Data Science Careers

Data Science combines technology, algorithm development, and data inference to solve sophisticated analytical issues. You can pursue this career path by obtaining an associate's, bachelor's, master's, and doctorate from an accredited institution.

So, data scientists are not limited to technology industries. They influence the arts, business, and everyday life. Moreover, these scientists rely on data to tackle real-world issues.

Since organizations that embrace data enjoy tremendous advantages over those that ignore data, the demand for data scientists has continued to climb.

This resource deep-dives into data science careers, salary potentials, opportunities, and resources. Read on to find everything you need in your data career journey.

Why Choose Data Science?

Data Science has gained wide acclaim across industries and among leading organizations. And it has been dubbed the most attractive career of the 21st century.

Corporations and leaders turn to data scientists to address problems that only big data can solve. Data scientists help businesses find patterns, create effective client strategies, and support strategic decision-making.

These professions attract curious people who seek to understand the world by collecting and analyzing data and drawing useful insight from their analysis.

A career in data science will allow you to participate in this transition. By choosing data science, you can become an expert at driving change.

As a data scientist, you can give executives the information they require to make better decisions.

Also, you can advance businesses and drive positive change in communities, among other courses.

How to Start your Data Science Journey

A career in data science requires at least a bachelor's degree. However, many roles require more advanced degrees.

To get started as a data scientist, you need a conventional education in programming, research methods, mathematics, statistics, etc., to gain employment and occupy data science roles in organizations.

Below is a list of different educational requirements you'll need to get started on this career path:

Education Requirements

Associate Degree.

An associate program in data science includes general education in science, communication, and mathematics.

At this level, students can take foundational classes in selected topics.

This program qualifies you for entry-level jobs such as Computer Support Specialist or Statistical Assistant.

You can continue on a bachelor's program with your associate degree.

Bachelor's Degree

This four-year degree program focuses on developing analytical and technical skills essential for analyzing massive data.

It provides advanced mathematics, computer science, programming, and software instructions.

With your degree, you can land roles like data analyst, data engineer, operations research analyst, etc.

You can enroll for a master's degree with your bachelor's degree.

Master's Degree

The master's degree in data empowers students to acquire specialized skill sets in data management, software engineering, machine learning, etc.

The master's programs come with projects to help learners have real-world experience.

Regardless of specialty, graduates can help decision-making at high levels in any sector.

Doctorate in Data Science

This degree prepares learners for a career in research and academia.

It comes with rigorous, intensive projects in deep learning, optimization, language processing, etc.

A doctorate is useful for data scientists who want to gain top-expert status.

Graduates can create innovative solutions for healthcare issues, economic challenges, management, and other spaces using complex data science concepts and methodologies.

Employment Prospects

Graduates in this field are qualified for various computing roles in high demand.

With your associate's or bachelor's degree, you can work in an entry position with room for advancement.

Data science is projected to add over 13,500 jobs each year.

This growth rate shows that careers in data science are on the rise.

Data Science Career Forecast

The BLS projects a career growth rate of 36% for data science occupations from 2021 to 2031. This is a result of more technological development.

Most data science roles pay over $100,000, with several openings yearly.

As data science continues to grow, many companies today need experts in this field to help them plan better for the future.

How to Boost your Data Science Career

There are several ways to boost your career after acquiring a degree.

You can take professional certification courses to advance your career, making yourself more attractive to hiring companies.

As technology is not static, so you shouldn't be. Stay up to date with any changes.

Other ways to boost your career include:

• Learning from a mentor to avoid some pitfalls

• Joining a data science club

• Attending conferences and webinars

• Internships

A combination of these can give you the edge as a Data Scientist in a competitive market.

Lucrative Data Science Career Paths

Data science is an extensive field with various career paths and job prospects in different sectors.

Here are some of the leading data science careers.

Data Scientist

Data scientists help businesses understand their goals using data.

They create algorithms, design data modeling processes and predictive models to extract businesses' needs, analyze the data, share insights and drive strategic business decisions.

Data scientists turn to industry knowledge, contextual understanding, and skepticism of existing assumptions -- to provide solutions to business challenges.

The main work of a data scientist is to make meaning of messy and unstructured data  that would not fit into a database, including data from these sources:

• Smart devices

• Social media feeds

• Emails.

