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9 Data Science Certificates Worth Investing in for 2024

21 minute read | December 25, 2023
Maria Muntean

Written by:
Maria Muntean

Ready to launch your career?

Data science is an increasingly attractive field, with a 28% rise in demand for data scientists over the last five years. A testament to the field’s importance is the rise of data science certifications, which serve as recognized proof of a data scientist’s skills and knowledge. Data scientists are in high demand and a senior data scientist can expect to earn well over $200,000 per year, according to Glassdoor. As a distinguished certified data scientist, even more doors will open for you, especially if your data science skills experience is backed by one of the best data science certifications on the market.

Many of the top data science certifications are very accessible, whether you are at the start of your data science journey, or are backed by a Bachelor’s Degree or Master’s Degree in Computer Science or Applied Data Science. You can choose to specialize by becoming a Open Certified Data Scientist (CDS), Senior Data Scientist (SDS), Microsoft Certified Azure Data Scientist, American DASCA Senior Data Scientist, Certified Analytics Professional (CAP) or more. These data science certifications prove that you are adept at managing deep learning and machine learning workloads, that you can gain insights through data exploration, understand data mining across multiple data points and data pipelines , know SAS programming basics, the R programming language, and have the technical skills to tackle real world business problems.

However, as the number of available data science certifications grows, so does the importance of selecting a data science certification that enhances your current skill set and provides long-term value for your career.

This article will help you make a data-driven decision, outlining the best data science certifications worth investing in for 2023. Each data science certificate program offers a unique blend of knowledge and practical experience to improve your competency and marketability in the data science job market. Let’s get you one step closer to your career as a data scientist!

What Does a Data Scientist Do?

A data scientist is a data analysis professional who uses data to solve problems and make predictions. They use their applied data science skills, including statistics, data visualization, mathematics, programming, data manipulation, predictive modeling, natural language processing and machine learning, to extract insights from data that would otherwise be hidden. Most data scientists hold a bachelor’s degree or master’s degree in Computer Science, but it is possible to enter the field without completing a formal education, and a bachelor’s or master’s degree isn’t strictly required if you have experience. Many potential employers will take this into account when they hire data professionals.

A certified data scientist can perform a number of daily tasks:

  • A data scientist gathers data: This involves collecting data from a variety of sources, such as surveys, customer transactions, and social media, using a proven data science methodology.
  • A data scientist has to clean and prepare data: This involves removing errors and inconsistencies from the data. This is an important step, as it can affect the accuracy of the data analysis.
  • A data scientist has to analyze data: Data analytics involves using statistical techniques to identify patterns and relationships in the data. Exploratory data analysis can be done using a variety of methods, such as regression analysis, clustering, and machine learning. Data analytics is one of the most important data science skills you’ll need to learn before working in the field.
  • A data analyst has to present data. Data visualization is an important task that all data science professionals have to familiarize themselves with. They may also use predictive modeling to create forecasts.
  • Data scientists build models: This involves using the insights from the statistical analysis to build models that can be used to make predictions. They may use the R programming language.
  • A data scientist has to present and communicate results: This involves communicating the results of the data analysis to stakeholders in a clear and concise way. As a senior data scientist, you have to be prepared to present your data management processing analysis to important stakeholders.
  • A data scientist will deploy models: This involves deploying the data analytics models into production so that they can be used to make predictions on a regular basis (predictive modeling). You may work on a number of important data science projects as a data engineer.

Data scientists are in high demand in a variety of industries, including business, finance, healthcare, and government, or any field where big data is generated. The job outlook for data scientists is very positive, with the Bureau of Labor Statistics projecting that employment of data scientists will grow 22% from 2020 to 2030, much faster than the average for all occupations or the field of data analytics. You can learn the necessary data science skills and become a certified data scientist by completing a number of data science courses, online or in-person. Choose your data science specialization carefully – the possibilities are endless for data science professionals, and there are a number of data science certificate programs to choose from.

Are Data Science Certifications Worth It?

Whether or not a data science certificate is worth it depends on your career goals and current knowledge. The best data science certifications often act as catalysts for most people, propelling novices to a competent professional level. AI and the use of natural language processing is fairly new but in high demand; experience will trump even the best data science certifications. It can be beneficial to gain experience, validate existing knowledge, and stand out in the job market. However, even the best data science certification is not a magic bullet. Practical experience in the data science field and continual learning are also essential, so look for certificates encouraging that. Some companies may also insist on a formal education, like a bachelor’s degree or a master’s degree.

