Major Data Science Career Choices that are shaping the Future

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value. Data science is used by computing professionals who have the skills for collecting, shaping, storing, managing, and analyzing data as an important resource for organizations to allow for data-driven decision-making. Almost every interaction with technology includes data—your Amazon purchases, Facebook feed, Netflix recommendations, and even the facial recognition required to sign in to your phone. Amazon is a prime example of just how helpful data collection can be for the average shopper. Amazon’s data sets remember what you have purchased, what you have paid, and what you’ve searched. This allows Amazon to customize its subsequent homepage views to fit your needs. For example, if you search camping gear, baby items, and groceries, Amazon will not spam you with ads or product recommendations for geriatric vitamins. Instead, you are going to see items that may actually benefit you, such as a compact camping high chair for infants.

Similarly, data science can be useful for reminding you of habitual purchases. If you order diapers every month, for example, you might see a strategically placed coupon or deal around the same time each month. Data science benefits both companies and consumers alike. In a study it was found that big data can increase a retailer’s profit margin by 60 percent, and “services enabled by personal-location data can allow consumers to capture USD 600 billion in economic surplus,” meaning they are able to purchase a good or service for less than they were expecting. For example, if you budgeted USD 7,500 to purchase a jacuzzi and then found the exact model you wanted for USD 6,000, your economic surplus would be USD 1,500. Data science can simultaneously increase retailer profitability and save consumers money, which is a win-win for a healthy economy.

Significance of Data Science

More and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind. Data science enables retailers to influence our purchasing habits, but the importance of gathering data extends much further. Data science can improve public health through wearable trackers that motivate individuals to adopt healthier habits and can alert people to potentially critical health issues. Data can also improve diagnostic accuracy, accelerate finding cures for specific diseases, or even stop the spread of a virus. When the Ebola virus outbreak hit West Africa in 2014, scientists were able to track the spread of the disease and predict the areas most vulnerable to the illness. This data helped health officials get in front of the outbreak and prevent it from becoming a worldwide epidemic. Data science has critical applications across most industries. For example, data is used by farmers for efficient food growth and delivery, by food suppliers to cut down on food waste, and by nonprofit organizations to boost fundraising efforts and predict funding needs. As per the leading economists, the CEOs know they are missing out on the importance of Big Data, but they do not have the right teams in place to perform the skills. He says, “I really do believe still that the combination of collaborations with firms’ big data and randomization is absolutely going to be at the center of what economics is and what other social sciences are going forward.” Pursuing a career in data science is a smart move, not just because it is trendy and pays well, but because data very well may be the pivot point on which the entire economy turns.

Career in Data Science and its future prospects

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 a leading site on Big Data. Here are some of the leading data science careers you can break into with an advanced degree.

Data Scientist

Typical Job Requirements: Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are much more technical.

Machine Learning Engineer

Typical Job Requirements: Machine learning engineers create data funnels and deliver software solutions. They typically need strong statistics and programming skills, as well as a knowledge of software engineering. In addition to designing and building machine learning systems, they are also responsible for running tests and experiments to monitor the performance and functionality of such systems.

Machine Learning Scientist

Typical Job Requirements: Research new data approaches and algorithms to be used in adaptive systems including supervised, unsupervised, and deep learning techniques. Machine learning scientists often go by titles like Research Scientist or Research Engineer.

Applications Architect

Typical Job Requirements: Track the behavior of applications used within a business and how they interact with each other and with users. Applications architects are focused on designing the architecture of applications as well, including building components like user interface and infrastructure.

Enterprise Architect

Typical Job Requirements: An enterprise architect is responsible for aligning an organization’s strategy with the technology needed to execute its objectives. To do so, they must have a complete understanding of the business and its technology needs in order to design the systems architecture required to meet those needs.

Data Architect

Typical Job Requirements: Ensure data solutions are built for performance and design analytics applications for multiple platforms. In addition to creating new database systems, data architects often find ways to improve the performance and functionality of existing systems, as well as working to provide access to database administrators and analysts.

Infrastructure Architect

Typical Job Requirements: Oversee that all business systems are working optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy.

Data Engineer

Typical Job Requirements: Perform batch processing or real-time processing on gathered and stored data. Data engineers are also responsible for building and maintaining data pipelines that create a robust and interconnected data ecosystem within an organization, making information accessible for data scientists.

Business Intelligence (BI) Developer

Typical Job Requirements: BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems.

Statistician

Typical Job Requirements: Statisticians work to collect, analyze, and interpret data in order to identify trends and relationships which can be used to inform organizational decision-making. Additionally, the daily responsibilities of statisticians often include designing data collection processes, communicating findings to stakeholders, and advising organizational strategy.

Data Analyst

Typical Job Requirements: Transform and manipulate large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing. Data analysts also aid in the decision-making process by preparing reports for organizational leaders which effectively communicate trends and insights gleaned from their analysis.

Data Scientists Are in Constant Demand

While some data science work will likely be automated within the next 10 years, “there is a clear need for professionals who understand a business need, can devise a data-oriented solution, and then implement that solution.” Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. Data science careers are in high demand and this trend will not be slowing down any time soon, if ever.

If you want to break into the field of data science, there are a number of ways you can prepare yourself to take on these challenging yet exciting roles. Perhaps most importantly, you will need to impress future employers by demonstrating your expertise and previous work experience. One such way you can build those skills and experience is to pursue an advanced degree program in your area of interest.

Gone are the days when devices were limited to personal computers, as gadgets owned by a single person have increased three-fold, producing more data to be extracted. The exponential rise of data will unlock new business opportunities across the globe, therefore increasing the demand for professionals to handle this data. It has been more than a decade since the Harvard Business Review announced data science as ‘the most sought-after jobs of 21st century’. Since then, the data science field has widened to include plenty of opportunities for skilled professionals. According to LinkedIn, data science is among the top 15 emerging careers in the world. With AI and machine learning taking its place in the field and the global business sector depending largely on the insights derived from data to plan business strategies, the career opportunities for data science will always be rich. Just like data analytics expanded to form data science, the latter is also spreading its wings to cover various disciplines that may benefit the processing of data. With increasing demands around the world, working as a data scientist can be a lucrative and satisfying career choice. All you need is the right skill-set which can be developed on the Master’s in Data Science programme offered at the University of Europe for Applied Sciences in Berlin. This programme will equip you with the relevant skills required to succeed in the data science field. The course has been created in line with the demands of the global data science job market which will help you land good job roles. Additionally, this master’s programme offers interdisciplinary electives where students have the chance to tailor their studies to their career goals. Industry expert instructors will guide you through the practical applications of data analytics and provide you with guidance to establish a successful career.

Today thousands of individuals learn Data Science related skills through college degrees or the numerous resources that can be found online and this could result in newer aspirants getting a feeling of saturation in this domain. However it is essential to realize that data science is not a domain that can just be learned, it needs to be inculcated. No doubt the skills being learned are of immense importance, but these are just the tools that help to work with the data. The mindset and the applicative sense of using these tools to accomplish various analytical tasks is what makes a true data scientist. Thus it should be remembered that there could always be an abundance of individuals who have learned Data Science, but there would always be a scarcity of Data Scientists. The future of Data Science is not definitive however what is certain is that it would continue to evolve into a new phase depending on the need of the hour. Data Scientists would exist as long as there exist Data. Data Science is one of the leading sectors in the technical field that is going to usher a new era of advancements in technology in near future.