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12 data science certifications that will pay off

CIO Business Intelligence

The exam covers everything from fundamental to advanced data science concepts such as big data best practices, business strategies for data, building cross-organizational support, machine learning, natural language processing, scholastic modeling, and more. and SAS Text Analytics, Time Series, Experimentation, and Optimization.

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Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

With nearly 5 billion users worldwide—more than 60% of the global population —social media platforms have become a vast source of data that businesses can leverage for improved customer satisfaction, better marketing strategies and faster overall business growth.

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Top Data Science Tools That Will Empower Your Data Exploration Processes

datapine

Companies surely need data scientists to help them empower their analytics processes, build a numbers-based strategy that will boost their bottom line, and ensure that enormous amounts of data are translated into actionable insights. connecting data sources and predicting future outcomes. perfect for statistical computing and design.

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The most valuable AI use cases for business

IBM Big Data Hub

Promote cross- and up-selling Recommendation engines use consumer behavior data and AI algorithms to help discover data trends to be used in the development of more effective up-selling and cross-selling strategies, resulting in more useful add-on recommendations for customers during checkout for online retailers.

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3 Key Components of the Interdisciplinary Field of Data Science

Domino Data Lab

There are many software packages that allow anyone to build a predictive model, but without expertise in math and statistics, a practitioner runs the risk of creating a faulty, unethical, and even possibly illegal data science application. All models are not made equal. After cleaning, the data is now ready for processing.

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AI In Analytics: Today and Tomorrow!

Smarten

Benefits include customized and optimized models, data, parameters and tuning. This approach does demand skills, data curation, and significant funding, but it will serve the market for third-party, specialized models. This technology can be a valuable tool to automate functions and to generate ideas.

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Solving the Data Daze – Analytics at the Speed of Business Questions

Rocket-Powered Data Science

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”). Source: [link]

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