You have to consider the area you’d like to specialize in, e.g. becoming a Certified Analytics Professional (CAP) or Senior Data Scientist (SDS). You can pick up a few basic skills and gain a basic understanding through self-study, but the learning process will be much quicker and easier if you complete a course or bootcamp. You can attend free or affordable bootcamps online. Sites like Coursera offer courses by prestigious colleges like Harvard University – at a fraction of the price it would cost to attend Harvard University, of course.

Data Science Certificate vs. Data Science Certification vs. Data Science Course

Understanding the differences between a certificate, certification, and course is paramount, especially since there are so many data science certificates and programs to choose from. Do you want to become a Master Certified Data Scientist? A Certified Analytics Professional? Do you need a bachelor’s degree or master’s degree before attempting the certification? And which are the best data science certifications to choose if you want to work in statistical analysis or data engineering?

A course refers to a series of lessons or lectures on a particular subject, often forming part of a more extensive program of study.

A certificate, on the other hand, is awarded after completing such a course, indicating that a learner has acquired specific knowledge or skills.

Unlike a certificate, a certification or data science program typically involves passing a rigorous examination process. It’s an official document proving an individual’s competence in a specific data science area, enhancing credibility and job prospects.

Of course, many data science professionals choose not to pursue a formal program at all and prefer to gain practical experience instead.

Data Science Certifications Worth Pursuing

This section will explore the data science certifications that are worth your time and resources, and outline their unique benefits.

IBM Data Science Professional Certificate

The IBM Data Science Professional Certificate is an ideal program if you are just starting in the data science field and you’re looking to kickstart your career. This certification demonstrates a candidate’s ability to solve real-world problems using data science methodologies and statistics.

It requires learners to master essential tools such as Jupyter Notebooks, SQL, Python, predictive modeling, natural language processing, and data visualization and analysis libraries. The program also involves constructing and evaluating machine learning models using Scikit-learn and SciPy, applying these techniques to real location datasets.

The certificate is earned after completing all courses within the IBM Data Science Professional Certificate program on Coursera.




The IBM Data Science Professional Certificate is a beginner-level program that doesn’t require any prior experience. The program comprises ten courses that can be completed in approximately five months, assuming a commitment of 10 hours per week.

To provide participants with practical expertise, this certificate course focuses on developing skills with the tools, programming languages, and libraries commonly used by professional data scientists. Key topics include data analysis, data visualization, machine learning, and big data.

Much of the program emphasizes applied learning, with hands-on labs conducted in the IBM Cloud using real-world data sets. This practical experience equips learners with job-ready skills and helps them build a portfolio of data science projects, demonstrating their proficiency to potential employers.

It’s important to note that this program suits anyone interested in data science or machine learning, regardless of their academic background. The only prerequisite is a passion for learning and a readiness to develop new skills.

Microsoft Certified: Azure Data Scientist Associate

The Microsoft Certified: Azure Data Scientist Associate certification is geared towards data scientists and professionals who work with data science and machine learning projects on the Azure platform.

The certificate validates your knowledge and experience using Azure Machine Learning and MLflow for data science tasks.


The Azure Data Scientist Associate certification exam costs around $165 USD. However, the exact price is based on the country or region in which the exam is proctored. Becoming Microsoft Certified can be a real career booster.


The Azure Data Scientist Associate certification requires you to pass a final test, Exam DP-100: Designing and Implementing a Data Science Solution on Azure. This exam measures your ability to design and prepare a machine-learning solution and train and deploy models.

Microsoft provides several learning paths to help you prepare for the exam. These include exploring the Azure Machine Learning workspace, working with data in Azure Machine Learning, or automating machine learning model selection with Azure Machine Learning. Each of these learning paths contains modules of varying lengths.

Moreover, Microsoft allows for the renewal of your Microsoft Certification for free. This can be achieved by learning the latest updates to the technology for your job role and passing an online assessment on Microsoft Learn.

Google Professional Data Engineer

The Professional Data Engineer exam assesses your abilities in designing data processing systems, operationalizing machine learning models, ensuring solution quality, and building and operationalizing data processing systems.

It’s designed for data scientists who focus on designing and monitoring data processing systems, emphasizing security and compliance.


