Do Data Scientists Need to Know AWS?

Data science is one of the fastest-growing fields you can work in today. New technologies and more data than ever before make data science an exciting and challenging job. In general, cloud computing knowledge is vital for data scientists since the cloud allows you to carry out advanced data analytics without overextending your computer and servers’ capabilities, but do you need to have a qualification in AWS to get the job?

Whether or not data scientists need to know AWS depends on a number of factors, including their specialty. Chances are, you will not need an AWS certification to get most jobs in data science. However, if your company uses AWS to process their data, then the right AWS cert may prove very useful. 

Being familiar with cloud computing is absolutely vital for any data scientist. Working solely off your own PC and servers (on-premises) will likely not give you the computing power, or the storage space, that you need to give impactful insights to your company. In this article, we’ll discuss which aspects of cloud computing are necessary for data science and whether you need an AWS cert in order to work in the field. 

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

What Is Cloud Computing?

The amount of data generated every day has grown hugely in recent years. Prior to the introduction of sites like Myspace and Facebook in the mid-2000s, user generated data with activities such as liking, commenting and sharing was almost negligible by today’s standards.

That has all changed now, with each internet user leaving a huge data footprint on the sites which they visit. That means that there is now more data out there than we know what to do with! It takes a lot of people with a lot of know-how to process all that information!

You probably know this already, but cloud computing is when companies ‘rent’ storage space and computing power from another company’s servers like Amazon or Google. 

In recent years, this has leveled the playing field for small and medium-sized businesses, since they can now have access to the software and computational power that larger companies already enjoyed, which can be used to provide valuable business insights. 

Used by companies all around the world, Amazon Web Services (AWS) is currently the leading cloud computing service, followed by players like Google Cloud and Microsoft Azure. Amazon offers a number of different certifications in AWS, some of which are more useful to data scientists than others. We will go over what each of them entails later on in the article. 

What Is Data Science?

In essence, data science is the practice of mining massive data sets for key insights that companies can use to inform business strategies. In larger companies, this can be done in conjunction with AI deep learning. For example, deep learning is used by Amazon to recommend products to customers based on their own purchase history and the purchase history of other customers who bought the same product. 

Data scientists use algorithms, machine learning, and other tools to find patterns in large, unorganized data sets. These patterns can then be analyzed for insights that can guide businesses to better understand the needs of their customers or clients. Data science is one of the fastest-growing fields out there, thanks to the massive explosion of data that has been generated in recent years. 

AWS and other cloud services are extremely useful to data scientists since they allow you to carry out this process of finding and analyzing patterns in large datasets, even if your company does not have the computing power or storage space on their own servers to support these activities. Using the cloud also means that if your computer crashes while running analytics, you do not lose all your work!

AWS for Dummies

AWS is a hosting provider or platform. It gives you access to many different services, which can help you carry out different tasks on the cloud. Again, some will be more useful to data scientists than others. Here is a short run-down of a few of the main services AWS provides:

• EC2 (Elastic Compute Cloud): This is the most commonly used service on AWS. It allows you to develop and deploy applications by using computing capacity in the AWS cloud. You can scale this service so that you only use as many virtual servers as you need to run your applications. 

• VPS (Virtual Private Cloud): This service allows you to rent a chunk of the AWS cloud. Using VPC, you can create networks within the AWS cloud, then run your servers in those networks. That means that you can use the cloud without controlling the cloud.

• RDS (Relational Database Service): This service allows you to run and manage databases on the cloud. It helps you manage the administrative tasks required to maintain a relational database, making the process less resource-intensive.

• S3 (Simple Storage Service): This one exactly does what it says on the tin. Like Google Drive or Dropbox, S3 allows you to upload data that can be stored on the AWS cloud or shared with other users. Using S3, you can create ‘buckets’ that can be used to store object-based files. 

• SageMaker: This is the service that allows you to build, train, and deploy machine learning for a number of uses. We will go further into this later in the article. 

Hadoop and Redshift Clusters

One of the major advantages of cloud computing for data scientists is the ability to use Apache Hadoop clusters. Hadoop clusters are networks of computers working in parallel to complete a common goal: storing and analyzing large unorganized datasets. In a Hadoop cluster, each computer is known as a ‘node.’ They can be ‘master’ nodes, which oversee key operations, or ‘worker’ nodes, which store the data and run computations.

The AWS equivalent is known as ‘Redshift.’ This service allows you to upload a dataset, then perform data analysis queries, making it one of the most useful AWS services for a data scientist. This service is scalable, which means that you can use a different number of nodes to analyze the data depending on the dataset’s size. Hadoop and Redshift also prevent data loss by replicating data across multiple nodes.

What Kinds of AWS Certifications Can You Get? 

Amazon offers 12 different certifications in AWS, some of which will be of more use to data scientists than others. You can get certifications at four different levels; foundational, associate, professional, or specialty. There are different paths you can take, depending on which career you are looking to break into. 

Here is a short rundown of some of the different certs you can get, and which ones will help you on your quest to join the fast-growing data science profession:

Foundational

Many people think that this basic qualification will only be useful in getting a job insofar as it shows a potential employer that you are interested in cloud computing. It is recommended that you have about six months of experience working with AWS in any role before attempting this certification. 

AWS Certified Cloud Practitioner: This is the basic, entry-level cert you can get from AWS. Unless you have lots of experience with AWS already, it is recommended that you take this exam before trying to move on to the more advanced certs. 

Associate 

These certs are recommended for people with an intermediate level of experience in designing applications. They are not as tough as the professional level certs, but a big step up from foundational!

