What kinds of jobs can someone do with a master’s in data science?

Career Opportunities in Data Science

Note that this article has been created by collaging many articles mentioned in the “References” section.

Data science is one of the fastest growing and most in-demand careers today. Learning skills in this area is an exciting way to grow or change your career.

Data scientists are needed across many industries. Job openings are surging because businesses are producing useful data in expanding volumes. Experts are hired to accomplish tasks such as to predict market trends, boost sales conversion rates, reduce costs, and even design new devices.

What exactly do data scientists do?

Data scientists analyze information. They take a multidisciplinary perspective, drawing from areas such as programming, machine learning, statistics, software engineering, human behaviour analysis, linear algebra, experimental science and data intuition. Data scientists solve problems and find new insights into how an objective can be achieved.

After asking questions related to a fundamental problem, data scientists will work with raw data, collecting, organizing and analyzing it. They create and use algorithms for the identification of patterns and trends in the work of answering questions.

Then, after answering the questions at hand, data scientists use the analyzed data to create visualizations. This is an important part of the task of presenting data analysis and findings. Insights must be shown in a way that is accessible for colleagues who aren’t trained or knowledgeable in technology.

Is data science right for you?

Successful data scientists have aptitude in the fields of maths, programming and statistics. Data scientists collect information and data, sorting and analyzing it. They use different kinds of data sources when problem-solving and addressing questions. Heard of algorithms? You’d be creating these as a data scientist.

You’ll use statistics in your analysis and employ complex concepts, including data visualization and machine learning. If you enjoy identifying patterns and trends, data science may well be the perfect field for you.

Computer programming, AI, and even areas such as human behavior are fields where proficiency will be key to your success as a data scientist. If you have a curious mind and are a keen problem-solver, data science is certainly a field you may want to train for.

Perhaps it will surprise you that creativity is also an important part of this scientific field. Keeping an open mind and being willing to follow your instincts (after education and training, of course) are key.

Job Paths

1. Data Scientist

Most highly trained data science professionals call themselves a data scientist or similar. The job is to take large amounts of data and transform that into insights on which a business or organization can take useful action. A data scientist is an extremely important addition to a company. This professional provides the information needed for a business or organization to make decisions.

Data scientists are employed across many industries, including large companies and government agencies. There is huge demand for these professionals. So you should find the job search process less challenging than in many other careers, especially if you are even more highly skilled than your competitors.

As a data scientist, you examine data to achieve insights and present these insights to other professionals. This must be done in a way that people without a technical background can understand. Data scientists need to have skills in areas such as computer science, analytics, statistics, modeling and maths. Depending on your organization and its goals, you may also need a reasonable or high degree of business knowledge and sense.

The position of data scientist is usually ranked a bit higher than that of data analyst. For example, a data scientist may create a complex data model that a data analyst may then use on a daily basis to produce business reports. Data scientists are usually fluent in programming languages such as SQL, Python and R.

A data scientist designs processes for data modeling. These processes are needed to create predictive models and algorithms, as well as custom analysis. This professional must work with business stakeholders and reach conclusions about how data should be utilized to reach objectives and goals.

Job titles: data science lead, data scientist (advanced analytics), data science lead – digital platforms, data science manager, data scientist, data scientist – government, data scientist – machine learning / computer vision / NLP, data scientist (computer vision and deep learning), data scientist (maintenance), data scientist / analyst, data scientist / engineer, head of data science, junior data scientist, junior quantitative researcher, machine learning data scientist, principal data scientist, quantitative researcher, research assistant, research coordinator, research development coordinator, research fellow, senior analyst – data scientist, senior data consultant, senior data scientist, senior manager – data science.

2. Data Analyst

As a data analyst, your responsibilities include not only the analysis of data but its interpretation as well. This combination of skills makes you indispensable to organizations in their decision-making processes. Employers hire data analysts to find new opportunities for increasing revenue and driving down costs.

In the data collection and analysis process, data analysts utilize specific methods. They collect statistics and transform them into information that a business can readily understand and harness for its benefit. Data analysts report their findings to businesses. It’s common for data analysts in the greater DC/MD/VA area to make between $70K and $120K annually.

A few of the data analyst’s job duties include tasks such as:

  • collaborating with business management in the prioritization of information requirements
  • identifying, understanding, and interpreting any patterns or trends found within complex sets of data
  • setting out strategies for the optimization of statistical result quality.

To become a data analyst, you should get a bachelor’s degree in a data analytics or a related subject such as data science or big data management. Some employers like to see a master’s degree too.