The Google Professional Data Engineer certification exam registration fee is $200.


The Google Professional Data Engineer certification exam is a 2-hour test with 50-60 multiple-choice questions. The exam can be taken online or onsite.

There are no prerequisites to take this certification exam. However, Google recommends candidates have at least three years of industry experience, including one year of experience with Google Cloud. Google provides several resources to help candidates prepare for the exam, including online training or in-person classes.

SAS Certified Data Scientist

The SAS Certified Data Scientist certification is an advanced certification for individuals who want to evince their ability to use open-source tools and SAS Data Management to manipulate big data. This certification is ideal for those aspiring to gain big data insights and make business recommendations using complex machine learning models. Becoming a SAS certified data scientist can give a real edge in your career.


The cost of the SAS Certified Data Scientist certification exams varies depending on the specific exam.

The Associate Exams are priced at $120 each. If you’re taking the Predictive Modeler exam, the cost is $250. All other exams cost $180 each.


The SAS Certified Data Scientist certification involves the completion of three different credentials, meaning candidates must pass three exams: the Data Curation Professional exam, the Advanced Analytics Professional exam, and the AI & Machine Learning Professional exam.

As a part of the certification journey, candidates are encouraged to take advantage of SAS Academics, which offers in-depth training and certifications. Academic discounts are also available for students and educators.

Databricks Certified Associate Developer for Apache Spark

The Databricks Certified Associate Developer for Apache Spark certification evaluates a candidate’s understanding and application of the Spark DataFrame API for basic data manipulation tasks within a Spark session. This certification is primarily geared towards individuals involved in data engineering or analysis and seeking to improve their skills in Apache Spark, a popular open-source distributed computing system for big data processing and analytics.

Candidates earning this certification are expected to demonstrate competence in using Python or Scala. It requires an exam.


The cost of attempting the Databricks Certified Associate Developer for Apache Spark certification exam is $200. This price is applicable per attempt, meaning that if a candidate has to retake the exam, each attempt costs an additional $200.


Candidates are given 120 minutes to complete the certification exam with 60 multiple-choice questions. The questions are distributed across three high-level topics.

While not mandatory, it’s recommended that candidates complete the Apache Spark Programming with Databricks course, available with either instructor-led or self-paced options through the Databricks Academy.

Amazon Web Services (AWS) Certified Machine Learning

The AWS Certified Machine Learning credential is a certification program from Amazon Web Services intended for individuals in development or data science. It is intended for professionals with over a year of experience developing, architecting, or running machine learning workloads in the AWS Cloud. It requires an exam.


The AWS Certified Machine Learning – Specialty exam (MLS-C01) costs $300.


Candidates are given 180 minutes to complete the exam. The exam consists of 65 multiple-choice or multiple-response questions. The exam is delivered via a Pearson VUE testing center or can be taken as an online proctored exam.

AWS provides various resources to help candidates prepare, including but not limited to exam guides and sample questions. It’s recommended that candidates leverage these resources to build the necessary knowledge and skills to excel in the exam.

Data Science Council of America (DASCA) Senior Data Scientist

The Data Science Council of America (DASCA) offers a Senior Data Scientist (SDS) Certificate recognized globally as a high-quality, vendor-neutral, cross-platform qualification. The SDS is designed for experienced big data analysts and technology professionals who aspire to display their advanced data leadership potential and stay at the forefront of data science.

This certification is best suited for technology professionals with at least four years of exposure to research and analytics who are interested in advancing to data scientist roles within large organizations. It aims to accelerate their journey toward becoming data leaders, data architects, and intelligence experts.


The total cost for the SDS certification program is $775. This is an all-inclusive, one-time fee covering the certification exam, preparation resources, a digital badge, and the shipping of materials. The price also includes the digital access and hosting cost of 3rd-party optional study resources. DASCA also offers special fee considerations for military and war veterans. It requires an exam.


Applicants for the SDS program should have a sound understanding of the latest technology and data science trends and their intersections with other streams of computer science, business, and industry. Depending on the candidate’s educational qualifications, the minimum experience required varies from 3 to 5 years in data sciences, computing, business analytics, or other field.

Candidates are granted a 6-month timeframe to prepare for the SDS exam with a recommended self-study schedule of 8-10 hours per week. The exam is held online and can be taken at any time and location, with digital proctoring for integrity.

