With lower barriers to entry, and new self-service platforms that make it easier than ever to adopt cutting edge technologies and machine learning algorithms, Data Science has never been more accessible.
As global adoption continues, the industry’s growth can partly be attributed to big name tech leaders like Amazon, Microsoft, IBM, Google and Facebook, who’ve introduced several cloud services and solutions, allowing customers with limited industry knowledge to apply Machine Learning approaches to solve business tasks.
Amazon’s Web Services, in particular, has emerged as a leader in the pact.
With customers in over 190 countries and over one million active customers, Amazon Web Services (AWS) is transforming enterprises and reshaping workplaces all over the world…
Just take a look at the numbers:
- AWS leads the cloud market with a 32% share. (Canalys)
- AWS dominates in the IaaS space: 31% of cloud infrastructure as a service of market share belongs to AWS while 10% belongs to Microsoft and 7% to IBM.
- About one-third of people who visit websites on the Internet daily access websites which are powered by AWS!
- AWS attracts almost half (52%) of the early-stage cloud users. (RightScale)
- AWS revenue should top $43 billion by 2022. (Wikibon)
90% of companies today run on the cloud, and some of the largest companies in the world are choosing AWS. Netflix, Expedia, Reddit, and the NSA use AWS to run their applications, but that’s not all. New and small companies are choosing AWS as their vendor of choice for getting started with the cloud.
Below we review the role of AWS in Data Science, share some basic knowledge for the platform’s application in Data Science projects, and outline the training and preparation process involved in obtaining a lucrative AWS certification.
AWS services for Data Science
An emerging trend that we’ve seen in Data Science is to leverage several data cloud services to solve business challenges. Some of the reasons why this approach is effective include:
- Simplicity of ML pipelines creation: Usually there is no great need to develop algorithms for business solutions from scratch. Most companies just need fast pipeline configurations from cloud-based components that they can apply to their data.
- Cloud-based: ML-based computing (especially Deep Learning) requires a lot of resources and therefore the quick and efficient nature of cloud services is more conducive to this.
- Additional services: Cloud platforms usually provide many additional services and integrations like deploying, monitoring, resource scaling and distributed computing, etc.
AWS is one of the key vendors in the cloud technology segment, providing various tools and services to solve data analysis and processing problems. They were among the first vendors that developed tools to implement and further enhance Data Science projects.
AWS offers a complete tool set for implementing Data Science projects using different algorithms such as classic ML, NLP, DL, Recommendation systems, Fraud (Anomaly detection) and CV. All these services are integrated into one ecosystem which ensures simple and native integration with standard AWS services such as Amazon S3, AWS Lambda, Amazon EC2, DynamoDB, AWS Redshift, and others.
Let’s focus on the main and most important AWS cloud services with useful functionality for Data Science:
- Amazon SageMaker – This is a basic service from AWS which provides a complete tool set for creating, training and deploying ML models.
- Amazon Personalize – Personalize helps in developing a recommendation system for determining target content for web or mobile applications.
- Amazon Textract – This service implements Natural Language Processing (NLP) algorithms for text information analysis.
- TensorFlow on AWS – TensorFlow is an extension (library) for SageMaker that allows for applying Deep Learning (DL) algorithms.
- AWS Deep Learning AMI – This service is a scalable instance for DL model deployment.
- AWS DeepLens – This computer vision (CV) tool supports DL algorithms.
- Amazon Fraud Detector – One of the new AWS services that implements fraud detection algorithms and can be used in the banking sector.
- AWS Certification – The AWS Certification system can test and validate your knowledge of AWS services, and in particular, solutions for Data Science.
If you are starting out with AWS services, interactive training like the SuperDataScience AWS Certified Cloud Practitioner Certification Guide is an excellent starting point.
Courses like this contain teaching materials with detailed descriptions of basic functionality and a lot of examples. In general, these courses allow beginners to learn and quickly understand AWS functionality for further use and implementation in Data Science projects.
Understanding the basics of AWS and how their solutions work is a vital step that needs to be taken before you start preparing for AWS certifications or dive into the platform’s official training materials. Both beginners and advanced practitioners shouldn’t neglect it, so let us explain it in detail.
What is AWS Certification for Data Scientists?
Let’s use Amazon cloud services as an example to indicate the kind of competence levels that hiring managers and employers look for in applicants.
Competency assessment approaches:
- Formal education (College/University degree): Although this might be a good indication of your educational history, it might not be able to showcase your knowledge and skills in a specific area, which is something most employers want to see.