Once you’ve done your degree, think about doing an internship. An entry-level job (for example, as a statistical assistant or technician) is also an effective way to get a foot in the door. Data analysts usually know Microsoft Excel, SQL, Tableau and Python.

Job titles: data analyst, data analyst – energy fleet analysis, data analyst / junior data scientist, data analytics consultant, data analytics project manager, data and analytics senior analyst, data infrastructure analyst, data quality analyst, economic data analyst, junior data analyst / scientist, lead data analyst, problem solver — risk analytics, research and data analytics advisor, security intelligence analyst, senior data analyst, senior data insights analyst, senior auditor (data analytics), senior data analyst / scientist, senior data and insights analyst.

3. Data Manager

Data managers must have a much greater awareness of the business side of things than data scientists. They are key to the achievement of important business goals, and they’re responsible for data flow, processes, and even people coordination wherever relevant.

An effective data manager must be knowledgeable in areas such as:

  • Storage and operations
  • Modeling and architecture
  • Integration and interoperability
  • Security
  • Data governance
  • Data quality
  • Business intelligence and warehousing
  • Management of master data, content and metadata, document data, and reference data

A data manager is responsible for the data of a domain, or perhaps of an entire department or enterprise. You must ensure data integrity throughout the lifecycle, making sure that people who need to use the data can access it in an efficient way.

Job titles: customer success manager — data centre, data centre facility manager, data centre management, data centre operations manager, data insight manager, data project manager, database administrator, data centre operations manager, data engineering manager, data insight manager, data manager, data project manager, investment data manager — analytics, manager – climate data science, manager — data management and delivery, product data manager, manager – data management and delivery,  manager — data modernization, product data manager, programmatic trader, reporting and data manager, senior data manager — informatics and data quality, senior manager – data governance, senior management (data governance), spatial data officer.

4. Data Architect

Data architects are the professionals specifically responsible for the design, implementation, and management of an organization’s data architecture. The position of data architect is more senior than some other career tracks in data science. Entry-level jobs almost never have this job title. Getting a master’s degree in data science or computer science is an excellent idea if becoming a data architect is your ultimate goal.

A career path here is to first get a bachelor’s degree and usually at least between three and five years of experience. Start your career in database administration or programming and then continue strengthening your skills in data warehousing, data modeling, data management, data development, and database design.

Data architects work in industries such as education, finance, insurance, and business. Two of the most significant employers of data architects are software companies and technology manufacturers. These professionals are needed in organizations that deal with enormous quantities of client data.

Job titles: big data architect, cloud solution architect – data science AI, data warehouse architect, data warehouse / business intelligence architect, digital solution architect, information architect, IT / data architect, IT solutions architect, lead solutions architect, senior data architect, senior services architect, solution architect, solution architect – data and analytics, solution architect – PEGA, senior architect – data and integration, senior data solutions architect.

5. Data Engineer

Data engineers work at a more fundamental level than data scientists. In other words, these professionals are the ones who work with the data in its rawer form. It is the work of the data engineer that makes data ready for data scientists to do additional processing. Data engineers may be proficient in several programming languages such as SQL, NoSQL, Apache Spark and Hadoop, as well as Python, R, Java and C++.

A data engineer must work with raw data that has machine, instrument, or human errors. As a data engineer, you work with data that may have problematic records or not be properly validated. It is more challenging to work with because it is unformatted and will have codes that are specific to particular systems.

Data engineers are masters in the field of data science, able to create innovative methods for storing and accessing enormous collections of data. These tech professionals design and create data architecture and tools. They must test them thoroughly in the process. The tools that data engineers build are intended to make the interpretation of a business’s data easier to accomplish. Data engineers are well-paid, making on average between $85K and $180K annually in the DC/MD/VA area.

Data engineers construct data architecture. They’re instrumental in the maintenance of these elements. Another duty will be analyzing and interpreting enormous sets of data. To be a data engineer, you need an exceptionally advanced understanding of many different data analysis tools and programming languages. Data engineering jobs often work for technology companies and the IT departments of businesses and other organizations.

Data engineers develop architecture, constructing it as well as testing and maintaining it. This architecture can include large-scale processing systems and databases. The data scientist’s duties are different in that he or she is the person responsible for cleaning and organizing data.

One of the data engineer’s most important goals is that of improving the efficiency of the business, thus helping it more effectively accomplish its goals. Data engineers test and launch especially advanced tools for data analysis, as well as techniques including machine learning and algorithms.

Job titles: data engineer, data engineer consultant, data engineer – data warehouse, data engineer – machine learning, data engineer – processing and analytics, data science engineer, data engineering manager, junior data engineer, junior integration engineer, lead data engineer, platform engineer – data, senior data engineer.

