When you search for data analytics certification programs on Google, you’re presented with hundreds of search results. How can you tell which one is worth it/suitable for your personal goals?

If you want to become a data analyst, keep reading. In this article, we’ll list and review top data analyst certification programs/courses worth every cent.

Top Data Analyst Certification Programs Right Now

1. Certified Data Product Manager by Product HQ

Level: Beginner to Advanced Level

Duration: Self-Paced

The data product manager course covers everything a professional needs to either start or improve their professional career. The content is a succinct mix of data analyst essentials such as SQL basics and data modeling, while also providing valuable teachings on product metrics and analytics.

If you’re a beginner, this is a fantastic choice since by passing the course you’ll get a strong foundation as a data analyst alongside core management skills. Finally, upon finishing the course, you get a certificate that will make it easier to land jobs. If you have data experience under your belt, then the course helps broaden your knowledge.

Some of the topics you’ll learn in the course are:

• Product Metrics
• Analytical Frameworks
• Business Metrics
• Market Research
• SQL
• Data Modeling
• Data Visualization

2. DataCamp

Level: Beginner to Advanced Level
Duration: Self-Paced

DataCamp is another fantastic data analytics certification program. It has one of the most diverse and comprehensive programs that are tailored according to your career in data. You can find hundreds of courses and certifications on the site; it’s essentially a one-in-all platform for learning all-things data. Regardless of whether you are a Python programmer, R programmer, a data scientist, or are looking to get into data analytics, DataCamp has a course for you.

DataCamp has several data analyst certifications, and each of them is tailored to specific specialties. Each tailored program is designed to help you accelerate your career in your specialization. The following are some of the data analyst certifications you can find on DataCamp:

• Data Analysis in Excel
• Data Analysis in Spreadsheets
• Exploratory Data Analysis in Python
• Exploratory Data Analysis in SQL
• Exploratory Data Analysis in R
• Bayesian Data Analysis in Python

3. Introduction to Data Analytics for Business

Level: Beginner level
Duration: 12 hours (self-paced)

The next on our list is a basic course that’s perfect for beginners/entry-level professionals.

Offered and designed by The University of Colorado Boulder through Coursera, the “Introduction to Data Analytics for Business” e-learning training course dives into the foundational principles of data analytics and how to use them for business decision-making.

The course is designed to keep the latest business analytics best practices. By the end of this course, you’d have learned:

• Data modeling
• Data quality
• Analysis
• SQL
• Business intelligence principles

4. Data Analyst Associate Certification by Microsoft

Level: Intermediate level
Duration: Self-paced (you can give the exam right away without spending any time on learning)

When it comes to data science certifications, Microsoft is at the forefront.

The “Data Analyst Associate Certification” is meant for intermediate-level data analysts who have been in the industry for a while.

Unlike typical certification programs, there are no mandatory learning objectives. In addition, there are no prerequisite programs. If you feel that you’re ready, you can simply schedule to appear for the “Analyzing Data with Microsoft Power BI” exam (Exam DA-100), pass, and earn the certification.

However, Microsoft has shared a clear learning path spanning 6 free courses consisting of a total of 16 modules, including (in order):

• Get Started with Microsoft Data Analytics
• Prepare Data for Analysis
• Model Data in Power BI
• Visualize Data in Power BI
• Data Analysis in Power BI
• Manage Workspaces and Datasets in Power BI

You can finish the aforementioned courses at your own pace.

5. Certification of Professional Achievement in Data Sciences (by Columbia University)

Level: Beginner level
Duration: 12 credit hours (roughly 1 semester)

Offered by Columbia University’s Data Science Institute (in collaboration with The Fu Foundation School of Engineering and Applied Sciences and the Graduate School of Arts and Sciences), this certification program is ideal for students who want to develop key data analytics skills.

The Certification of Professional Achievement in Data Sciences focuses on building strong foundations, preparing the students for a career in data science.

This is a non-degree and part-time program. Learners have to complete a minimum of 12 credit hours and four core courses, including:

• Algorithms for Data Science – requires a basic knowledge of programming, calculus, and algebra. This course will dive into data organization, modeling, sorting, linear programming, and much more.
• Probability and Statistics for Data Science – requires basic knowledge of calculus. This course will take you through the application of probability and statistical analysis in data science.
• Machine Learning for Data Science – requires a strong background in calculus, probability theory, and stats. This is an introductory course for machine learning.
• Exploratory Data Analysis and Visualization – requiring a background in programming, this course will develop foundations in data visualization, perception of discrete and continuous variables, and much more.

6. CCA Data Analyst

Level: Intermediate level
Duration: Self-paced (you can give the exam right away without spending any time on learning)

Next on the list is the “CCA Data Analyst” certification exam by Cloudera.

This certification has been designed for existing SQL developers, business intelligence analysts, data analysts, database administrators, and system architects, who want to demonstrate their competency in Cloudera’s CDH using Hive and Impala.

There are no prerequisites for this exam. If you want, you can opt for an OnDemand course by Cloudera.

The exam lasts for a total of 120 minutes and is based on 8-12 performance-based tasks (with a large dataset) that have to be completed on the Cloudera Enterprise cluster. Candidates have to score at least 70% in order to pass the exam.

7. IBM Data Science Professional Certificate

Level: Beginner level
Duration: Self-paced (typically takes about 10 months)

If you want a comprehensive education in data science, the “IBM Data Science Professional Certificate” program is perfect.

