7 Data Science Certificate Programs to Accelerate Your Career

In our increasingly digital world, data science skills are highly sought after by employers, as our need for scientific methods, systems, and computing continues to expand with new technology,

Data scientist certifications function to validate data skills and experience, which ultimately accelerates career advancement in industries searching for data-driven insights.

Whether you’re a long time professional seeking to advance in the field of data science, or a recent graduate wanting to strengthen your resume, this article is a guide to understanding data science careers and certifications, as well as offering an in-depth comparison of popular certifications for data scientists.

Get Certified in Data Science: 3 Major Benefits

Achieving certification in data science offer a range of benefits that can accelerate your career in the following ways.

Enhanced Career Prospects: A certification in data science can open up opportunities across diverse industries, as companies progressively prioritize the hiring of individuals with certified skills in data science. Certification also demonstrates to employers that you can handle complex data problems or computing systems, a skill set that enhances your prospects for advancing within your organization.

Increased Earning Potential: Professionals with certification in data science may enjoy increased earning potential, as employers increasingly reward this vital skill set. According to Gallop, certification increases salaries by an average of 8.6%.

Staying Up-to-Date with Industry Trends: The data industry is constantly evolving and trends related to tools, methods and technologies move quickly. Certification in data science ensures that you’re up to date with current industry trends and helps you stay competitive in a dynamic and fast moving industry.

7 Top Data Science Certificate Programs

Certified Analytics Professional (CAP):

• CAP is a certification program that validates a data scientist's technical skills. CAP covers the 7 domains of data analysis, such as predictive, diagnostic, and descriptive analytics. This program is not for beginners and requires an application to get started, as well as a combination of experience and education. The exam starts at USD 440 for members, and $640 for non-members.

Cloudera Data Platform Generalist Certification

• The CDP Generalist Certification is an exam used to test proficiency in and knowledge of the Cloudera CDP platform. It covers the fundamentals and security features of CDP architecture, as well as different data analytics experiences and functions. The exam costs USD 330 and can be found here.

Data Science Council of America (DASCA) Certifications

DASCA offers two different certifications:

• DASCA senior data scientist: designed for data science professionals with at least 4 years of experience, it certifies advanced technical skills and knowledge across different domains of data analytics. For USD 775, this certification program covers topics like big data analytics, machine learning, and predictive modeling.
• DASCA principal data scientist: This certification program is designed for data science professionals with at least 10 years of experience in big data. This certification is offered through 4 different tracks at different costs, starting from USD 850.

IBM Data Science Professional Certificate

• This course by IBM covers data science methodology and popular tools, with a focus on visualizing and analyzing data using Python programming language. It's suitable for those with no prior knowledge of data systems and takes approximately 5-6 months to complete. It's available with a subscription to Coursera, so total cost depends on the length of time it takes to complete.

Microsoft Certified: Azure AI Fundamentals

• This computer science course is perfect for data professionals and students who wish to validate their skills and knowledge in AI. The certification exam can be taken alone, or following a one day course covering fundamentals like natural language processing, generative AI, and computer vision. The course is good for beginners and is also foundational for the Azure data scientist associate certification. The certification exam costs USD 99 and can be found here, while the associated Microsoft Azure AI Fundamentals course can be accessed for free here.

Open Certified Data Scientist (Open CDS)

Open-certified data scientist offers 3 different certification levels:

• Level 1: Certified data scientist
• Level 2: Master certified data scientist
• Level 3: Distinguished certified data scientist

These certifications test proficiency in data analytics by demonstrating practical skills in interview and through data science projects. Anyone wishing to become a certified data scientist through Open should have experience with a range of data types and proficiency in at least one programming language. Certification length and cost information is not available on Open's website.

SAS Certified Professional: AI and Machine Learning

• This certification showcases your proficiency in using open-source AI tools for data insights. In order to be a SAS certified data scientist, you must pass several exams in machine learning, forecasting and optimization, and natural language processing. Each exam costs 180 USD and is available here.

Tips on Choosing the Right Data Science Certificate Program

Given the wide range of available data science certifications, choosing the right course or certification can be challenging, and options should be weighed carefully. Consider the following factors when researching online courses.

Course Reputation

Course reputation is essential for data science professionals seeking certification. Providers of data science certifications should be fully accredited, to ensure they meet a high standard and are recognized within the industry. Given the wealth of online courses in the data domain, reputation should play a significant role in this decision, to avoid wasting time and money on fraudulent organizations or unrelated coursework.