6. Business Analyst

A business analyst examines and analyses business processes. This professional finds efficiencies and takes on a leadership position when it comes to project teams. The business analyst provides necessary technical information for the business.

Information technology is the most common sector where business analysts are found. Business analysts also work in a range of other business departments. Some of the most common duties and tasks include:

  • Identifying a business’s opportunities and identifying problem statements
  • Producing business documents setting out information in great detail, as well as advanced use of spreadsheets
  • Creating solutions and communicating them to the business
  • Evaluating business processes
  • Report management
  • Data analysis, including pricing, budget forecasts, and plans
  • Effective presentation of data to the business

To become a business analyst, you need a bachelor’s degree in a field such as information systems finance, business administration or another closely related discipline. You can also get a master’s in Business Analytics, Business Administration or Information Systems to make yourself more competitive in the job search.

Job titles: analyst customer communication insights, analyst – primary market research, business analyst, business analyst – marketing, business consultant principal (data management), business intelligence analyst, business intelligence (BI) & data warehouse developer, business intelligence specialist,  customer segment analyst, customer strategy specialist, customer success manager – data centre, insights analyst, insights consultant – data scientist, junior business analyst, performance and quality advisor.

7. Software Engineer

Software engineers differ from data scientists in that their territory centers much more on end-user functionality, as well as application development and feature creation. Their focus is designing and developing software systems. Software engineers are also instrumental in the maintenance of these systems.

Software engineers create applications that generate data that may be used by data scientists. Both professions require strong programming skills.

The types of systems you’d work on can vary widely, encompassing everything from simple applications to intricate online platforms. Software engineers usually play a role in every phase of software development. After release of a product, the software engineer will frequently be responsible for maintenance.

Software engineer salaries in DC/MD/VA can vary significantly depending on the company profile and experience of the engineer. The average salary for software engineers is about $111,000.

Job titles: associate software engineer, backend software engineer, embedded software engineer,  frontend software engineer, graduate software engineer, graduate software engineer – prototype development, junior back end software engineer, junior developer, junior software engineer, lead software engineer, lead software engineer – platform, PHP software engineer, senior software engineer, software engineer, software engineer specialist, software engineer – site reliability engineering, software integration engineer, software quality assurance engineer, software developer, software development engineer intern, software development internship.

8. Machine Learning Engineer

To be a machine learning engineer, you need both data science and software engineering expertise. The objectives and goals of a machine learning engineer are different than those of a data scientist.

A machine learning engineer creates working software. This is different than data scientists and their objective of visualizations and analysis. Just a few of the skills you need as a machine learning engineer include statistics and probability; data evaluation and modeling; system design and software engineering; computer science and programming; and application of machine learning algorithms.

As a machine learning engineer, you’ll develop AI (artificial intelligence) systems and machines. These systems and machines not only learn but apply their knowledge. To do this, you must be highly skilled with sophisticated algorithms and data sets.

Job titles: computer vision engineer – machine learning image processing, machine learning engineer, machine learning solutions lead, machine learning team lead.

9. Statistician

A statistician differs from a data scientist in that he or she focuses only on statistics rather than on all the other disciplines that are part of data science. To be a statistician, you need a university degree (or more than one degree) in statistics or mathematics.

As a statistician, you’ll establish and utilize statistical techniques and theory for the collection, analysis and interpretation of numerical data. This is essential for reaching decisions and creating policy in an organization. Some of the fields and industries in which you may find work as a statistician include, for example, business, medicine, government, science, and education.

Statisticians in can make as much as between $80K and $140K annually.

Job titles: senior biostatistician, senior clinical statistician, senior statistician, statistician, trainee biostatistician.

10. Data Modeler

The work of the data modeler is essential for data scientists to able to do their work. Data modelers build the blueprints for databases. These databases are the storage places for the data used by data scientists.

Like data scientists, data modelers are essential for a business to gain useful information from raw data and then use this information for business decisions. Job responsibilities include:

  • Incorporating data from departments and systems and presenting them in a way that is accessible to decision-makers.
  • Reverse engineering earlier data sets to develop a better grasp of established models.
  • Making sure that the physical model is intuitive through testing.
  • Consulting with executives and other end users to ascertain data standards for the company.

Job titles: data modeler, data modeler / data analyst, credit risk modeler, solution designer / data modeler, modeling geologist, senior credit risk modeler.

What kinds of projects can data scientists work on?