And more.

A data scientist's role combines computer science, statistics, and mathematics.

Working as a data scientist can be challenging but satisfying, putting you at the forefront of new technological advancements.

Average Salary

According to BLS, most data scientists in the US earn a median annual salary of $100,910.

Roles and Responsibilities

• Organize data

• Evaluate vast amounts of sophisticated raw and processed information

• Enhance data collection methods to include all pertinent information for creating analytic systems

• Recommend solutions and strategies to tackle company challenges

• Develop prediction systems and machine learning algorithms

Industries Employing Data Scientists

Computer design companies and Management of Companies and Enterprises provide the highest level of employment for data scientists.

Top paying and industries with the highest concentration of employment in Data Scientists include:

• Management, Scientific, and Technical Consulting Services

• Scientific Research and Development Services

• Credit Intermediation and Related Activities

• Other Information Services

• Software Publishers

• Data Processing, Hosting, and Related Services

• Computer and Peripheral Equipment Manufacturing

• Semiconductor and Other Electronic Component Manufacturing

• Data Processing, Hosting, and Related Services

• Accounting, Tax Preparation, Bookkeeping, and Payroll Services

Quantitative Analyst

A quantitative analyst is an expert who uses quantitative methods to help companies make business and financial decisions about risk management, investments, and pricing.

They create and edit analytical models, work closely with financial staff, and research the best tools to create and maintain financial and stock portfolios.

Quantitative analysts have strong organizational and mathematical skills.

As a quantitative analyst, you can gain experience by helping companies build impressive stock and trade portfolios.

Average Salary

Quantitative analysts earn a median annual salary of $86,023.

Roles and Responsibilities

• Research and analyze market directions and statistics to make modeling decisions

• Create and implement complex quantitative models

• Perform daily statistical analyses (e.g., risk analytics, loan pricing, and default risk modeling) and coding tasks (e.g., pattern recognition or machine learning)

• Detail model specifications and procedures of data gathering

• Test new models, products, and analytics programs

• Retain and improve financial models while in use

• Apply or develop independent tools to ascertain results

Industries Employing Quantitative Analysts

Industries employing quantitative analysts include:

• Insurance carriers

• Banking and lending

• Investment and assets management

• Management consulting firms

• Accountancy firms

• Financial software companies

Applications Architect

Application architects provide mastery, direction, and aid in application architectural consulting to technical analysts, application engineers, and other systems architects.

They are tasked with building quality applications by creating well-designed architectures, writing clean code, and implementing ongoing integration and unit test code coverage.

Strong verbal and written communication skills are important in this position to work well with team members of various degrees of technical depth and knowledge.

A bachelor's degree in computer science or a similar field is commonly required for this position.

Average Salary

Application architects earn a median annual salary of $118,537. 

Roles and Responsibilities

• Designing major aspects of the architecture of an application

• Ensure projects are properly scoped and resourced.

• Providing technical leadership to the application development team

• Performing design and code reviews

• Guarantee that uniform enterprise-wide application design standards are bolstered

• Teaming with others to ensure the architecture is aligned with business requirements

Industries Employing Applications Architect

Industries and organizations that employ applications architect include:

• Computer manufacturing companies

• Software firms

• Computer systems design and related services

Machine Learning Engineer

A machine learning engineer is a person who concentrates on studying, building, and designing self-running artificial intelligence (AI) systems to automate predictive models.

Machine learning engineers design and develop AI algorithms for learning and making predictions that define machine learning.

They bridge data scientists focusing on statistical and model-building work and creating machine learning and AI systems.

The machine learning engineers assess, analyze and organize large amounts of data while executing tests and optimizing machine learning models and algorithms.

They also work as part of a larger data science team.

Average Salary

Machine learning engineers earn a median annual salary of $112,792.

Roles and Responsibilities

• Verify the quality of data

• Perform statistical analysis

• Extend machine learning libraries

• Study and implement machine learning algorithms and tools.

• Selecting appropriate data

Industries Employing Machine Learning Engineers

• Internet and web services

• Computer hardware developers

• Banking and lending

• Installations and energy management

Data Architect

Data architects design, structure, and maintain data, usually organized in a relational database.

They ensure the precision and accessibility of data relevant to an organization.

