They still have an opportunity to make an amazing comeback with the help of data science. Yes, it is necessary for engineers to learn data science in 2021, in order to keep their place in the job market. Data science is a blend of mathematics, machine learning, business decision tools, and algorithms. It helps businesses bring out knowledge and insight from structured and unstructured data.
With data becoming the center of decision-making in almost every industry, the demand for data science professionals has also surged in the recent past. On the other hand, engineers are highly skilled professionals who need a switch. Most engineers are looking for ways to shift from their engineering jobs to data science or the big data industry to stay ahead in the job marke
Big data skills are crucial to land up data engineering job roles. From designing, creating, building, and maintaining data pipelines to collating raw data from various sources and ensuring performance optimization, data engineering professionals carry a plethora of tasks. They are expected to know about big data frameworks, databases, building data infrastructure, containers, and more. It is also important that they have hands-on exposure to tools such as Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig, and to name a few.
Here, we list some of the important skills that one should possess to build a successful career in big data.
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This week, take part in our survey and let us know where you recently applied Data Science, Analytics and Machine Learning. Also: Data Scientist, Data Engineer & Other Data Careers, Explained; A Guide On How To Become A Data Scientist (Step By Step Approach); A checklist to track your Data Science progress; How to Determine if Your Machine Learning Model is Overtrained; Differentiable Programming from Scratch; and much, much more.
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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I just want to say that whether you choose data science or data engineering should ultimately depend on your interests and where your passion lies. However, if you’re sitting on the fence, unsure of which to choose because they are of equal interest, then keep reading!
Data science has been a hot topic for a while, but a new king of the jungle has arrived — data engineers. In this article, I’m going to share with you several reasons why you might want to consider pursuing data engineering over data science.
Note that this IS an opinionated article and take what you want from this. That being said, I hope you enjoy!
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Data engineering is among the core branches of big data. If you’re studying to become a data engineer and want some projects to showcase your skills (or gain knowledge), you’ve come to the right place. In this article, we’ll discuss data engineering project ideas you can work on and several data engineering projects, and you should be aware of it.
You should note that you should be familiar with some topics and technologies before you work on these projects. Companies are always on the lookout for skilled data engineers who can develop innovative data engineering projects. So, if you are a beginner, the best thing you can do is work on some real-time data engineering projects.
We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting data engineering projects which beginners can work on to put their data engineering knowledge to test. In this article, you will find top data engineering projects for beginners to get hands-on experience.
Amid the cut-throat competition, aspiring Developers must have hands-on experience with real-world data engineering projects. In fact, this is one of the primary recruitment criteria for most employers today. As you start working on data engineering projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career.
That’s because you’ll need to complete the projects correctly. Here are the most important ones:
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