To evaluate the credibility of a data science course, confirm their accreditation status online and look at reviews from students. Evaluate their curriculum by comparing it to similar companies, and look for faculty with industry credentials. Another way to confirm reputation of an online course is by examining their industry connections and partnerships.

Course Content and Specialization

When seeking data science certifications, it’s important to consider how the curriculum and certification applies to your professional goals. Will this certification help you get a job in the industry, a promotion, or a new role within your organization? Examine the the course content to verify that it's relevant to your current goals. Don’t forget to look for certificates in a field that you’re actively interested in - your career longevity will be impacted if you choose certification in a topic or industry in which you have little interest or passion.

Future Job Needs

Since job trends in data are changing quickly, it's important to choose a data science course that has long term viability, and reflects where trends are currently heading. Is demand in this topic projected to grow or diminish? Always consider future growth when choosing a data science program.

Cost and Financial Aid

Some data science professional certificate programs can be costly, so picking the right one for you might involve balancing your budget concerns with the need to find reputable programs with long term potential. Some programs offer financial aid, and you can connect with their teams further to explore the possibility of payment plans.

8 Rewarding Data Science Career Paths

Data science has a range of applications and possible careers across diverse industries. We'll look at some of the most popular and in demand roles for professionals with data science background.

Business Analyst

A business analyst is responsible for analyzing a business' systems and processes, seeking ways to problem-solve and maximize success. They may consider things like integration with technology in order to identify a business' needs and come up with creative solutions.

Data is a major component of business analysis, and a business analyst will need to collect and organize data on everything from market trends to consumer demographics. They use predictive modeling and machine learning techniques to predict future trends, market changes, and consumer needs, making data science skills essential for this role.

Database Administrator

Data scientists in a database administrator role are responsible for the security and performance of a database, which they achieve by implementing and maintaining database systems to ensure data is accurately stored and optimally retrieved. They often troubleshoot potential problems and they must develop a clear disaster recovery plan with developers.

Database administrators use data to analyze performance metrics and perform quality assessments of their database. They must use and analyze historical data on performance, as well as predictive modeling to project future data storage needs.

Data Engineer

Data engineering involves managing and designing data infrastructure to support data analysis, storage, and processing. At large organizations, data engineers build data pipelines or manage data warehouses, while at small companies, data engineers will perform a wide range of data-related tasks like developing machine learning algorithms or using data analysis tools.

Since data science is pivotal to this role, a data science certificate is a good way to demonstrate data engineering skills to a potential employer. Data engineers transform data from multiple sources, develop data pipelines and algorithms, and manage data warehouses.

Database Architect

Another role in the database management domain, database architects design the structure of a database, through careful planning to understand their organization's needs. Where database administrators implement and maintain existing databases, architects are data scientists who design the systems that an organization will use, ensuring sufficient data retrieval and storage.

Database architects use data science in a number of ways, starting with machine learning algorithms and statistical techniques to understand data patterns and design appropriate models. Data analysis is also an important component of choosing the right technology, as database architects must evaluate performance metrics and use predictive modeling to forecast growth and make informed decisions.

Machine Learning Engineer

Machine learning engineers are responsible for designing and implementing machine learning algorithms or systems. They build complex machine learning models in order to improve decision making or solve organizational problems.

Data science is crucial for this role, as machine learning engineers use data to develop and train machine learning models. They must first analyze and normalize data to train their models, and use data analysis techniques to evaluate algorithm performance.

Quantitative Analyst

Quantitative analysts are data scientists who apply statistical analysis to financial markets. They develop and deploy models and algorithms to analyze data in finance related to risk and investment strategy.

Quantitative analysts employ data science technology in a range of ways. Their quantitative methods include conducting research in finance, using statistical analysis and predictive analytics to assess risk and forecast market trends. They use machine learning for sentiment analysis and pattern recognition in order to make data-based financial decisions.

Data Mining Specialist

A data mining specialist is a data analyst who focuses on understanding insights and patterns present in large data sets. The information they get from data analysis can uncover trends and patterns, optimizing decision making and market strategy.

Data mining specialists use data science to collect and integrate data from a range of sources, like databases and warehouses. Their knowledge helps them visualize data distributions and prepare raw data for statistical analysis.

