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Quiz: What's your IQ on data science techniques and processes?

Creating a successful data science program enables you to look deeper into your organization's data for analytics uses. Take this quiz to see how much you know about the data science process.

Finding valuable insights hidden in your company's data can take very deep analysis. That's where data science techniques and tools come in.

Especially in a big data environment, instituting an effective data science strategy enables you make the most of the available data to help your organization optimize business processes, boost revenue and gain a competitive edge on business rivals. That's accomplished through predictive analytics, data mining and machine learning applications designed to identify patterns, trends and relationships in large data sets.

However, creating a data science program isn't easy. Data science teams have to be able to integrate with business units while doing informative and relevant data analysis work that doesn't bog them down in basic querying and reporting tasks.

Do you have the know-how required to implement a successful data science process in your organization? Take this quiz to find out. You'll also get references to additional articles to help round out your knowledge of data science techniques and best practices.

Next Steps

Learn more about how to get business value from data science projects

Listen to this podcast about integrating data engineers and data scientists

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This was last published in April 2017

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What are you doing to establish a useful data science strategy at your organization?
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It is important to have a clear objective of the information to be derived from data collected. A clear question will determine and define how and what data to collect.
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I disagree with question 6: "True or false? Research scientists trained in disciplines such as physics don't make good data scientists because they often lack a business or technology background."

Folks brought in with experience of Technical and business knowledge will bring out better results. For example, hive complex analytics queries and understanding the mathematical part or computation is easier , since they have undergone Mathematical experience.
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