Data science experts are needed in virtually every job sector—not just in technology. In fact, the five biggest tech companies—Google, Amazon, Apple, Microsoft, and Facebook—only employ one half of one percent of U.S. employees. However—in order to break into these high-paying, in-demand roles—an advanced education is generally required.
“Data scientists are highly educated–88 percent have at least a master’s degree and 46 percent have PhDs–and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist,” reports KDnuggets, a leading site on Big Data.
Here are some of the leading data science careers you can break into with an advanced degree.
Data science has been viable in handling some genuine issues and is as a rule progressively embraced across ventures to drive more canny and better-educated direction. With the expanded utilization of PCs for everyday business and individual activities, there is an interest for keen machines, can learn human conduct and work designs. This brings Information science and enormous information examination to the bleeding edge.
A review says that the worldwide Data science market is assessed to develop to USD 115 billion out of 2023 with a CAGR of ~ 29%. A report by Deloitte Access Financial matters says that a gigantic 76% percent of organizations have plans to expand their spend throughout the following two years on expanding their information logical abilities. Practically everything ventures can profit from information science and investigation. Notwithstanding, underneath are a few ventures that are better ready to utilize information science and investigation.
Retailers need to effectively expect what their clients need and afterward give those things. On the off chance that they don’t do this, they will probably be left behind the opposition. Huge information and examination give retailers the bits of knowledge they need to keep their clients glad and getting back to their stores. One concentrate by IBM said that 62% of retail respondents asserted that experiences gave by investigation and data gave them upper hands.
There are numerous ways retailers can utilize huge information and investigation to make their customers want more and more. For example, retailers can utilize enormous information and examination to make hyper-individual and important shopping encounters that make their clients profoundly fulfilled and more inclined to settling on buy choices.
The clinical business is utilizing enormous information and investigation amazingly to further develop wellbeing in an assortment of ways. For example, the utilization of wearable trackers to give significant data to doctors who can utilize the information to give better consideration to their patients. Wearable trackers likewise give data like whether the patient is taking his/her prescription and following the right treatment plan.
Information aggregated after some time give doctors extensive data on patients’ prosperity and give considerably more noteworthy information than simply diminutive in-person visits.
Enormous information and investigation can likewise help clinic administrators further develop mind and decrease holding up occasions. Clinical information is an extraordinary illustration of how suppliers can see a lot of information to track down designs and endorse suitable approaches.
3. Banking and Finance
The financial business is for the most part not took a gander at as being one that utilizes innovation a great deal. In any case, this is gradually changing as brokers are starting to progressively utilize innovation to drive their direction.
For example, the Bank of America utilizes normal language handling and prescient investigation to make a remote helper called Erica to assist clients with survey data on forthcoming bills or view exchange accounts.
Erica, the remote helper, is additionally prepared to get more brilliant with each exchange. Bank of America delegates say that the associate will ultimately concentrate on their clients’ financial propensities and recommend important monetary guidance at proper occasions.
It is nothing unexpected that development organizations are starting to accept information science and examination amazingly. Development organizations track everything from the normal time expected to wrap up responsibilities to materials-based costs and everything in the middle. Large information is currently being utilized incredibly in the development business to drive better independent direction.
There is always a need for people to reach their destinations on time and data science and analytics can be used by transportation providers, both public and private, to increase the chances of successful journeys. For instance, Transport for London uses statistical data to map customer journeys, manage unexpected circumstances, and provide people with personalized transport details.
Public transport officials also use predictive analytics to keep things functioning smoothly. In 2017, Americans took 10.1 billion public transit trips. The substantial data generated from these trips can allow data scientists to analyze this data to ensure that all obstacles are properly dealt with.
5. Communications, Media, and Entertainment
Consumers now expect rich media in different formats as and when they want it on a variety of devices. Collecting, analyzing, and utilizing these consumer insights is now a challenge that data science is stepping in to tackle. Data science is being used to leverage social media and mobile content and understand real-time, media content usage patterns. With data science techniques, companies can better create content for different target audiences, measure content performance, and recommend on-demand content.
For example, Spotify, the on-demand music streaming service, uses Hadoop big data analytics to collect and analyze data from its millions of users to provide better music recommendations to individual users.
One challenge in the education industry where data science and analytics can help is to incorporate data from different vendors and sources and use them on platforms not designed for varying data.
For example, the University of Tasmania with over 26,000 students has developed a learning and management system that can track when a student logs into the system, the overall progress of the student, and how much time is spent on different pages, among other things.
Big data can also be used to measure teachers’ effectiveness by fine-tuning teachers’ performance by measuring against subject matter, student numbers, student aspirations, student demographics, and many other variables.
7. Manufacturing and Natural Resources
The increasing demand and supply of natural resources, such as oil, minerals, gas, metals, agricultural products, etc. has led to the generation of huge amounts of data that is complex, difficult to handle, and a prime candidate for big data analytics. The manufacturing industry also generates huge amounts of data that has so far gone untapped.
Big data allows decision-making to be supported by predictive analytics in the natural resources industry. Large amounts of geospatial data, text, temporal data and graphical data can be analyzed using data science to ingest and integrate these large datasets. Big data also has a role to play in reservoir characterization and seismic interpretation, among others.
Big data has many applications in the public services field. Places where big data is/can be used include in financial market analysis, health-related research, environmental protection, energy exploration, and fraud detection.
One specific example is the use of big data analytics by the Social Security Administration (SSA) to analyze large numbers of social disability claims that come in as unstructured data. Analytics is being used to rapidly process medical information and detect fraudulent or suspicious claims. Another example is the use of data science techniques by the Food and Drug Administration (FDA) to identify and analyze patterns related to food-related diseases and illnesses.
9. Energy and Utilities
he energy and utilities industry generates and will continue to generate huge amounts of data that can be analyzed using big data analytics. For instance, nowadays, smart readers allow data to be collected every 15 minutes or so as compared to how it was previously when it was once a day. This data can be used to better study the consumption of utilities, which in turn allows for better control of utility use and improved customer feedback. The use of big data by utility companies also allows for improved asset and workforce management and is useful for identifying and correcting errors as soon as possible.
10. Outsourcing Industry
The value of the global data science and analytics outsourcing market was US$ 2.49 Bn in 2018 and is expected to grow to USD 19.36 Bn by 2027 at a CAGR of 25.8%. Factors driving this growth are shortage of skilled resources and high adoption by diverse industries.
Outsourcing companies are not far when it comes to Data Science Services. They are making use of data science to automate back-office processes, keep prices in check, and shorten the turnaround time. Flatworld Solutions is one such company using artificial intelligence (AI) and machine learning (ML) to automate the back-end processes for clients to automatically classify and index documents, process PDF files, name and classify files, automatically discover documents, use image annotation for inventory management, and more.
Conclusion: The term ‘Data Science’ was first coined in 2001 and it took less than two decades for it to become the phenomenon it is today. Finance was the first industry to understand data science advantages when no one could and used it to sift through and analyze large amounts of data and help companies reduce losses.
Today, Data Science is a force to reckon with and almost all industries are trying to leverage it’s potential, and this number will only continue to increase as data science technology becomes more reliable and cost-effective. However, to capitalize on data science opportunities, you will need to understand industry-specific challenges, understand data characteristics of each industry, and match market needs with custom capabilities and solutions.