A data architect must possess outstanding analytical skills and be detail-oriented.

Also, must be able to communicate effectively, as they are part of a larger team within an information technology department.

Average Salary

Data architects earn a median annual salary of $123,753.

Roles and Responsibilities

• Design, structure, and maintain data

• Ensures the preciseness and accessibility of data

• Plan future databases

• Design application programming interfaces (APIs) to retrieve data

Industries Employing Data Architects

• Computer design and related services

• Technical consulting services

• Data processing

• Software publishers

• Insurance carriers

Data Engineer

Data engineers work in an indoor office setting, and a college degree in computer science, engineering, or a related field is always a minimum requirement for this position.

They are responsible for creating and translating computer algorithms into prototype code and organizing and identifying trends in large data sets.

Expected skills and experience also include proficiency in SQL database design, mastery in creating process documentation, strong written and oral communication skills, and the ability to work unaided and on teams.

Average Salary

Data engineers earn a median annual wage of $93,637.

Roles and Responsibilities

• Comprehend data needs and advise the company on technological resources.

• Derive technical solutions to improve access to data and data usage.

• Assemble reports, dashboards, and tools for business users.

Industries Employing Data Engineers

• Software publishers

• Internet and web services

• Telecommunications

• Automotive

Statistician

A statistician is a vast term for a professional in either theoretical or applied statistics.

They analyze various data to enhance the knowledge of professionals or businesses in the private or public sector. Their work depends on their ability to develop reliable data using appropriate, up-to-date methods.

Many statisticians have a master's or doctoral degree in statistics; they combine their degrees with other specialized areas of mastery to apply their analysis to that field.

Average Salary

The median annual wage of a statistician is $78,971

Roles and Responsibilities

• Obtaining, analyzing, and interpreting data.

• Recognizing trends and relationships in data.

• Designing processes for data collection.

• Communicating findings to stakeholders.

• Helping in decision making.

Industries Employing Statistician

• Technical consulting services

• Computer design and related services

• Scientific Research and Development Services

• Colleges, Universities, and Professional Schools

Business Intelligence (BI) Developer

Business intelligence developers must be data professionals, as they work mainly with databases and different types of software.

This role is commonly found in office environments in large corporations and IT companies. Most BI developer roles require a degree in computer science or programming and significant work experience.

As the major focus of this position is to solve problems, BI developers must always be mining data and illustrating it in an easy-to-understand format.

Average Salary

Business intelligence developers earn a median annual salary of $83,091

Roles and Responsibilities

• Develop and maintain BI tools and interfaces.

• Simplifies technical language and intricate information

• Provide quantifiable solutions to a complex problem

Industries Employing Business Intelligence (BI) Developer

• Information technology

• Computer systems design and related services.

• Finance and insurance.

• Management of companies and enterprises.

• Manufacturing.

IT Enterprise Architect

Enterprise architects bolster their company's information technology (IT) systems and services.

Duties performed by enterprise architects include:

• Maintaining IT services and projects.

• Keeping the business up to date with new technology.

• Providing feedback to the company on IT networks.

Enterprise architects interact with external customers and developmental teams within the business to work on projects and help keep all IT assets running appropriately.

A bachelor's degree in computer science, information technology, or a related field is needed for this role.

Average Salary

IT enterprise architects earn a median annual salary of $137,563

Roles and Responsibilities

• Responsible for the upkeep and supervision of an organization's IT systems and services.

• Upgrading enterprise services, software, and hardware.

• Determine the essentials of a range of business units and procedures

Industries Employing Enterprise Architects

• Information technology

• Software publishers

• Finance and insurance

Data Analyst

A data analyst collects, cleans, and interprets data sets to answer queries or solve issues.

They work in many industries, including business, finance, criminal, science, healthcare, and government.

The role includes much time spent with data but also entails communicating results.

Data analysts must have a combination of technical skills and leadership skills.

Average Salary

Data analysts earn a median annual salary of $63,442

Roles and Responsibilities

• collect data

• Clean data

• Developing and designing the structures of a database

• Finding patterns or trends in data

Industries Employing Data Analysts

• Government

• Healthcare

• Finance and insurance

• Law

• Information Technology

Pros and Cons of Data Science Careers

With a degree in data science, you can land high job demands and earn lucrative salaries.