Data Science Council of America (DASCA) Principal Data Scientist

Compared to the Senior Data Scientist certification above, the Principal Data Scientist certificate is designed for seasoned professionals in the data science and analytics fields who have significant experience in data analytics and intelligence.

Ideal candidates have demonstrated their ability to transform data and business intelligence infrastructures within organizations and have a track record of managing and architecting enterprise-wide business intelligence systems. These individuals are seen as thought leaders, idea generators, and technology pioneers in the data science community. It requires an exam.


The cost of acquiring the PDS™ certification depends on the applicant’s track. Here are the current pricing options as of 2023:

  • For invite-only applicants (Track 1), the fee is $850.
  • Via Corporate Nomination (Track 2), the fee is $1250.
  • The fee for those upgrading from SDS™ (Track 3) is $750.
  • For Open Applications (Track 4), the fee is $1450.


The PDS™ program provides a thorough structure, offering flexibility in its completion timeline. Candidates are given a full year from the date of registration confirmation to pass the exam and complete the assessment requirements. For preparation, candidates generally should study for 8-10 hours per week.

The program involves a multi-stage process that includes completion of registration formalities, preparation for the exam using the provided learning resources, and completing four assessment milestones.

Holders must update their professional achievements on their myDASCA dashboard once every five years.

Open Certified Data Scientist (Open CDS)

The Open CDS certification is designed to be a vendor-neutral, peer-reviewed certification, and is for individuals who integrate statistical and data analysis techniques to solve various business problems. Its requirements allow it to serve as an objective measure of a data scientist’s capabilities, making it highly valued by world-leading enterprises when assessing the skills of potential and current employees.


The pricing details for Open CDS are not publicly available on the certification’s website.


The Open CDS certification process is unique because it doesn’t involve attending specific training courses or passing written exams. Instead, applicants must demonstrate their skills and experience against a set of requirements. This demonstration is achieved by submitting written applications and undergoing peer interviews.

This experience-based approach emphasizes practical skills and on-the-job experience. It distinguishes the Open CDS from many other certifications, mainly focusing on theoretical knowledge or passing a particular exam. This certification process is developed from the IT sector’s requirements, ensuring it is closely aligned with the skills and knowledge data science professionals need in the real world.

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Where To Start When Considering Certifications

When considering certifications in data science, it’s essential to consider where you’re starting from and your ultimate career goals.

For instance, if you’re just beginning to explore the field or looking to solidify your foundational skills in data science, you might not need to jump into an official certification right away. In this case, enrolling in a comprehensive data science course, such as Springboard’s Data Science Bootcamp, can be a strategic and efficient way to start. Springboard offers students both a certificate upon completion and the foundational skills that are needed to launch a career in data science.

Springboard’s Data Science Bootcamp is designed to equip you with a solid understanding of the core concepts, techniques, and tools used in data science. You’ll learn through hands-on data science projects and get support from industry professionals and a community of peers. This holistic approach ensures that you don’t just learn the theory but also understand how to apply your knowledge in practical situations. Although this is not an official certification per se, it serves as a stepping stone for further specializations and certifications.

For those who aspire to progress into specific roles such as a data analyst, data engineer, Certified Analytics Professional, or even a data science manager, obtaining a data science certification can give you an edge. Many organizations in tech, healthcare, finance, and other industries value such credentials as they validate your proficiency in certain areas. More than that, some jobs might require data science certifications as a prerequisite. In such cases, having a certificate from an organization like the Data Science Council of America can be advantageous.

What To Look For in a Data Science Certification

There are several factors to consider when evaluating the best data science certifications. Let’s explore them below.

Course Reputation

A well-regarded course often has a track record of success, is endorsed by industry professionals, and is recognized by hiring managers. Look for data science courses with positive reviews, strong alumni networks, and a curriculum designed by industry experts.

Course Support

The quality of support provided throughout the course is another essential factor. This can include resources like textbooks, lectures, and access to software and tools. However, support should also encompass access to instructors or tutors, community forums, and mentorship opportunities.

The best courses offer individual mentorship and career coaching, giving you direct access to industry professionals who can guide you, answer your questions, and provide personalized feedback.

Future Job Needs

Before choosing a certification, consider whether it aligns with your career goals and the demands of the job market. Ensure the certification covers the required skills and tools in your desired industry.

