The Top 5 Data Science Certifications  | Towards Data Science

IntroductionTensorFlowSASIBM Data Science — PythonTableauGoogle Machine LearningSummaryReferences
Introduction
TensorFlow
SAS
IBM Data Science — Python
Tableau
Google Machine Learning
Summary
References
While there are countless articles on this particular topic, I wanted to search unique, yet commonly used programs or platforms practiced by a typical data scientist along with their respective certifications. Some of these are free, some will have a small price, but all may be well worth investing. It all ultimately depends on what your focus and path are in data science. There are several reasons for wanting to pursue a certificate in your data science career; some may be more direct, like a data science certificate, or a more indirect certificate like programming in Python. With in-person teaching and learning becoming less popular, even before the current state of the world, online learning is not looked at as a nice-to-have only, but almost a requirement in order to become a successful data scientist or data science applicant. Whereas in the past, online programs and certifications seemed to have a negative cloud around them, they are now becoming expected by employers and show those same employers how willing you are to improve on your skills, no matter where you are in your career.
I have had a somewhat formal master's of science in data science education from Southern Methodist University; however, it was online, and employers did not hesitate to think it was any less of a degree. Similarly, several hiring managers enjoyed and were generally impressed when I would bring up certificates in data science, and other forms outside of traditional education like tutorials. In addition to having experience in various forms of learning, I have experience in being a professional data scientist as well and have therefore gained a general sense of which skills employers desire you to have. Additionally, I have become well aware of some of the tools and platforms that could really benefit you in your day-to-day work.
Below, I will outline five data scientist certifications that can boost your career in data science.
TensorFlow [2] is widely used by both data scientists and machine learning engineers. It serves as an open-sourced platform that includes powerful libraries that drive your machine learning model building process. Typical projects with TensorFlow may go over:
neural networks, generative adversarial networks, image classification, text classification, regression, boosted trees, time series forecasting, and much more
The title of the TensorFlow certification is:
TensorFlow Developer Certificate program [2].
The program entails models over computer vision, convolutional neural networks, and natural language processing (NLP). While the cost of the exam is $100, TensfowFlow recommends obtaining an education stipend from your current employer (suggesting relative success). This certificate is useful if your focus is on either or both machine learning and deep learning. The certificate itself is not a tutorial of course, but only a certified method of confirming your success in TensorFlow. However, they recommend the TensorFlow in Practice Specialization offered through Coursera. The best part of this specialization is that it covers all things TensorFlow for free. Only take the exam if you believe the $100 fee is worth it or if you can get it waived with the stipend program. Some interesting stats from this specialization are that:
40% started a new career after completing
12% pay increase or promotion
As you can see, this certificate along with its respective certification is an outstanding way to show off your skills and certify your competency from a widely used and attractive platform.
SAS [3] is perhaps, the least commonly used platform for data scientists. However, this statement can be turned into a positive, as you can ensure that your skillset is truly unique and one of a kind. You will be able to offer something that most data scientists can not. While Python and R are the typical programming languages used by both data scientists and machine learning engineers, SAS is another language, almost more similar to SQL that focuses on statistics. When I started my data science master’s degree, this platform was the first method of learning I had with data science.
SAS stands for Statistical Analysis System. As a data scientist, you can dangerously slip into black-box machine learning. However, when you learn with SAS, you get pretty granular with statistics. This knowledge will be especially useful in data science interviews when hiring managers will ask you complex statistical questions that cannot be answered with typical Python data science libraries. A lot of the benefit of SAS is testing for normality with methods like Q-Q plots, histograms, and residual plots. and performing tests like ANOVA’s and MANOVA’s (Analysis of Variance, or Multiple Analysis of Variance).
The title of the certificate is:
SAS Programmer Professional Certificate [3].
This certification is also offered through Coursera. You can enroll for free and get certified at a price.
Keep in mind, even though these are certifications and the courses are usually free, the exam or actual tangible certification will usually cost you. However, you may still do most of these courses, if not all, for free, and if your employer or whomever you are showing your certificate to is understanding, you may be able to show them your work and completion of the associated courses without paying the fee.
You will learn techniques in the SAS programming language as well as explore different data types. The career outcomes of this certificate and its respective course are quite impressive:
21% started a new career after this specialization
50% got a pay increase or promotion
Whereas the previous certificates and their respective courses were focused on specific sub facets of data science, this certification encompasses much of the overall, general data science process. The title of this certificate is:
IBM Data Science Professional Certificate [4].
Similar to the above certificate, this one is also offered through Coursera.
Since this certificate is more encompassing, I will list out all nine courses for this professional certification.
What is Data Science?