As there is nothing without cons and pros in life, so is it with data science.

Here are a few pros and cons of a data science career

Pros

• Abundance of Positions

• A Highly Paid Career

• Data Science is Versatile

• It's in Demand

Cons

• Mastering Data Science is near impossible - it's impossible to master each field in data science.

• A large amount of domain knowledge is required.

• Working over time and under pressure

• Ethical concerns may arise.

Skills You'll Develop While Studying Data Science

Like all career paths, more advanced roles demand higher skills and qualifications.

Here are certain skills you will need to be proficient in, regardless of your role as a data scientist.

Business Intelligence

One of the best ways to channel technical skills productively is to have business intelligence.

Without it, no one aspiring for a career in data science will be able to discern the issues and challenges needed to be solved to enhance the growth of an organization.

To do this, you must understand how the issues and challenges you solve can impact the business.

Therefore, you need to know how businesses operate to channel your efforts in the right direction.

This is important for assisting the organization you work for explore new and exciting opportunities.

Statistics

Statistics is a fundamental element of data science. Therefore, you need a solid understanding of probability and statistics in your data science journey.

This will help you recognize what types of analysis are suitable and evaluate your results to be sure they're valid and meaningful.

In summary, statistics understanding is the discrepancy between knowing your conclusion is valid and hoping it is.

In exploring data science roles, you will use statistics to gather, review, analyze, and draw conclusions from data and apply quantified mathematical models to appropriate variables.

Programming

There is no way around it. To work in data science, you need to learn to code.

Specifically, you need to learn to code in either R or Python, the most frequently used programming languages in data science.

The good news is that you don't need to learn both programming languages; either one is fine.

When you pick a language, you must learn the key libraries used for data science jobs.

These libraries are tools that complement base programming language, and they make life easier.

Communication Skills

Companies looking for anyone with a data science degree are out for someone who can and fluently translate their technical data to a non-technical team.

A data science student must help businesses make decisions by equipping them with quantified insights and also understand the needs of their non-technical colleagues, such as the marketing or sales team, to wrangle data appropriately.

When communicating, consider the results and values embedded in the data you analyzed.

Most business owners are not after what you analyzed. Instead, they are interested in how it can impact their business positively.

Data Visualisation

The business environment produces tons of data every day. This data is to be translated into an easy-to-understand format. For example, most people naturally understand pictures in graphs and charts more than unstructured data.

As a data science student, you must be able to visualize data using data visualization tools like Tableau, Matplotlib, etc.

These tools will aid you in converting complex results to a format that anyone will easily understand.

Data visualization helps organizations work with data directly. As a result, they can easily grasp insights that will help them act on new business opportunities and stay ahead of the competition.

How to Prepare for a Data Science Career

1. Get an accredited degree or certificate.

Before choosing any data science program, ensure the college or institution is accredited by data science professional bodies and the government of that country where the institution resides.

Accredited programs follow the required standard of course development and undergo strict external assessments, which help them offer students the level of training needed.

One of the benefits of obtaining a degree or certificate in data science with professional accreditation is the numerous job offers you get after graduating.

Most employers prefer to hire candidates from DASCA-accredited institutions.

1. Gain interpersonal skills during your studies

Interpersonal skills such as communication skills - effective listening and speaking, and teamwork are necessary for any successful career.

They also include the ability to monitor, control and manage your emotions.

With this said, ensure your data science program offers you the opportunity to develop interpersonal skills that would enhance your data science career.

To improve your interpersonal skills while you study or work, practice the following.

• Establish your goals

• Identify and observe successful interactions

• Practice

• Ask for feedback

• Reflect

• Stay positive

• Choose your words carefully

• Be in charge of your emotions

• Know your values

1. Apply for internships during or after your studies

Internships are one of the most popular methods of learning on the job and gaining crucial skills.

It is officially commissioned by the company and features some documentation or certificates that justify an intern's time at the company.

Internships help you gain hands-on experience and are one of the best methods of adapting to job roles that you like.

Through your internship, employers can find out your skills and how well you fit inside a Data Science process or channel.

This serves as proof of your accomplishments and your foundational abilities.

1. Don't be quick to niche down.

The data science field is vast, with different areas of specification. So it is wrong to niche down so quickly.

