The Evolution Of Technology: From Specialist Jobs To Generalist Roles
Organizations must make use of their data in meaningful ways to achieve their goals. Doing this often is an evolutionary process that involves a variety of talent.
As we continue to learn and develop new skills and knowledge, where will we fit into the organizations in the future?
Read the following article. The author describes five types of data science workers in the future. How can you use this information to prepare for these roles?
Which role best suits your strengths? Does this predicted job classification model fit your current company?
Where do you see yourself in the next five years?
Of course, not everyone wants to become a data scientist, but everyone needs to understand the data that is important to their field. How can I use what I learned in this program to be successful in the future?
Calculators were once people. Web-mastering was once a popular career. In the past, middle management had secretaries.
In any case, advances in hardware and software required specialized skills and placed them in the hands of generalists. While skilled jobs have been lost, the democratization of these technologies has sparked a wave of innovation, trade, and job creation.
Similarly, I believe that the work of a data scientist as we know it today will be largely unrecognizable in 5-10 years. Instead, end users in all sectors of the economy will use data science software in the same way that the average person uses Excel today. In fact, these data science tools may just be another tab in Excel 2029.
Today, there is little need for financial analysts to hire data scientists to support them, as the platforms they use already have the data science tools they need. This will become common in many other fields as well, as a basic understanding of data science will be a required skill for many jobs. Many of today’s data science tasks are now automated, with some observers warning that the jobs of data scientists could be automated.
Careers in data science are experiencing a gold rush. A 2018 Bloomberg article praised data science as “America’s most popular job,” and job site Indeed.com saw a 75% increase in job openings for data scientists from January 2015 to January 2018. He pointed out that he did. Some consulting firms have PhDs in data science, and their salaries are $300,000, the article said. Dozens of U.S. universities are currently launching data analytics programs. The University of California, Berkeley introduced a new data science major in 2018, and it quickly became one of the school’s most popular majors. The university created a new School of Data Science and Information Studies in November, calling it “the biggest organizational change in decades.”
The growing popularity of data science
https://www.forbes.com/sites/forbestechcouncil/2019/03/01/radical-change-is-coming-to-data-science jobs/?mkt_tok=eyJpIjoiTnpVd1pEQTJOakk0TXp… 3/5
But all of these young people are in careers that may not be recognized in 10 years. Data science skills can be a huge advantage in your career, but surprisingly few people work as pure data scientists. At the time, when I was studying computer science, compiler design was a compulsory subject. We needed to know how to translate programming languages like C directly into machine language, which is hexadecimal code that computers can directly interpret. It was common to write parts of commercial applications in machine language to improve performance.
Over the past few decades, successive layers of software functionality have been abstracted into higher-level development tools. Most coding today is done in easy-to-learn high-level languages like Python, and relatively few programmers need to know how to communicate directly with hardware. Data science is quickly following the same trajectory. Over the next 3-5 years, higher-level tools will increase expertise in underlying technologies such as high-performance computing (splitting problems across multiple CPUs), data munging (preparing raw data for analysis), and internal structure. The need for knowledge becomes less and less. machine. Learning systems or low-level statistical methods. All of this is handled internally.
Dozens of companies are currently implementing new data analysis tools, including Trifacta, Element Analytics, and Kylo. Many are aimed at reducing tedious data preparation tasks and allowing data scientists to jumpstart their analysis efforts. Data science frameworks are also emerging that automate algorithm selection and parameter optimization (e.g. Auto-sklearn, DataRobot). These frameworks and tools, combined with data management platforms, create a great building block for future data consumers.
Over the next few years, I predict that data scientists will fall into at least five types of workers.
The first group are data science generalists who interpret and make data usable. These generalists focus on educating end users and helping them with questions about the data, rather than finding all the answers themselves. This is likely a transitional role, and he will likely be more common in five years than he will be in ten.
The second and largest group consists of industry experts who apply data science techniques and tools to specific industries such as manufacturing, healthcare, and finance. I think most jobs will be created here. However, these are not considered a career in data science. This employee will be a data science-savvy manufacturing manager rather than a manufacturing-savvy data scientist. Today’s equivalent is a researcher who is a master of statistics.
The third and smallest group consists of designated experts in specific data science and technology. Here are the remaining pure data science jobs. Your role is to perform abstract data science, improve the performance of algorithms, and design new generalized approaches. They are like today’s computer scientists: instead of solving everyday problems, they end up building theoretical foundations.
The fourth group moves from data scientists to analytics developers. These are software development specialists who deal with data interactions and help people draw conclusions from data reports. Designing algorithms is just one part of their job, supported by a data platform and robust code libraries that perform much of their work on a turnkey basis.
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