Within the past few years, the study of data science and machine learning has exploded into its own job field. However, the tech subgenre has been galavanting to the mainstream for nearly 3 centuries. It all started sometime in the 1740s with Bayes’ Theorem.
Today, the demand for data scientists is at its peak and is only continuing to surge. By the end of this year, there will have been 2.7 million data scientist job openings. The million-dollar question is:
What do data scientists do?
To be direct, data science is the process of analyzing data. Explaining with more complexity, data scientists use heterogeneous data – data that were composed of different forms or dissimilar components – to solve rigorous problems.
To do so, data scientists use their master skills in computer science, high-level mathematics, and more. These aforementioned skills are especially unique to data scientists developing industrial machine learning technologies, programs, and Enterprise AI.
In short, data scientists take three steps in analyzing their data: preparing, testing, and capturing. Of course, 2-3 individual tasks are necessary to complete each step.
To prepare for data analysis, data must be first captured. In other words, the first task in a data scientist’s work is to simply collect their data. Scientists can extract or acquire them. To further prepare for data analysis, the scientist then maintains their data, safely storing and staging it.
Some data storage methods include scalable storage optimized with artificial intelligence. Finally, the data undergo processing which involves mining and classification.
Once data scientists are done with preparation, having completed the previously mentioned tasks, they will move on to physically testing their findings. Think back to your public school science fair days:
What must you do before testing your data?
Draft a hypothesis or an educated guess. It involves developing a theory to test with the data model. After this, the data is finally ready for analysis. This is the stage where new findings based on the initially collected data are discovered. It’s often done by modeling, exploring, and experimenting with data to reach desired outcomes and to decide what the data means.
All that’s left to do at this point is to communicate the results. Reconnecting data science to science fair procedures, how would you express your final discoveries? Perhaps a visual aid, such as a tri-fold poster board.
While data scientists may not necessarily take this route of expression, they create an easily understood picture of the model’s predictions. It’s easily translatable to an audience of laymen. The final task in data analysis is to apply your results. It’s to help end-users understand how to use the predictions to take effective actions within their business.
Who are data scientists?
More importantly, how do you become one?
Jenn Gamble, Director of Noodle.ai – the leading machine learning software giant – spoke on the subject, saying, “You don’t necessarily need a Ph.D. to do data science – you need an aptitude for math and a creative, problem-solving mentality.”
By 2025, we will be creating data worth 175 billion terabytes on a daily basis, so the primary way to fully understand and analyze the world’s surging data is to hire more data scientists with access to advanced tools.
Some of the most popular tools in the industry include the R programming language, python, PyTorch, hadoop, and Apache Spark. Among the most crucial roles needing fulfillment in the machine learning job economy are data engineers, AI hardware specialists, and software engineers.
Data engineers create and maintain the methods which bring in data, needing skills in Scikit-learn, AForge.NET, and/or Java programming language. Software engineers analyze business data and design software to fit needs, needing skills in Java programming language, SQL, and/or python. Lastly, AI hardware specialists create and program AI to perform specific tasks, needing skills in machine learning, python, Saas, and Java programming language.
Data science provides opportunities for people to express their creativity.
It gives them the means to create technology that can initiate changes worldwide. Think about all of the space exploration, autonomous vehicles, personalized medicine, and personalized education that have been created within the past few years. They are the work of data scientists.
This isn’t all, however.
Data scientists have also created technologies capable of monitoring wildlife migration and optimize energy. The essentiality of data science is no question. In fact, between just 2011 and 2012, job listings for “data scientist” increased by 15,000%.
Find out more about what data science is below.
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Author: Brian Wallace
Brian Wallace is the Founder and President of NowSourcing, an industry leading infographic design agency based in Louisville, KY and Cincinnati, OH which works with companies that range from startups to Fortune 500s. Brian also runs #LinkedInLocal events nationwide, and hosts the Next Action Podcast. Brian has been named a Google Small Business Advisor for 2016-present and joined the SXSW Advisory Board in 2019.