Tools for Data Science (Jupyter Notebook, RStudio IDE, etc)
Data Science Methodology (computing power, deployment, etc)
Python for Data Science and AI (types, variables, classes module, etc)
Databases and SQL for Data Science (Structured Query Language, etc)
Data Analysis with Python (Pandas, Numpy, and Scipy libraries, etc)
Data Visualization with Python (Matplotlib, Seaborn, etc)
Machine Learning with Python (classification, clustering, etc)
Applied Data Science Capstone (RESTful API calls, Folium, etc)
As you can see, this certification includes nearly every part of data science and even machine learning. Depending on your goal and where you apply, this course could completely substitute a degree. The evidence to support that statement comes from:
46% started a new career after completing
19% got a pay increase or promotion
These stats are incredible — making it one of the most beneficial courses and certifications you can take. I recommend this course if you want a holistic view of data science. To articulate how popular this course is, around 1 million people have viewed the certificate homepage.
This certificate could cause some controversy, but it should be something you seriously take into consideration. Perhaps it is more of a data analysis or business intelligence skill, Tableau [5] serves as a visualization tool to describe metrics and statistics. However, there are several other ways that Tableau can be useful for data scientists. Some of those benefits include:
visual representation of model ingestion data
exploratory data analysis
shifts and trend analysis
impressive visualizations for your data science model metrics
Oftentimes, when you are a data scientist, you will need to present your findings, and Tableau is an easy tool that helps you describe your model stats and metrics so that your stakeholders can see daily, for example, how well the business problem is being solved.
There are several different specific certifications, but I will highlight one:
Tableau Desktop Specialist [5].
This certificate focuses on the fundamentals of Tableau, which is most likely all you will need. As a data scientist, it is assumed that you have impressive problem-solving skills, so once you know the basics, you will be able to learn the more complex functions as you go. The fee is $100 for this certification. The exam is multiple-choice, multiple responses, and hands-on with 30 questions in 60 minutes, with automatic scoring. Tableau offers several classes as well that will help you in acing this exam.
As some of these courses and certificates focus more on data science and machine learning directly, this certificate will separate you in the large pool of data scientists by knowing how to present your data science findings with Tableau.
The last certification, perhaps the most difficult, is from Google [6]. It is preferred to achieve this certificate if you are a machine learning engineer, but if you are a data scientist focusing just on models, this certificate will allow you to go further into deployment and engineering. The exam will test these main complex concepts:
Frame ML problemsDevelop ML modelsArchitect ML solutionsAutomate & orchestrate ML pipelinesPrepare and process dataMonitor, optimize, and maintain ML solutions
You can choose the beta exam which allows becoming Google Cloud Certified, saving you 40% on the cost, and also some swag — exclusive Google-branded apparel. The title of the officiating certification is:
Professional Machine Learning EngineerBETA [6].
The key objectives that the exam tests you on are extremely useful concepts that every data scientist or machine learning engineer could eventually benefit from. Some of those key objectives are:
framing the ML problem by translating the business challenge into an ML use case
ML solution architecture with SDLC best practices
data preparation and processing with designing data pipelines
ML model development with productionizing
ML pipeline automation and orchestration with CI/CD test and deployment
ML solution monitoring, optimization, and maintenance with tuning performance and identification of retraining.
As you can see, this certification is complex and covers difficult, yet incredibly important characteristics of data science and machine learning.
You do not need all of these courses or certificates to become the best data scientist; however, they can all work to make you highly successful in different ways. You have learned about five top certifications. Here are all of them listed out with their respective links:
TensorFlow Developer Certificate program [2]SAS Programmer Professional Certificate [3] IBM Data Science Professional Certificate [4]Tableau Desktop Specialist [5]Professional Machine Learning EngineerBETA [6]
Of course, there are more courses and endless certificates. But, the most important thing you can do for your career is to jump into either of these certificates (or other similar ones) and work towards bettering yourself as a data scientist. Imagine being certified in TensorFlow, SAS, IBM Data Science, Tableau, and Google Machine Learning. You would certainly impress employers of the current and future. Not only would these certificates make you look better to other data scientists when interviewing, but they will also ultimately help you to become better in your data science career. Learning should never stop, and completing courses and certificates is no exception.
I hope you found this article interesting, unique, and useful! Feel free to comment down below and start learning as well. Thank you for reading!
[1] Photo by SCREEN POST on Unsplash, (2020)
[2] TensorFlow, TensorFlow Developer Certificate program, (2020)[3] Coursera, SAS Programmer Professional Certificate, (2020)[4] Coursera, IBM Data Science Professional Certificate, (2020)[5] Tableau, Tableau Desktop Specialist, (2003–2020)[6] Google, Professional Machine Learning EngineerBETA, (2020)
[7] Photo by Remy_Loz on Unsplash, (2020)