5 Top Data Science Certification to Pursue

Five Top Data Science Certification to Pursue


There is a large amount of data available today. Businesses have requirements for professionals who are capable of dealing with enormous amounts of data they have and extracting information out of it. It is highly evident that there is a need for individuals who are proficient in handling big data.

If you are looking forward to being such an individual, then pursuing a data science course certifications is the best move you can make for your career as a data scientist. This gives you an edge over others, and also helps you to improve your ability. Getting a certification proves that you are a skilled individual. Companies will certainly hire you when you make an offer. You can expect higher salaries as certification provides you more credibility in the industry. You will have more integrity, and prospects in the job you preferred.

There are quite a few certification programs available. This is regardless of the fact whether you are a new data scientist, and looking to gain some experience in the field or you are considering enhancing your skills. You can choose a program according to the content and your level of familiarity at the same time. Even if you are new to data science, a certification can help you to prove your skills if you do not have the relevant experience. Here, five certifications are listed, in no particular order, to help you to understand and become proficient in basic and advanced data science techniques.

List of Data Science Certifications

1. EMC Data Scientist (EMCDS) Track

This program has two certification courses: Data Science Associate and Data Science Specialist. The Data Science Associate certification covers the foundation of data science. This is preferable for beginners. Some of the topics covered in the exam include characteristics of big data, data analytics, and data science. Lifecycle of the data analytics course consists of data preparation and model building. R programming language is used to analyze the data obtained from previous stages. A large part of the exam includes advanced analytics theories and applications from machine learning and big data technology as well as the tools.

After the associate-level course is completed, you can go for the specialist-level course. It deals with advanced concepts of data science. These courses can be taken personally or online.

With this certification, you can start taking up big data and analytics projects. The goal is to be able to apply suitable methods and tools to analyze big data.

2. SAS Certified Data Scientist

To become an SAS Certified Data Scientist, you need to successfully complete two other exams: SAS Certified Big Data Professional and SAS Certified Advanced Analytics Professional. Once you pass these exams, you will be eligible to get your SAS Certified Data Scientist accreditation.

In the SAS Certified Big Data Professional program, there are two exams you need to take. These are the SAS Big Data Preparation, Statistics and Visual Exploration course where you will be practically tested about data management, regression, and visual data exploration. Second one is the SAS Big Data Programming and Loading exam. In this exam, you will have questions about Hadoop architecture, Hive and Pig programming and how to manipulate data using all this.

Under SAS Certified Advanced Analytics Professional certification, there are three exams:

Predictive Modeling Using SAS® Enterprise Miner™ 7, 13, or 14* exam; SAS Advanced Predictive Modeling exam; and SAS Text Analytics, Time Series, Experimentation and Optimization exam. In the Predictive Modeling exam, you will be tested about Enterprise Miner, how to build predictive models, and pattern analysis. For the Advanced Predictive Modeling exam, you should be aware about neural network concepts, logistic regression, how to perform predictive analysis on big data, and open source models in SAS. In the final SAS Text Analytics, Time Series, Experimentation and Optimization exam, you will be tested on text analytics, time series, incremental response models, and optimization of linear and non-linear programs.

3. Microsoft Certified: Azure Data Scientist Associate

In order to obtain this certification, you are required to take the Designing and Implementing a Data Science Solution on Azure exam. You will be better equipped with the materials if you possess a background in statistics and computer science. You will learn how to apply data exploration procedures to get insights. These insights will have to be passed on to stakeholders.

The course includes applying machine learning techniques to set up AI solutions, natural language processing, computer vision, and predictive analysis. It tests your ability to organize the development environment according to requirements and perform modeling from prepared data after fixing data inconsistencies. You have to select an apt algorithm and evaluate its performance as well as the risks. Along with this, you will have to learn to consider ethical and security concerns while building solutions.

You can prepare for this exam with a paid instructor-led training or you can opt for the free online course.

4. IBM Data Science Professional Certificate

Online learning platform Coursera, and IBM have teamed up together to provide this certification program. This program is ideal for beginners as it starts from the fundamentals of data science and advances from there. There are a total of nine courses.

It starts with an introduction to data science and goes on to explain the implementation of data science like data mining, linear regression, and other real-world examples. Next, it covers some open source data science tools you can use. It can be confusing to figure out approaches to solve certain problems. But, the data science methodology course aims to help you understand how to think as a programmer. Its purpose is to show you a methodology which you can use to make sure that the data you are using to solve problems is applicable and maintained. The course promotes iterative steps to take in data science projects by going back to previous steps and improving as you go on further.

Python for data science course deals with programming basics. This course is going to be helpful for those who are new to the Python programming language. You also get to work on a project to analyze a data set. Then comes databases and SQL for data science course. Running out SQL queries in relation to data science is something you will learn here. You get to work with real data sets, databases and data science tools. This includes learning how to build databases and analyzing data with SQL and Python.

Following this, you will gain an understanding about analysis techniques in the data analysis by Python course. The covered subjects include importing and cleaning data sets, manipulation, summarization, and developing machine learning regression models. Data visualization and trend prediction are also covered in this course. Once you have analyzed the data and have some results, you need to present them. Data visualization comes into play at this point. Line graphs, bar and pie charts, and other visualization techniques with Python are introduced.

Machine learning with Python is an interesting course. You will learn real-world applications of machine learning and why it is required. An overall outline of unsupervised and supervised learning, regression, classification, and clustering is introduced. You also get to work on projects with real-world examples. The final course in this program is applied data science capstone project. It is an open-end project where you have to make your own problem question and develop code to answer them by using all the techniques you have learnt in the previous courses. This helps you to create sample projects to present along with the certification.

5. Google Certified Professional Data Engineer

This exam evaluates your ability in designing and building data processing systems, machine learning models to make sure that you obtain valuable solutions. This certification uses Google Cloud Platform for data science solutions.

A data engineer should be able to develop systems that are scalable, reliable, efficient, and secure. There are certain topics you will need to learn in order to take this exam. Knowing how to design data processing systems is the foremost concept required. This includes understanding of database systems to be used according to the requirements, schema design, data streaming, data visualization, and other modules. Under operationalizing data systems, storage costs, related costs, data acquisition, cleansing and importing is to be studied. Machine learning operationalizing consists of hardware considerations, continuous monitoring and evaluation of models.

While taking the quality of the solutions into consideration, you need to keep certain security and compliance parameters in mind. Few of the topics included are, cloud Identity and Access Management (IAM), encryption and key management, privacy concerns, and legal concurrence. Steps like troubleshooting and constantly improving efficiency are some of the many topics you are tested on.


It is evident that being certified can greatly boost your knowledge in the data science domain. It makes companies more inclined to hire you.

The role of the data scientist calls for advanced skills. There are various other certifications apart from the ones listed above. The different certifications have overlapping subjects and aims. You can choose any of them, depending on your base knowledge and what you wish to learn.

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Author bio:

Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management – Kolkatta) with over 25 years professional experience ,Specialised in Data Science, Artificial Intelligence, and Machine Learning.

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