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4 Skills for the Aspiring Data Scientist | 7wData

4 Skills for the Aspiring Data Scientist | 7wData

4 Skills for the Aspiring Data Scientist
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The role of data scientist is extremely hot in today's talent market. Here are the four skills you need to develop to fill this role.
According to The Quant Crunch: How the Demand for data science Skills is Disrupting the Job Market, published by IBM , the number of jobs for all U.S. data professionals will increase by 364,000 openings (to 2,720,000) by 2020. Of those analytics positions, the fastest-growing roles are data scientist and advanced analyst. Many of these positions will go unfilled due to a lack of talented people who can match the requirements.
With the expected surge in demand and the lack of supply, the question aspiring analytics professionals ask is, "How can I become a data scientist?" Although this path is not for everyone, there are four skills that you can seek out to get started.
Among the most important skills you must develop is critical thinking. This includes learning how to structure a problem so that it can be solved as a mathematical model. If you were one of the kids in grade school who groaned when you had to solve story problems, data science might not be your ideal career path. The job of a data scientist is to take real-world problems (which are messy to define and even messier to break into solvable components) and transform them into mathematical models that, when automated, create repeatable business processes.
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Oftentimes, your business peers will identify "wouldn't it be amazing if" ideas about the potential benefit the business could achieve if it better utilized its informational assets. A data scientist must take these statements and break them down into a description of the desired result, determine what data is needed to get that result, and understand how that data can be converted into a model that can be repeated in a systematic fashion.
Consider autonomous vehicles. The idea is "wouldn't it be amazing if cars could drive themselves?" The solution requires applying hundreds of sensors to the car, each ingesting millions of data points per second, and running those through a model to determine what the next action must be. Each of these responses represents a quantifiable model. Does the car need to go forward? Does it need to stop? Does it need to turn left or right? Being able to break those "what if" ideas down and identify how to reach the goals is the first step in becoming a data scientist.
Data is different from information and knowledge. Data is the raw material from which information and knowledge are created through the application of analytics.
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In the case of autonomous vehicles, data includes all the sensor readings: position of the car, position of other objects around the car, speed, road conditions, etc. To make the data useful, you need to transform it into a result. The data is not always clean, and it is not always in the format that can be easily fed into the model. This is an area where good data scientists excel. They know how to transform millions of data points into something that ultimately derives an answer that solves a business problem.
Effective data scientists have a solid understanding of what this takes.
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