Why a Data Scientist May Be the Hottest Career of the Decade
Data Scientist Is Going To Be A Hot Career This Decade
Data Science Is a Hot and Growing Field
If you would like to stay ahead of the ever-changing job market, learning about why a data scientist may be the hottest career of the decade is well worth the effort. Career consultants estimate that data scientists will be in high demand for many years to come and there could actually be a shortage of data scientists by 2018. This is because there is growing demand for the analytical information and competitive intelligence that data scientists provide to organizations trying to maintain an edge over their competition.
What Makes A Data Scientist
What Is Data Science and What Are Data Scientists?
Data science is an umbrella term that brings together a number of fields of inquiry, techniques, and methodologies that have the end goal of obtaining meaningful and actionable information from large amounts of data that can be used to assess conditions, identify trends, and ultimately assist an organization in decision making. If that was hard to comprehend, think of data science as a way to boil down enormous amounts of data into useful information that can be acted upon. The uses of the data collected and presented by data scientists vary from keeping a competitive edge over business competitors to targeting advertising in an effective manner to predicting trends, amongst many others.
Facebook is a good example of a company that uses data science in a practical way. While Facebook is many things to many people, at its core Facebook is a huge data collection enterprise. Every time a Facebook user innocently selects “Like”, a new piece of data has been collected by Facebook that the company can use to target advertising. Facebook’s voluminous data is scrutinized using data science methodologies to provide powerful targeted advertising options to advertisers looking to reach people who are inclined to buy their products or use their services.
Although many areas of expertise are utilized in data science, the three core areas of expertise used by data scientists to collect and analyze data are statistics, mathematics, and computer science. Since the practice of data science requires expertise in all of these core fields, data scientists work in teams to analyze data and draw conclusions from the data, building off the intellectual strengths of each member of the team.
Data scientists have to be creative and technically adept to manage and assess data, bring data together from various sources into one data set, and develop tools that people can use to interpret and use the data they collect. One creative way of obtaining data is using the Internet to perform data mining. The Internet is loaded with data that on its own may not be very useful, but by using data mining methods to search out and retrieve various types of data available online, data scientists can analyze and coalesce the data into useable information.
You may even be practicing the arts of data mining, data analysis, and data science without even realizing it. Computer savvy homebuyers are well aware of the great amount of data that can be obtained online about homes they are considering buying. Visiting one website alone may not offer a great amount of data about a particular home under consideration; however, by visiting a number of websites and taking note of and analyzing the data that they provide about a home, prospective home buyers can develop a clear understanding of what a home is realistically worth and potential drawbacks. For example, what the current homeowners paid for a home, the features of a home, the values of surrounding homes, whether the home is in foreclosure, the ranking of local schools, local amenities that may increase the value of a home, etc.
On a broader scale, data scientists can mine and analyze reams of data related to the housing market to identify inflection points and trends, such as when the housing market has reached the end of a long price decline and started to recover. Real estate investors and others with interests in the real estate industry cab use the conclusions of the data scientists regarding a housing price trend reversal to make strategic investment and business decisions.
What Makes A Data Scientist A Good Career Choice?
While the art of data science has been around for decades, the term “data scientist” is relatively new (being less than a decade old) and is just reaching mass consciousness. Many employment experts believe that the demand for data scientists is going to explode in coming years, as companies scramble to use data science methodologies to gain a competitive advantage in the marketplace. This is what makes a data scientist a good career choice.
McKinsey Global Institute studied the future of the data scientist job market and concluded that up to 500,000 people will be employed as data scientists by the end of this decade, and these data scientists will require hundreds of thousands of support staff to obtain, analyze, and present the data. While predicting future job vacancies is anything but an exact science, the McKinsey Global Institute estimates that 190,000 data scientist positions will go unfilled later this decade, unless a serious effort is made to educate a new generation of data scientists.
Colleges and universities have taken note of the increasing demand for data scientists and are increasingly offering data science degrees and course offerings. Related statistics, mathematics, and computer science degrees can also be utilized to land a job as a data scientist. Even if you do not have an advanced degree, there will be opportunities to work in the field of data science as one of the many support personnel that the field will need. Certainly, learning some of the techniques that data scientists utilize, such as data mining and data analysis, are worthwhile for anyone looking to enhance their job skills and potential for career advancement.
If you are interested in developments in data science, Columbia University publishes a journal called “The Journal of Data Science” that is utilized by data scientists to exchange information and provide details regarding the latest developments in the field.
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