- List of completed projects or similar work based on AWS services: Of course, this approach only applies to experienced developers since most projects within the Data Science sphere are quite complex. You can easily bulk up your portfolio by enrolling in SuperDataScience’s hands on prep courses like Complete Guide to AWS Machine Learning Certification Exam Guide, and take advantage of SDS Club’s 90% discount.
- Official certificate from a tool provider: This might be the right option if you need a flexible study schedule and the ability to choose specific subjects. In our case, it is AWS Certified Machine Learning or the AWS Certified Data Analytics specialty.
The structure of the certification procedure for Data Scientists is standard. Therefore, we will only briefly describe the steps of the certification process:
- Selection of a course and skill level.
- Certification Training: based on official AWS Training and additional courses.
- Certification procedure: final exam.
Exam preparation is an important step in certification and usually includes the following aspects:
- Review and verify the recommended knowledge and experience for the examination. In the event that you missed some required learning and preparation aspects, you should complete an additional training course, which will be discussed below.
- Review the exam guide, AWS FAQs and white papers, and sample questions. This will help you better understand the exam procedure and prepare you for possible exam questions.
- Explore the AWS learning path. This will help you learn more about additional official AWS resources and courses for certification preparation.
As for the final exam, here is a look at what to expect:
- The 3-hour exam consists of 65 multiple-choice and multi-selection questions.
- Certification procedure includes either your presence in a special testing center or an online proctored exam.
- Availability of multiple languages.
And while certification does have its many advantages, the process also involved several disadvantages too. Let us review the both the pros and cons:
- Industry-wide recognition & higher salary: Global acknowledgment by most employers and high average salary (up to $132,000 per year)
- Limited mathematical knowledge: Advanced mathematical education is not required.
- Abundant Training Material: Various training and educational resources are available for this area.
- Exposure to real-world applications: As you prepare for certification, you will get a comprehensive understanding of AWS Data Science applications.
- Varying levels of certification: Several levels of certification are available within the AWS ecosystem.
- Certification perks: Upon completion of certification, you will have access to additional resources for further certification in other sectors and invitations to various AWS Summit events.
- Certificate Expiry: Certificates are only valid for two years from the date of issue and after that they need to either be re-validated or upgraded.
- Cost of Certification: The price of certification for Machine Learning starts from $300 (or you can just test your knowledge with a practice exam for $40 per try). Not to mention the cost of additional training. Thankfully, there are high-quality and economical preparation courses out there like SuperDataScience’s AWS Certification guides! All SDS Club readers receive up to 90% off when they sign up through SDS Club!
- Limited Languages: Certification and official educational resources are available in a limited number of languages. Third-party educational courses and materials, however, are generally available in more languages.
- Varying Exam Questions: The questions for the certification test procedures change constantly, so relying on the help of previous test-takers is not an option.
- Insufficient Official Training Material: Documentation and official AWS training courses are often not enough for many users, who often choose to take external courses to help fill the gaps.
This certification path is appropriate for individuals who have basic knowledge of AWS and need to boost their skillset over a relatively short period of time. Now that we have the basics covered, let’s take a closer look at training course selection and educational resources for AWS certification.
Training toward AWS Certification for Data Science
Before we dive into the educational resources and courses, let us highlight an important point that will save you time and money: 99% of your efforts should be spent on proper preparation for the certification procedure since it is an integral part of training.
Even if you are an experienced specialist in mathematics, probability theory or Data Science, success in AWS certification is not guaranteed! The exam covers varying aspects of the AWS cloud environment and its tools, and we recommend reviewing all aspects (new and old) of using AWS services and applications that you may not be familiar with.
Generally, users who have completed an AWS certification for Data Science complete a 3-part training journey:
- Study of official documentation on AWS cloud services and tool functionality.
- Training using the official training courses: AWS Training.
Training using unofficial courses, like SuperDataScience’s previously mentioned guides, including the AWS Machine Learning Certification Exam guide.
Let us describe each of these steps, explain their importance and summarize the advantages and disadvantages.
Official documentation provides students with basic understanding of AWS core functionality and applications of AWS cloud tools and services for Data Science projects. Most of these services are integrated into the same AWS cloud ecosystem. So, in addition to Machine Learning tools, you will most likely use other tools for processing, analysis, and storage of data.
In this case, the documentation provides clear and concise information enabling general understanding of the structure and functionality of this cloud system.
- Information is provided on all available and required functionality,
- The information is provided in concise and structured form, which is typical for documentation.