Offered by IBM through Coursera, this extensive certification program (typically lasting 10 months) focuses on building foundational skills that will help you kick-start your career in data science and analytics.

The program is based on a total of 9 courses that will dive into machine learning, predictive modeling/predictive analytics, data visualization, machine learning, and programming languages including Python and SQL.

Courses include:

• What is Data Science?
• Tools for Data Science
• Data Science Methodology
• Python for Data Science
• Databases and SQL for Data Science
• Data Analysis with Python
• Data Visualization with Python
• Machine Learning with Python
• Applied Data Science Capstone

All of the aforementioned courses (with the exception of the first one) include hands-on practical labs in the IBM Cloud environment.

What makes this data analyst certification program special is that it doesn’t require you to have any prior knowledge of programming. What’s more, nearly 39% of learners start their careers after completing this program.

In the end, you’ll receive a certification of completion that you can share anywhere to showcase your competency.

8. Amazon AWS Certified Big Data

Level: Intermediate level

Duration: Self-paced (you can give the exam right away without spending any time on learning)

If you have at least 2 years of experience with AWS technology and want to showcase your expertise in big data analytics, the “Amazon AWS Certified Big Data” program is an ideal option for you.

By passing this certification exam, you’ll demonstrate competency in implementing core AWS Big Data services.

While there are no strict prerequisites, the folks at Amazon do recommend completing one of their associate-level certifications or the AWS Certified Cloud Practitioner exam.

Furthermore, having some experience with architecting AWS Big Data services and 5 years of experience in data analytics is highly recommended.

9. SAS Certified Advanced Analytics Professional

Level: Advanced level
Duration: Self-paced (you can give the exams right away without spending any time on learning)

The “SAS Certified Advanced Analytics Professional” credential is meant for highly experienced data analysts who want to showcase their expertise in predictive modeling and statistical analysis.

Unlike the previously-reviewed options, in order to get this certification, you’ll have to pass three exams, including:

• SAS® Certified Predictive Modeler
• Certified Specialist: Advanced Predictive Modeling
• SAS Specialist: Text Analytics, Time Series, Experimentation and Optimization

Of course, each of those exams has its own preparation roadmap and learning outcomes. You can prepare and appear for them at your own pace.

Additionally, a candidate who wants to get the SAS Certified Advanced Analytics Professional credential must have ample experience in machine learning, modeling techniques, optimization techniques, time series forecasting, and other core areas.

10. Microsoft Certified Solutions Expert (MCSE): Data Management and Analytics

Level: Advanced level
Duration: Self-paced (you can give the exams right away without spending any time on learning)

Next on the list is the data analyst certification by Microsoft in Data Management and Analytics.

This sought-after credential validates a professional’s expertise in managing a high-level data center, system administration, data engineering, virtualization, networking, and storage.

To get this MCSE credential, you first have to complete a prerequisite certification. Here are your options:

• MCSA: BI Reporting
• SQL 2016 Database Development
• SQL 2016 BI Development
• MCSA: SQL Server 2012/2014
• MCSA: SQL 2016 Database Administration

After finishing a certification course, you’ll have to take all of the following exams:

• Implementing Data Models and Reports with Microsoft SQL Server
• Developing Microsoft SQL Server Databases
• Designing Database Solutions for Microsoft SQL Server

Each certification course and exam has its own unique learning paths and goals. You can evaluate your options on Microsoft’s website and learn more.

Are they useful when applying for a job?

Data analytics certifications can be highly useful when applying for a job in the field for several reasons:

1. Validation of Skills and Knowledge: Certifications serve as a third-party validation of your skills and knowledge in data analytics. Employers can have confidence that you possess a certain level of expertise and proficiency in the subject matter covered by the certification.
2. Competitive Advantage: In a competitive job market, having a relevant certification can set you apart from other candidates who may not have one. It can be a distinguishing factor that catches the attention of hiring managers.
3. Career Advancement: Certifications can open doors for career advancement and help you qualify for more senior or specialized roles within data analytics. They demonstrate a commitment to professional development and improvement.
4. Industry Recognition: Certain certifications are widely recognized and respected within the industry. For example, the Certified Analytics Professional (CAP) designation is highly regarded in the analytics community. Possessing such a certification can enhance your credibility.
5. Easier Skill Assessment: Certifications provide a standardized way for employers to assess your skills. They can use the certification as a benchmark to evaluate your capabilities, which can simplify the hiring process.
6. Adaptability: Data analytics is a rapidly evolving field with new tools and technologies emerging regularly. Earning certifications can demonstrate your willingness and ability to stay up-to-date with industry trends and adapt to new challenges.
7. Cross-Industry Applicability: Many data analytics certifications cover fundamental concepts and skills that are applicable across various industries. This versatility allows you to pursue opportunities in different sectors.
8. Networking Opportunities: Some certification programs offer networking events, forums, or communities where you can connect with other professionals in the field. Building a professional network can be valuable for job opportunities and knowledge sharing.
9. Salary Enhancement: Research has shown that individuals with certifications in data analytics tend to earn higher salaries than those without certifications. Employers may offer higher compensation for certified professionals.
10. Global Recognition: Many certifications have international recognition, which can be particularly valuable if you plan to work in different countries or for global organizations.