AWS Certified Solutions Architect: This cert is all about designing and implementing applications using AWS. It also touches on how to create hybrid systems using both on-premises and AWS components. This will be somewhat useful, but not absolutely necessary for most data scientists. 

Professional

These certs are not for the faint-hearted. For the particular cert covered here, you will need to have status as an AWS Certified Developer already – Associate or AWS Certified SysOps Administrator – Associate.

AWS Certified DevOps Engineer: This cert primarily focuses on continuous delivery (CD) and automation. CD involves creating an automated process whereby applications are tested again before being deployed to customers. Automation of production and the management of applications on the AWS platform are other key aspects of DevOps. 

Specialty

These are the certs you really want to have as a data scientist. It is usually recommended that you have two years of real-world experience in the specialty area before trying to get the cert. These certifications are for professionals who want to become experts in their field. 

• AWS Certified Data Analytics: This cert used to be called ‘Big Data’ until April 2020, so it is easy to see how this one could be very useful for a data scientist. It covers the various techniques that allow you to carry out in-depth data analytics using AWS, as well as the security measures required to keep that data safe.

• AWS Certified Machine Learning: This is another very useful cert for a data scientist to have. It covers the design, creation, and implementation of machine learning solutions. It is recommended that you have two years of experience using machine learning on AWS before you take this exam.

• AWS Certified Database: This is a brand new qualification offered for the first time in April of 2020! This is another great choice for a data scientist, especially if your chosen career path involves working with large databases. It will not only teach you how to design and deploy databases but also how to keep them secure from hackers. 

What Features of AWS Are Useful for Data Scientists?

Even though an AWS cert is not necessary for being a data scientist, several features of the system can be very useful in the profession. Further, while there are many alternatives to AWS, it is considered the most mature and reliable cloud computing platform in the market. That is not likely to change any time soon. 

Here are a few of the main ways that AWS can facilitate efficient and effective data science:

Data Storage

As mentioned above, S3 (Simple Storage System) is the AWS data storage feature. This can be very useful for a data scientist, although it must be said that data scientists often require more storage than S3 can provide. RDS is the data warehousing system on AWS, and can also be a useful tool for managing data and automating administrative tasks. 

AWS Glue is another service that can be useful for data storage. It creates a unified catalog of all the data in a lake, which can then be searched using metadata. AWS Glue connects several different AWS services into a managed application, simplifying the process of data storage and keeping everything together. 

Data Analytics

AWS contains a number of different services to help you analyze large datasets. These include Amazon Athena, Amazon Elastic MapReduce, and Amazon Kinesis. Each of these applications allows you to generate an in-depth analysis of structured and unstructured data. AWS also offers Amazon QuickSight, which can be used to generate handy visualizations of datasets. Analytics is another area in which AWS is invaluable to the data scientist. 

Amazon Kinesis allows you to process video streaming data in near-real-time. For example, law enforcement can use Kinesis to monitor thousands of traffic cameras, analyze the license plates of passing cars, then match them to the license plates of vehicles that have been reported stolen. This is a serious piece of data analytics software. 

Machine Learning

SageMaker is AWS’s feature, which allows you to build, train, and deploy machine learning. This can be used to generate recommendations for customers in online stores or recognize speech and images. The predictions generated by SageMaker AI can be used to inform important business decisions. By making machine learning that much easier, SageMaker is an extremely valuable tool for any data scientist. 

How to Stay Safe on AWS

When you are dealing with data, security should always be a primary concern, especially if you are dealing with large amounts of other people’s data. Here are a few tips on staying secure when dealing with data on AWS:

• Set up the security strategy first. This will make it much easier to bake the security strategy into all the functions which come after. Also, when you use a new tool, you can make sure it supports your strategy before implementing it. 

• Use the AWS Marketplace to install firewalls. While it might be tempting to install firewalls only on buckets that contain sensitive data, it is a good idea to install them on every layer just to be safe. 

• Implement a password renewal strategy. Set an amount of time after which passwords will be changed. It is also recommended to set up two-factor authentication and automated lockout after multiple failed attempts. 

Do I Need an AWS CERT to Be a Data Scientist? 

The short answer is no. If you join a new company that uses cloud software to analyze big data, there is no guarantee that they will be using AWS. 

You should know the basics of AWS for sure, since you may be asked about the principles during an interview, but whether or not you will need advanced AWS knowledge changes from case to case. For a job in a company that exclusively uses AWS, advanced knowledge will be a massive plus. 

Rather than getting a cert, it might be an idea to create a trivial project on AWS, where you can put in your model and mess around with some of the features. That way, you can develop a basic understanding of how AWS works without committing to learning the more advanced aspects which you may or may not need in your career. This will help a lot with basic AWS questions when you are doing an interview. 

While you may not need an AWS cert specifically, it is still a good idea to have, at the very least, a basic understanding of cloud computing. That is because cloud computing will make data mining and data acquisition far easier, quicker, and less resource-intensive. 

If you join a new company, and they use a specific system, like Google Cloud or AWS, it is probably a good idea to become pretty familiar with that particular system. 

Conclusion

So, it is not necessary to get AWS certification if you want to be a data scientist. However, it really does help in a lot of ways. If you want to stand out from the crowd in a competitive field, then the right AWS cert could be the thing that gets you the job. Just having put the work into cloud computing knowledge may be enough to make you the right candidate. 

That said, it is possible that you get your dream data science job only to find out that the company uses Google Cloud, and you have wasted a lot of time learning the wrong system! In any case, good luck finding the right qualification and joining an exciting and fast-growing industry!

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

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