- This is not an educational resource, and can only help in preparing for certification as an additional source in the form of a library of available functionality.
As you can see, documentation is just a reference or a simple tutorial. This means that it might not always be efficient for the pre-certification training process. In some cases, it will simply be a complex educational task.
Official AWS Training and Certification
AWS has developed special educational resources for AWS Machine Learning developers and offers them special training programs.
Preparing for the examination via these resources involves two approaches:
- ML Learning library – Data Scientist path: Digital and Classroom Training – basic library in the form of guides, tutorials and short courses on the fundamentals of Machine Learning using the AWS platform. This path is designed for those who want to become ML experts and gain some knowledge in mathematics, statistics and data analysis. It involves the following preparation and training efforts:
- Recommended learning progression: Includes various aspects of training, starting with lecture videos and guides on some basic principles of Data Science. It ends with special practical exercises in virtual classrooms focused on AWS ML pipelines.
- Related ML specialty courses: These are designed to help individuals understand the principles and details of Data Science applications such as Natural Language Processing and Computer Vision.
- Optional training courses: Courses like these provide a basis for mathematical training for ML and an understanding of basic ML algorithms such as Linear and Logistic Regression.
- Tailored Learning Paths – more structured education courses, organized by specialization:
- Business Decision Maker – to learn business procedures and processes.
- Data Platform Engineer – to learn of data processes.
- Data Scientist – to learn necessary procedures for development of ML algorithms and models.
- Developer – for general understanding and deployment of Data Science projects.
- “Native” knowledge library of AWS services from certification tool provider.
- Good integration with AWS documentation.
- Common platform and integration with the certification procedure.
- Information is focused on the functionality of AWS tools related to Data Science tasks, and the basic principles are left out.
- Training courses present materials briefly, which is not always enough for a successful certification process.
- A clear educational structure for the certification process is not present.
Educational resources are an important part of your preparation. They contain basic materials about AWS services and are also integrated with the certification platform.
However, it should be noted that using these educational resources on their own is not always enough to understand the fundamentals of Machine Learning algorithms. Therefore, most users enroll in additional education courses from external providers.
Unofficial Certification Training
Thanks to the recent boom of online learning, mastering Machine Learning and related tools and platforms is now easier than ever before.
Universities and Data Science learning portals (such as SDS Club) offer a variety of paid and free courses that facilitate Data Science learning.
Many of these match the quality of well-known universities courses and involve industry-leading experts that inject real-world experience into your learning—making this an attractive choice for those wanting to become AWS certified.
Often, students enrolled in these types of online courses will also receive a certificate of completion. While this certificate is not comparable to official AWS certification, it does demonstrate your completion of the course, and can be displayed on your LinkedIn profile or resume.
Next, using the example of the Udemy platform and their courses, we will take a look at all the advantages and disadvantages of this aspect of preparation for AWS certification:
- AWS Machine Learning Certification Exam Complete Guide – all-in-one preparation for the AWS Certified Machine Learning specialty exam.
- Complete Guide for AWS Cloud Practitioner Certification Exam – preparation for exam on AWS cloud services and tools.
- Wide choice of subjects, including ML, DL, NLP, CV, Data Engineering, Data Analysis, AWS services, and certification features.
- Taught by industry experts.
- Clear and structured preparation for the AWS certification exam.
- Includes theoretical and practical examples.
- Additional training materials on AWS cloud structure.
- Course materials layout using the best practices.
- Good support from online course instructors.
- On-demand content, allowing you to learn at your own pace.
- Access to educational courses is available on a paid basis, but there are often discounts on such courses, which currently amount to 90% off.
Unofficial certification training courses like these perfectly complement AWS’ material and can be used both as basic training for Data Scientists and as an additional source in preparation for AWS certification.
As AWS continues to dominate the cloud market, becoming AWS certified is a guaranteed way to increase your credibility and boost your earning potential on the Data Science job market.
With so many training options available, it has never been easier to become AWS certified. And while official training material often provides you with free and high-quality resources, enrolling in an unofficial certification training program like SDS Club’s not only saves you time, it allows you to focus on the exact skills that will be tested in the exam and those that will have the most impact on your career.
If you’re interested in starting your journey toward becoming AWS certified, join us today and enroll in the SuperDataScience’s AWS Cloud Practitioner Certification Exam Guide course or the AWS Machine Learning Certification Exam Guide for up to 90% Off for the SDS Club readers. See you in class!