Data Visualization Engineer

A data visualization engineer is a type of data scientist who's responsible for representing data in a visually appealing and accessible way. They design informative data visualizations like reports and dashboards so their organization and its stakeholders can understand complex data, which can inform business decisions.

Data visualization engineers require in depth data science knowledge to understand data insights. They should be familiar with essential analytics tools in order to transform data into visualizations that are both appropriate for their audience, and appealing to look at.

How To Find Data Science Jobs

For professional data scientists who have obtained certification, the next step is finding a position that balances your needs and experience. The good news is, data scientists enjoy some job security, with demand projected to increase over the next decade. Here are are few resources for finding one of the ample data science jobs available.

Job Boards

When it comes to jobs for data scientists, popular job boards like LinkedIn and Indeed will provide results, given the widespread use and popularity of these platforms. CakeResume is another job board with hundreds of active listings across multiple countries, and where data scientists are in regular demand.

For those looking for dedicated data science job boards, the following are popular among data scientists:

• Datajobs.com: One of the most well-known job boards in the data science community.
• Ai-jobs.net: Great for AI or machine learning jobs.
• Open Data Science Job Portal: A data scientist job board with a global following.
• Data Umbrella: Dedicated to underrepresented data scientists

Using job boards can be challenging, especially with so many users competing for the same postings. Try these tips to maximize your data science job board experience:

• Create a profile: Make sure your job board profile is finished and accurate, as well as tailored to the roles you'd like to apply to.
• Use advanced search: Narrow down your results to include components like location and salary range.
• Perfect your resume: Your resume should be tailored to the roles you want by highlighting relevant skills and any data science certifications.
• Set up alerts: Be the first to hear about a new job posting by setting up email alerts based on search criteria.
• Apply thoughtfully: Applying to jobs online can be tedious, with each application taking time and effort. Focus your applications on jobs that most closely match your experience and needs.

Online Communities

Online communities and forums are a great way to learn from other data scientists, network, and keep current on trends in data science. Check out some of these popular data science forums and online communities:

• Reddit: Reddit's forum r/datascience is an enormous, active community for data scientists.
• Tableau: Tableau's platform is a community meant to share data visualizations and connect with other data professionals.
• Kaggle: You can connect with over 1.5 million data scientists on Kaggle.
• IBM data community: In addition to webinars and courses, IBM offers forums and discussions for data professionals.

Engaging with online communities is good advice for any data scientist, but it can be challenging to do right. Follow these tips for engaging with other data professionals online:

• Understand the rules: Make sure you read the community's rules and etiquette guidelines before posting or commenting.
• Listen well: Take your time to learn from others online.
• Be thoughtful: Contribute to conversations with relevant content or by asking questions. Forums are a great way to showcase your technical skills while still being supportive and constructive.
• Stay connected: Follow up public conversations with professional direct messages or email.

Networking

Finding the right job increasingly relies on networking for the following reasons:

• It generates job leads: Networking helps you to be more visible in the industry, and can unlock hidden jobs that haven't yet been publicly advertised.
• It helps develop skills: Networking is a great way to continue informal learning through exposure to different perspectives and experience.
• It keeps you informed: Connecting with other data scientists helps you stay current on industry trends and insights, and it's a great way to get professional advice.

If you're struggle with networking, try these tips to network more effectively:

• Attend conferences and industry events: Participate in networking events as actively as possible in order to meet new people.
• Be prepared: Prepare an elevator speech, or brief introduction, in advance, and research event attendees and organizations.
• Be organized: Keep track of contacts, events, and interactions using your preferred organizational tools.
• Follow-up: If you've successfully connected with a fellow data scientist, make sure you follow up with a professional email or message.

📚 Further reading: 10 Best Professional Networking Sites To Expand Your Professional Network

Conclusion

A diverse range of industries are looking to hire data professionals, especially those with a data science professional certificate. Whether you're a beginner or a senior data scientist, certification is a great way to validate your skills to employers and increase your professional growth potential. Choosing the right certification program involves balancing budget concerns, reputation, and content, but once certified, data professionals can seek a wide range of positions. Wherever you are in your career journey, it's important to continue learning and stay competitive in the changing data market.

Check out Cake's article library as a source for more career advice and resume tips for data professionals. If you need a free online resume builder, sign up for Cake and create your resume from our free resume template today!

— Originally written by Tiffany Quinn —