A list of data science careers shaping our future

For four years in a row, Glassdoor has named a Data Scientist most popular job appointed in the USA. In addition, the U.S. Bureau of Labor Statistics predicts demand for data science skills will lead to an increase in employment in the field by 2026 27.9 percent will lead. Not only is there great demand, but there is also a noticeable shortage of qualified data scientists.

Daniel Gutierrez, managing editor of insideBIGDATA, told Forbes : “There is definitely a shortage of people on the street who can do data science.” If you have a passion for computers, math, and discovering answers through data analysis, then an advanced degree in data science or data analytics could be your next step.

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What is data science?

Martin Schedlbauer , PhD and professor of data science at Northeastern University, says that data science is used by "computer professionals who have the skills to collect, design, store, manage and analyze data [as a] critical resource for organizations to enable data-driven decision making to make it possible." Almost every interaction with technology involves data - your Amazon purchases, your Facebook feed, your Netflix recommendations, and even the facial recognition required to log into your phone.

Amazon is a prime example of how helpful data collection can be for the average shopper. Amazon's records remember what you bought, what you paid for, and what you searched for. In this way, Amazon can adapt the subsequent views of its homepage to your needs. For example, if you're looking for camping gear, baby items, and groceries, Amazon won't bombard you with ads or product recommendations for geriatric vitamins. Instead, you'll be shown items that you can actually benefit from, such as: B. a compact camping high chair for small children.

Likewise, data science can be useful in reminding you of habitual purchases. If you e.g. For example, if you order diapers every month, you might see a strategically placed coupon or offer at the same time each month. This use of data is intended to act as a trigger and make you think:

"I just remembered that I need to buy diapers and I should buy them now because they're on sale."

Data science benefits companies and consumers alike. The McKinsey Global Institute has found that big data can increase a retailer's profit margin by 60 percent and that "services enabled by personal location data can enable consumers to generate $600 billion in economic surplus." , d. H. they may purchase a good or service at a lower price than expected. For example, if you budgeted $7,500 to purchase a hot tub and then find the exact model you want for $6,000, your economic surplus is $1,500. Data science can simultaneously increase retailers' profitability and save consumers money, which is a win-win for a healthy economy.

Why is data science important?

Data science allows retailers to influence our purchasing habits, but the importance of data collection goes much further.

Data science can improve public health through wearable trackers that can motivate individuals to adopt healthier habits and raise awareness of potentially critical health issues. Data can also improve diagnostic accuracy, speed the search for cures for certain diseases, or even stop the spread of a virus. When the Ebola virus broke out in West Africa in 2014, scientists were able to track the spread of the disease and predict which areas were most vulnerable to the disease. This data helped health authorities get ahead of the outbreak and prevent it from becoming a global epidemic.

Data science has important applications in most industries. For example, data is used by farmers to produce and deliver food efficiently, by food suppliers to reduce food waste, and by nonprofits to increase fundraising and predict financing needs.

In a 2015 speech, economist and Freakonomics author Steven Levitt said that while CEOs know they are missing the importance of big data, they don't have the right teams in place to acquire the skills. He says: "I really still believe that the combination of collaboration with big data and enterprise randomization [...] will be absolutely at the heart of what economics is and what other social sciences will be in the future."

A career in data science is a smart move, not only because it's trendy and pays well, but because data could very well be the fulcrum on which the entire economy turns.

In-Demand Careers in Data Science

Data science experts are needed in virtually every field of work - not just technology. In fact, the five largest tech companies - Google, Amazon, Apple, Microsoft and Facebook - employ just half a percent of the U.S. workforce. However, getting into these high-paying, in-demand positions typically requires advanced training.

"Data scientists are very well educated - 88 percent have at least a master's degree and 46 percent have a doctorate - and while there are notable exceptions, a very good educational background is typically required to develop the depth of knowledge required to be a data scientist," reports KDnuggets, a leading Big Data website.

Here are some of the top data science careers you can get into with an advanced degree. Since we don't have the numbers in the DACH region, we rely on American values.

1. Data Scientist

Average salary: $117,212

Typical Job Requirements: Find, clean, and organize data for companies. Data scientists must be able to analyze large amounts of complex raw and processed data to find patterns that are beneficial to a company and help make strategic business decisions. Compared to data analysts, data scientists are much more technical.

Learn more: What does a data scientist do?

2. Machine Learning Engineer

Average salary: $131,001

Typical job requirements: Machine learning engineers create data funnels and deliver software solutions. You usually need good statistics and programming skills as well as knowledge of software engineering. In addition to designing and developing machine learning systems, they are also responsible for conducting tests and experiments to monitor the performance and functionality of such systems.

3. Machine learning scientists

Average salary: $137,053

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 have titles such as research scientist or research engineer.

4. Application Architect

Average salary: $129,000

Typical Job Requirements: Track the behavior of applications used in an organization and how they interact with each other and users. Application architects also focus on designing the architecture of applications, including developing components such as user interface and infrastructure.

5. Enterprise Architect

Average salary: $150,782

Typical Job Requirements: An enterprise architect is responsible for balancing a company's strategy with the technology needed to achieve its goals. To do this, he must have a thorough understanding of the business and its technological requirements in order to design the system architecture necessary to meet these requirements.

6. Data Architect

Average salary: $118,868

Typical job requirements: Ensuring data solutions perform and designing analytics applications for multiple platforms. In addition to developing new database systems, data architects often look for ways to improve the performance and functionality of existing systems and work to provide access to database administrators and analysts.

7. Infrastructure Architect

Average salary: $127,676

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

8. Data Engineer

Average salary: $112,493

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

9. Business Intelligence (BI) Developer

Average Salary: $92,013

Typical Job Requirements: BI developers design and develop strategies to help business users quickly find the information they need to make better business decisions. They are extremely data literate and use BI tools or develop custom BI analytics applications to help end users understand their systems.

10. Statistician

Average Salary: $88,989

Typical Job Requirements: Statisticians collect, analyze, and interpret data to identify trends and relationships that can inform organizational decision-making. In addition, statisticians' daily tasks often include designing data collection processes, communicating results to stakeholders, and advising on corporate strategy.

11. Data Analyst

Average salary: $69,517

Typical Job Requirements: Transform and manipulate large data sets to create desired analytics for businesses. For many companies, this task may also include tracking web analytics and analyzing A/B tests. Data analysts also help in decision making by creating reports for management that effectively communicate trends and insights from their analysis.

Data scientists are constantly in demand

Schedlbauer concludes that while some data science work will likely be automated in the next 10 years, "there is a clear need for professionals who can understand a business need, develop a data-driven solution, and then implement that solution."

Data science experts are needed in almost every field, from government security to dating apps. Millions of companies and governments rely on big data to be successful and better serve their customers. Data science careers are in high demand, and this trend isn't going to slow down anytime soon, if ever.

Entry into the professional field

If you want to enter the field of data science, there are a number of ways you can prepare for these challenging yet exciting tasks. Perhaps most importantly, you will impress future employers with your expertise and previous professional experience. One way to gain these skills and experience is to pursue an advanced degree program in your area of ​​interest.