(Newswire.net — September 23, 2020) — Data science is a multifaceted field and has its application across almost every industry, be it healthcare, business, e-commerce, or banking. In the US, a data scientist’s job is the number one job by Glassdoor for four consecutive years! Data science is not just a buzzword but a need for the industry. But what does the future of data science speak?
An overview of Data Science
Data science is a skill that includes collecting, managing, shaping, storing, and analyzing the data to produce data-driven solutions for organizations. The data is contained in every minute interaction with the technology like signing in to your email, social media feed, purchase from e-commerce websites, etc. Data science is beneficial for both the organization and the customer equally.
Data science unifies data analysis, statistics, and other related methods to analyze and understand the actual phenomenon. The best example of data science observed in daily life would be the recommendation engine. You must have always noticed the recommendations of products or services whenever you are surfing on the internet. These “things you may like” are mostly related to what you have searched before.
Current Data Science Scenarios
The size of big data is astonishing. It has braided itself in-depth with individual aspects and business life. The expert intuition, authoritarian whim, is no longer relied upon due to the advent of data science. Data science has built systems that can talk, envision, foresee, and give appropriate & real outcomes. In today’s date, there is around 2.5 quintillion byte of data output. With this, the data load is increasing, and hence there is a rise in data science roles.
By the end of 2020, the data science roles are expected to be increased by twenty-eight percent. The data scientists can be called as the wizards of all the business solutions. The organizations are expecting a lot from the data scientists working for them. The business leaders are adopting trends to keep their data-technology and business priorities set. Few of the movements are as followed:
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Augmented Analytics: If a business wants to differentiate itself from the competitors, the business leaders are adopting augmented analytics. The growth of IoT and the adoption of cloud computing are two significant drivers of augmented analytics.
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Conversational Analytics and NLP: The conversational analytics and natural language processing (NLP) will be a companion to Augmented Analytics. The customer experience, supply chain, financial operations, and talent management systems are driven by the data and analytics cooperatively.
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Continuous Intelligence and Graph Database: More than fifty percent of the upcoming business solutions are expected to incorporate continuous intelligence. It tends to guide business solutions by utilizing real-time data.
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Automated Data Management: There is a shortage of data technology experts where data is increasing exponentially. Business leaders are looking for tools or platforms which provide automated business analytics and data science.
Scope of Data Science in the future
As mentioned earlier, Glassdoor named data scientists as the best job in the US. Not limited to this, LinkedIn named it as the most promising career in the year 2019. It’s not just the power of earning in this field but also the most critical job satisfaction factor. The people in the role of a data scientist are also readily promoted. Data Science is providing a lucrative career path as of now. Let us now understand it’s future scope.
As labeled “promising,” it clearly describes the need of data scientists in a large number. In the role of curating knowledge from the data, the demand for specialized experts is increasing gradually. There is a dire need to support new ideas and solve the problems by making sense of massive data gathered every day.
Data scientists are rare breeds who mix data visualization, statistics, machine learning, database, data preparation, and coding. And hence, many of the companies are trying to hire data scientists. For example, the MHR Analytics made a data search report that says that more than eighty percent of the UK companies are thinking of hiring data consultants or data scientists. It justifies why data science is said to be a hot technology!
With the ever-increasing data, the need for data scientists will also keep on growing. Data science lays a promising career path for every data science enthusiast. One needs to grab on data science pillars – computer science, mathematics, statistics, and communication.
Opportunities in Data Science
Data science offers various job roles; here are a few leading careers in data science:
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Data Scientist: The data scientists are expected to curate, sort, and organize the data for the organizations. They must have the ability to analyze critical raw data and find meaningful information that will benefit the organization in coming up with data-driven decisions.
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Machine Learning Engineer: The machine learning engineer is expected to create data funnels and provide software solutions. A machine learning engineer needs to be strong in programming skills, statistics, and computer science or software engineering knowledge. A machine learning engineer builds and creates machine learning systems and conducts experiments and tests to monitor the systems’ functionality and performance.
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Data Analyst: The data analysts are expected to work on the large data set, manipulate, and transform it into the organizations’ desired analysis. They also collaborate with organization leaders to develop decision-making with the help of the analysis outcome.
Many other career options like Machine learning scientist, Application Architect, Enterprise Architect, Infrastructure Architect, Data Architect, Data Engineer, BI (Business Intelligence) Developer, Statistician, etc.
Bottom Line
There are many opportunities in which you can put yourself in challenging and exciting roles by preparing yourself. Since data science, engineer’s are required in every industry, there are plenty of job opportunities. One needs to prove their expertise in data science with their skills and previous work.