In some ways, the term data science has become a catch-all for many different kinds of responsibilities. Depending on what degree concentration a data scientist chooses, what industry they work in, and where their passions fall, they might participate in projects involving the creation or implementation of:

  • Cyber security tools designed to detect fraud
  • Dashboards and other reporting tools
  • Machine learning models that classify, cluster, or correlate different kinds of data
  • New ways to approach market analysis and sentiment analysis
  • Predictive models that identify future sales trends
  • Robotics models that demonstrate how computer networks learn
  • Systems that can predict when and why transportation accidents are likely to happen
  • Systems that optimize manufacturing processes
  • Tools that can find and eliminate junk data

Some data scientists work in research looking for new data science applications.

What other factors affect salaries in data science?

Education isn’t the only factor determining how much you’ll earn when you become a data scientist. If you dig into the average master’s in data science salary, you’ll discover that there is a vast salary range on sites like Indeed and Monster.com. There are $70,000 data scientist jobs and $200,000 data scientist jobs, and positions with salaries that fall across the range in between.

While it’s true that there are still more data science job openings than there are data scientists to fill them, that isn’t uniformly true across the United States. The local cost of living also plays a role in data science salaries. According to Indeed, data scientists tend to have the highest salaries in California, Connecticut, Delaware, New York, and New Mexico—all of which have a relatively high cost of living. Data scientists in states and cities where the cost of living is lower tend to earn less.

The industry you work in also influences your earning potential. Data scientists who work in the aerospace industry and finance earn the most, according to the US Bureau of Labor Statistics.

Experience is important, too. There’s really no such thing as an entry-level data scientist. Many data scientists are data analysts who advanced into data scientist positions after working in data analytics a couple of years and then earning a master’s degree in data analytics or data science. Nearly all data scientists have master’s degrees—some sources say 88 percent—and almost half have PhDs. That said, a data scientist who has been in the field for 15 years will almost always earn more than one with two or three years of experience, regardless of degree.

The takeaway is that even though the average data scientist salary is relatively high, earning a master’s degree in data science is no guarantee that you’ll end up with five zeros on your paycheck. To become one of the top-earning data scientists, you might need to go back to school for a PhD or switch industries. On the other hand, don’t forget the role experience will play in your earning potential. It may be that the only thing you need to do to boost your salary is to work hard and wait.

Education for Data Science

If you decide to pursue a career in data science, the first step is to study programming, statistics, and linear algebra. You must have strength in these areas to start and excel in data science studies. You don’t have to have a background in computer science. In our graduate program, we have students from more than 30 different backgrounds, such as chemistry, biology, physics, mathematics, statistics, economy, public policy, music, history, literature and all different branches of engineering.

Once you have competency in these areas, you should enroll in our data science program. There are two options: a Graduate Certificate in Data Science and a Master of Professional Studies in Data Science. Employers are likely to prefer job applicants with a masters degree.

Once you finish your first two semesters (in other words, after completing DATA 601-604), you can consider launching your career with an internship in the field. If internships are hard to find or secure, you can volunteer instead to get the job experience. The idea is just to get started with applied learning, making connections and working your way up the career ladder.

Keep a portfolio of completed projects that you can show to potential employers and clients. This can include practice problems and university assignments. It’s best to keep a GitHub account for this purpose. If you feel your portfolio is a little on a sparse side, ask family and friends if they’d like you to do some projects for them. It can also be helpful to join data science communities online.

You can enter data science competitions as well. You can find such competitions online. Taking part in these competitions will give you the chance to practice your skills, learn from other people and even connect with potential employers.

MARKET DEMAND

  • Click here to see the current market data.
  • The employment market for data scientists is robust, with a growing need for qualified data engineers. According to a recent jobs report by Glassdoor list of the 50 best jobs in America by number of job openings, salary, and overall job satisfaction, Data Scientist is ranked #1 with more than 4,000 job openings, Data Engineer is ranked #3 with more than 2,500 job openings, and Analytics Manager is ranked #5 with almost 2,000 job openings.
  • Across a recent five-year period, the number of data engineers has grown to half the number of data scientists. The number of data scientists more than doubled over those five years, and the number of data engineers sextupled.
  • The Bureau of Labor Statistics provides listings of high-growth job titles. Training in data science is relevant to many job titles, including statistician, computer systems analyst, software developer, database administrator, and computer network analyst, data scientist, data analyst, data engineer, and data manager. U.S. Bureau of Labor Statistics reports that the demand for data science skills will drive a 27.9 percent rise in employment in the field through 2026. Not only is there a huge demand, but there is also a noticeable shortage of qualified data scientists.
  • “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.

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