Review job descriptions for roles you’re interested in, and see if the certification program covers those areas. Some roles may also require specific certifications.

Earning Power

Finally, consider the earning potential associated with the certification. While it’s not the only factor to consider, a certificate that increases earning power is often a good investment. This doesn’t just refer to higher salaries but also to the potential for career advancement.

Certifications that are recognized and respected in the industry can make you more hirable and open up opportunities for promotions and leadership roles. Research and compare the average salaries of professionals with and without a given certification to understand its potential return on investment.

Understanding What Each Data Scientist Certification Requires

A certification can open doors in the field of data science and will help you demonstrate your skills to employers. There is an increasing demand for certified professionals with this important digital badge of honor pinned to their resume, but before you sign up, study the specific skills required and specific prerequisites. From completing hands-on projects, to gaining real-world experience or completing peer interviews, preparation is the key to success. 

Obtaining certifications in data science can significantly boost your career prospects. Here’s an example of the specific prerequisites and skills required for popular certifications:

America DASCA Senior Data Scientist (SDS):

  • Prerequisites: Minimum 5 years of experience in data science or related field, bachelor’s degree in a relevant field like computer science, statistics, or mathematics.
  • Skills: Advanced knowledge of statistical methods, machine learning algorithms, deep learning, big data technologies, data visualization, cloud platforms, and communication skills.

Certified Data Scientist (Open CDS):

  • Prerequisites: Minimum 3 years of experience in data science or related field, bachelor’s degree in a relevant field, strong portfolio of data science projects.
  • Skills: Proven expertise in data analysis, machine learning, programming languages like Python and R, statistical modeling, data visualization, and strong communication skills.

Cloudera Certified Professional (CCP) Programs:

  • Cloudera Certified Associate (CCA) Data Analyst:
    • Prerequisites: No prior experience required to become Cloudera Certified Associate, basic understanding of data analysis concepts.
    • Skills: Ability to interpret and analyze data, familiarity with data visualization tools, understanding of basic statistics.
  • Cloudera Certified Professional (CCP) program Cloudera-certified Data Engineer:
    • Prerequisites: CCA Data Analyst certification, 1-2 years of experience in data engineering.
    • Skills: Expertise in building and maintaining data pipelines, proficiency in programming languages like Python and Scala, knowledge of Hadoop and Cloud platforms.
  • Cloudera Certified Professional (CCP) Data Scientist:
    • Prerequisites: CCA Data Analyst certification, 2-3 years of experience in data science.
    • Skills: Strong grounding in machine learning algorithms, familiarity with deep learning concepts, proficiency in Python and statistical libraries.

Whether you are trying to land your first role or need to advance to becoming a principal data scientist, knowing what the various tracks require (and what every certification requires) will help prepare and prove your worth as a subject matter expert. Bear in mind that technical know-how and professional experience aren’t the only requirements. You’ll need to achieve a specific overall score. Most exams require a fee as well, and these fees can change according to the location. Having a better understanding of what each certification consists of will help you identify the right course material and the essential skills you’ll need to succeed.

Choosing the Best Data Science Certifications for Your Needs

Data science is a lucrative field booming with opportunities. If you’re looking to break into this exciting domain or level up your existing skills, data science certifications can be a valuable stepping stone. But with so many options available, choosing the right one can feel overwhelming. Fear not, fellow data enthusiast! This guide will help you navigate the maze and find the best data science certifications for your aspirations.

First things first: What are your goals? Are you aiming for an entry-level data analyst role, or do you aspire to become a seasoned data science professional tackling complex predictive modeling and AI projects? Identifying your career path will guide your certification choices.

Here are some top contenders, categorized by level and focus:

1. Beginner-Friendly:

  • Google Data Analytics Professional Certificate: Gain fundamental data analysis skills like cleaning, manipulating, and visualizing data through this six-month online program from Coursera and Google. It’s a great springboard for data analyst roles.
  • IBM Professional Certificate in Data Science: Offered by Coursera, this program delves deeper into Python programming, statistical analysis, and machine learning, preparing you for broader data science roles.

2. Intermediate-Level:

  • Microsoft Certified Azure Data Scientist Associate: Validate your proficiency in using Microsoft Azure cloud platform for data analysis and machine learning tasks. This exam-based certification caters to professionals working with Azure cloud technologies.
  • Open Certified Data Scientist (OCDS): This vendor-neutral certification assesses your general data science knowledge and skills, regardless of specific tools or methodologies. It’s ideal for demonstrating broad data science competence.

