict more accurately and dependably meet goals and objectives.

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Do I Need Both BI Tools and Augmented Analytics?

Smarten

Data Discovery including self-serve data preparation, smart data visualization with charts, graphs and other visualizations for clarity and decisions. Predictive Modeling to support business needs, forecast, and test theories. Assisted Predictive Modeling. Smart Data Visualization. Auto Insights.

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3 Key Components of the Interdisciplinary Field of Data Science

Domino Data Lab

In this article, we will provide an overview of the three overlapping components of data science, the importance of communication and collaboration, and how the Domino Data Lab MLOps platform can help improve the speed and efficiency of your team. All models are not made equal.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. An e-commerce conglomeration uses predictive analytics in its recommendation engine.

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What is ARIMAX Forecasting and How is it Used for Enterprise Analysis?

Smarten

This article looks at the ARIMAX Forecasting method of analysis and how it can be used for business analysis. An Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms.

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What is ARIMA Forecasting and How Can it Be Used for Enterprise Analysis?

Smarten

This article provides a brief explanation of the ARIMA method of analytical forecasting. Autoregressive Integrated Moving Average (ARIMA) predicts future values of a time series using a linear combination of its past values and a series of errors. ’ The ARIMA model is suggested for short term forecasting.

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What is the KMeans Clustering Algorithm and How Does an Enterprise Use it to Analyze Data?

Smarten

This article provides a brief explanation of the KMeans Clustering algorithm. The Smarten approach to business intelligence and business analytics focuses on the business user and provides Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist. Use Case – 2.