Before getting any degree, you must know the area you want to specialize in after your studies.

However, you need a solid foundation in data science before taking specialized programs to narrow down.

Doing this helps you avoid missing the important elements needed for a successful career in data science.

Start with an introduction to the core courses as this helps you develop deep knowledge in different areas before specialization.

Data Science Resources to Help Your Career

There are several educational resources on data science you can see online. With these resources, you can advance your career.

Most of these resources are free, while you must pay for others before accessing them.

Resources from professional organizations:

• Association of data scientists

• International Institute of Business Analysis

• Computing Technology Industry Association

Publications:

• CHANCE

• Data Mining and Knowledge Discovery

• Data Science Journal

• International Journal of Business Intelligence Research

• Analytics Magazine

OpenCourseware:

• What Is Data Science? - IBM

• The Data Scientist's Toolbox - Johns Hopkins University

• Machine Learning - Stanford University

The following are other resources you can also explore

• Introduction to data science 

• Kaggle.com

• Statistics: An introduction using R

• Online Statistics Education

How to Switch to a Data Science Career

It is not wrong to change careers at any point in life, especially with the recent technological advancement and high job prospects in tech.

Switching to a data science career is not hard to crack, especially if you have a degree or experience in a related field.

You can join a Bootcamp, take an online certifications course or pursue a bachelor's degree to gain skills for entry-level roles.

Also, you can take advanced and professional courses in data science to compete for high-paying roles.

Each route has pros and cons. You just have to decide what works or suits you.

Frequently Asked Questions

What is Data Science?

Data science deals with large volumes of data using modern tools and techniques to derive meaningful information and make business decisions. Data science uses intricate machine learning algorithms to build predictive models.

Data science is a multidisciplinary field that deals with large volumes of data using scientific processes to find unseen patterns and extract knowledge and insights from unstructured data.

Using modern tools, one can get meaningful information to make the right business decisions from data.

Data science is connected to machine learning, mining, and big data.

What certification do I need for a Data Science career?

There are various types of data science certifications you can get.

Here are some of them:

• IBM Data Science Professional Certificate

• Google Professional Data Engineer Certification

• Cloudera Certified Professional (CCP) Data Engineer

• SAS Certified AI & Machine Learning Professional

• Microsoft Certified: Azure Data Scientist Associate

• Professional Certificate in Data Science (Harvard)

• Applied Data Science With Python (University of Michigan)

• From Data to Insights With Google Cloud Specialization (Google)

Data science certifications help prove your skills to potential employers and qualify you for higher positions.

Can I get a Data Science degree online?

Yes, it is possible to get a data science degree online. All you need is your internet connection and your attention.

Most institutions offer accredited data science programs online.

An online data science degree develops specialized knowledge and technical skills that prepare students for advanced positions.

Online data science education offers benefits like a flexible schedule, lower cost, the convenience of learning from home, etc.

What fields can Data Science work in?

Anyone with a degree in data science can work anywhere, depending on their education, skill sets, and experience.

Lucrative opportunities exist in:

• Software development and engineering

• Data Engineering

• Healthcare

• Infrastructure architecture

• Data Architecture

• Machine learning engineering

• Information technology

• Data modeling

• Data mining

• Cloud and Distributed computing

• Data Visualization and presentation, etc.

Is there a high demand for Data Science majors?

Yes, there is high demand for data science majors.

With the fast development of modern technologies, data science is currently in high demand, which will only grow.

There is an explanation for such popularity. The amount of data being produced around the world is accumulating every day.

And every business requires data analysis and predictive modeling to be at the top in today's highly competitive market.

No scientific research in any field can be carried out if there is not enough data collected.

This means the more data an organization has, the more reliable data-derived forecast it can make.

Final Thoughts

The data science field is vast, and there's space for everyone.

There are several ways to break into the field of data science. It requires that you prepare yourself to take on challenging yet intriguing roles.

You can start with an associate's or bachelor's degree.

Most importantly, you must prove to your future employers that you are the right candidate by showing your expertise and work experience.

And to achieve that, you can pursue advanced and professional degree programs in your area of interest.

Do you seek to start your journey in data in science, or are you thinking of switching careers?

Whatever your option, this guide will provide you with the necessary information you need to break into the field of data science.