3. Advanced:

  • SAS Certified Advanced Analytics Professional: Master advanced data analysis techniques and modeling using SAS software. This certification caters to professionals in fields like research and finance where SAS is widely used.
  • Data Science Council of America (DASCA) Senior Data Scientist (SDS): This rigorous program tests your expertise in handling complex data science projects, including model building, natural language processing, and time series forecasting. It’s targeted towards experienced data science professionals.

Remember: The “best” certification is subjective. Consider factors like:

  • Your learning style: Do you prefer self-paced online courses, instructor-led programs, or a mix of both?
  • Time commitment: Can you dedicate a few hours a week or do you need a flexible, self-paced option?
  • Budget: Certifications can range from free to several thousand dollars.
  • Hands-on experience: Look for programs that offer practical projects and real-world data sets to solidify your learning.

Beyond the big names, explore other exciting options:

  • Specializations: Platforms like Coursera and edX offer data science specializations focusing on specific areas like natural language processing, computer vision, or healthcare analytics.
  • Open-source tools: Certifications in tools like R programming language or TensorFlow can give you an edge in specific data science niches.
  • Cloud platforms: Certifications in cloud platforms like IBM Cloud or Amazon Web Services (AWS) are valuable for data scientists working in cloud environments.

Remember: Certifications are just one piece of the puzzle. Continuously develop your skills through hands-on projects, participating in hackathons, and staying updated on the latest data science trends and tools.

Data science is a dynamic field, so keep learning, keep exploring, and keep pushing the boundaries of what’s possible! With the right approach and the right certifications, you can unlock a rewarding career in this ever-evolving domain.

Bonus tip: Don’t be afraid to try other tracks within data science. Maybe you’ll discover a passion for text mining or artificial intelligence. The possibilities are endless!

FAQs About Data Science Certification

We’ve got the answers to your most frequently asked questions.

Are Data Science Certifications Expensive?

The cost of data science certifications can vary widely depending on the institution offering them, the breadth and depth of the curriculum, and the resources provided.
Some online courses may cost a few hundred dollars, while more comprehensive programs, particularly those affiliated with universities, might cost several thousand dollars.

It’s crucial to consider not just the cost but also the course’s value.

Will a Certification in Data Science Help You Get a Job?

A certification can significantly improve your employability, as it provides evidence of your skills and knowledge. Many hiring managers and recruiters look for certifications on resumes to filter candidates. It shows that you have undergone rigorous training and met the standards set by the certification body. The pool of Big Data is growing, and many companies want to hire data professionals with data wrangling and analytical skills to manage and use all that data. Data science certifications can prove that you have the necessary skills in advanced analytics to do the job.

How Many Data Science Certifications Do You Need?

The number of data science certifications you need will depend on your career goals and the specific requirements of the roles you’re interested in.

If you’re just starting in data science, a single comprehensive program that provides strong foundational knowledge can be sufficient. If you already have a master’s degree, you might not need a certification at all.

As you advance in your career, you may pursue additional certifications in more specialized areas to demonstrate your expertise and commitment to continuous learning.

What Is the Data Science Council of America? (DASCA)

The Data Science Council of America (DASCA) is a worldwide leader in data analytics accreditation and certification. It is an independent, vendor-neutral organization that offers a variety of certifications for data analysts, data engineers, data scientists, and other data science professionals.

Is It Better to Pursue a Vendor Neutral Certification Than a Specific One?

Whether or not you pursue a vendor neutral certification or pursue something more specialized/niche depends on the field you’d like to work in. Data science is a lucrative field – and a diverse one – with high salaries across the board. Try to gain knowledge in as many areas as possible, about as many aspects of the field as possible, before making a final decision. Remember, this field demands continuous learning and a regular update. You need to be prepared to acquire new skills as (artificial intelligence) AI solutions, model building, deep learning, data wrangling and other elements evolve. You may even need to retake the exam regularly if your certification requires it. 

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Maria Muntean

Maria-Cristina is a content marketer with 7 years of experience in SEO and content strategy for SaaS and technology brands. She holds an MA thesis on the effects of emotions in written and video content. She loves to spend time near the ocean and watch horror movies.