In the following, the author will introduce five certifications that can enhance a
data science career.
TensorFlow
SAS
IBM Data Science — Python
Tableau
Google Machine Learning
Summary
References
TensorFlow
TensorFlow is an open source machine learning framework widely used by data scientists and
machine learning engineers. It contains a variety of powerful code libraries that can assist in the process of building machine learning models. Typical usage scenarios of TensorFlow include:
Neural Networks
Generative Adversarial Network (GAN)
Image classification
Text Categorization
return
Boost tree
Time series forecast
and many more
The name of the TensorFlow certification is: TensorFlow Developer Certificate.
The framework contains various models such as Computer Vision (CV), Convolutional Neural Network (CNN) and Natural Language Processing (NLP). Because the certification exam fee is US$100 (more expensive), it is recommended that you go to your boss for reimbursement (maybe it will succeed). If your focus is on machine learning or deep learning, then this certificate will be useful. The certificate itself is not a tutorial, but an endorsement of your ability to use TensorFlow. However, the author recommends learning through the TensorFlow in Practice series of courses provided by the Coursera platform, because it covers all the content of TensorFlow and is free. As long as you think the $100 test fee is not expensive, or you can waive tuition through the bursary program, then go for the test. It is said that people who have studied this series of courses:
40% of those who completed the course started a new career
12% get a promotion and salary increase
It seems that this certification and the corresponding certificate can demonstrate your skills well, and at the same time prove your competitiveness on the broad platform of TensorFlow.
SAS
SAS is probably the least commonly used platform for data scientists. However, this may also become your advantage, because you can say with certainty that your SAS skills are truly unique and unique. What you can do, most data scientists cannot do. Unlike Python and R, which are programming languages used by data scientists and machine learning engineers, SAS is a SQL language similar to statistical information. When the author was studying for a master's degree in data science, this platform was the first data science learning platform he came across.
The full name of SAS is Statistical Analysis System. As a data scientist, you may enter a black box state when you are doing machine learning, and you cannot know how the machine learns. However, if you use SAS, you will get very detailed statistics. This is especially useful when interviewing for data science positions. When a hiring manager asks you complex statistical questions, it is impossible to answer these questions by only mastering the Python data science library. The advantage of using SAS is that you can use methods such as Q-Q charts, histograms, and residual plots to test normality, and you can also perform tests such as ANOVA and MANOVA (analysis of variance or multiple analysis of variance).
The name of this certificate is: SAS Programmer Professional Certificate.
This certification also has corresponding courses on the Coursera platform. You can register for free and get a fee certificate after completing the course.
Remember, even if these are certifications and courses are free, exams or physical certificates usually cost money. However, you can still study some or all of the courses for free, and if your boss or interviewing company approves you for studying the relevant courses, then you do not need to pay for exams or purchase certificates.
In this course you will learn the SAS programming language and processing of different data types. This certificate and related courses have a great effect on career development:
21% of those who took this series of courses started a new career
50% get a promotion and salary increase
IBM Data Science Certificate-Python
The previous certificates and courses focused on the specific direction of data science, and this certificate covers the entire general data science system. The name of the certificate is: IBM Data Science Professional Certificate.
Similar to the previous certificate, courses related to this certificate are also available on the Coursera platform. The scope of the certificate is very wide, the author lists all nine courses:
What is data science?
Data science tools (Jupyter Notebook, RStudio IDE, etc.)
Data science methodology (computing power, deployment, etc.)
Python and data science and AI (types, variables, class modules, etc.)
Database and SQL in data science (structured query language, etc.)
Use Python for data analysis (Pandas, Numpy and Scipy libraries, etc.)
Use Python for data visualization (Matplotlib, Seaborn, etc.)
Use Python for machine learning (classification, clustering, etc.)
Ultimate data science applications (RESTful API calls, Folium, etc.)
As mentioned above, this certification covers almost every part of data science and even machine learning. Depending on your goals and application location, this course can even completely replace a degree. The evidence is as follows:
46% of those who completed the course started a new career
19% get a promotion
What an amazing number, it will be one of the courses and certifications you benefit the most. If you want to fully understand data science, the author recommends this course. Approximately 1 million people browsed the course homepage, which shows the popularity of the course.
Tableau
Some people may disagree with this certificate, but you should still consider it seriously. Tableau is a visualization tool for describing metrics and statistics, so it may be more like data analysis or business intelligence skills. However, there are some benefits for data scientists to master Tableau, including:
Visual representation of model ingested data
Exploratory data analysis
Change and trend analysis
Impressive visual data science model indicators
Generally, if you are a data scientist and you need to state your findings, then Tableau is a simple and easy-to-use tool that can help you describe the state and metrics of the model so that your colleagues can view the progress of data business issues every day.
Tableau contains several certifications, but the focus is on one: Tableau Desktop Expert.
The certificate focuses on the basics of Tableau, which may be important for you at an entry level. As a data scientist, we assume that you already have excellent problem-solving skills, so once you master the basics of Tableau, you can learn more complex functions. The cost of this certification is US$100. The exam contains a total of 30 questions and takes 60 minutes, including multiple choice questions, short answer questions and practical questions, which are automatically scored by the system. Tableau also provides several courses that can help you learn faster.
Compared with other courses and certificates that are more focused on data science and machine learning, obtaining this certificate can make you different, that is, how to use Tableau to showcase your data science discoveries.
Google machine learning certification
The last certification (perhaps the most difficult) is from Google. If you are a machine learning engineer, then you better have this certificate. If you are a data scientist who only focuses on models, then this certificate can also enable you to further deploy and engineering. This certification will test your understanding of the following main complex concepts:
Defining the ML problem
Develop ML model
Build an ML solution
Automate and orchestrate ML pipelines
Prepare and process data
Monitor, optimize and maintain ML solutions
You can choose the beta version certification, and you can get Google Cloud certification after passing it, you can save 40% of the cost, and you can also get Google exclusive custom clothing. The name of this certificate is: Professional Machine Learning Engineer BETA.
The main goals of the exam are some very useful concepts, and every data scientist or machine learning engineer who has passed the exam will eventually benefit a lot. Some of the key goals are:
Define machine learning problems by translating new business challenges into ML use cases;
Use SDLC (software development life cycle) best practices to build ML solution architecture;
Data preparation and processing by designing data pipelines;
ML model development and mass production;
ML pipeline automation and orchestration with CI (Continuous Integration)/CD (Continuous Delivery) testing and deployment;
Monitoring, optimization and maintenance of ML solutions with performance tuning and model retraining logos.
It can be seen that this certification is very complex and covers the difficult core areas of data science and machine learning.
to sum up
To become a good data scientist, you don't need to complete all these courses or obtain all the certificates, but these certifications can bring you a huge improvement in different ways.
