“How Do I Use Data (Science) to Solve Real-World Problems?”

It sounds complicated. It is. But with these five steps, you can do it.

Obviously, this is not a flash in the pan or a marketing gag: The big data market is anticipated to value US$56 billion in 2020 (+7 compared to 2019), and it is projected to reach US$103 billion by 2027.

This undoubtedly is a real-world growth industry directly linked to the demand and growth of real-world companies: Those first-moving companies simply realized that knowledge is power, and they decided to know more precisely how their processes work, what their customers want, how they act and react to (possibly future) products and services.

Put simply, they wanted to gain advantage over all those who do not, by finding out exactly how to improve their work. And guess what? They succeeded. Otherwise, the market would have stopped growing long ago.

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Big Data

Never before have we collected data the way we do today. In this so-called Information Age, everything is tracked and digitized. Traditional IT systems can’t process the massive data sets we’re producing, so engineers are developing new systems for that purpose. But even if we manage to acquire, store and process this never-ending stream of incoming data, one question remains: How will that information be used?

No wonder, many other – and quite “ordinary” – companies have been thinking about better data insights, too. However, due to the opacity of the topic, they find it difficult to find out which steps are important on the way to their own data expertise, and in what order they should be taken. That’s why many such projects come to a standstill.

Here, data scientist, entrepreneur and author Kirill Eremenko comes into play. His new book leads you, step by step, through successful data projects, from a concrete initial question to be answered with useful data, to the result and presentation of the data analysis. In quick succession he suggests:

  1. “Identify the question.” This is the linchpin of successful data analysis.
  2. Clean the data. It takes time, but you can’t work with corrupt data.
  3. Analyze your data. Select the most suitable type of algorithms.
  4. Envision your data. Relay information visually to show what your analysis means.
  5. Present your findings. Begin at the start of the analysis process and tell a story.

The book also explains many factual questions and clears up some misunderstandings about data projects and their management. Find out more here:

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Confident Data Skills

Data scientist Kirill Eremenko explains big data, algorithms, data analysis and careers in data.

Kirill Eremenko Kogan Page Publishers
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