There are some websites that show accurate salaries based on experience, location and company. Based on these website we will discuss how much salaries software engineer and data scientists are getting as of 2021
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#data-science #artificial-intelligence #machine-learning #developer
The annual Analytics India Salary report presented by AIM and AnalytixLabs is the only annual study in India that delves into salary trends and provides a comprehensive view of the changing landscape of analytics salaries. The report, now in its seventh year, look at the distribution of average salaries across several categories including years of experience, metropolitan regions, industries, education levels, gender, tools, and skills.
The Data Analytics function is experiencing significant growth and development in terms of skills, capabilities, and funding. Last year, despite the pandemic, the Indian start-up industry witnessed $836.3 million investment, almost a 10% (9.7%) increase than the previous year. Also, more than one in five (21%) analytics teams across firms in India witnessed a growth in the last 12 months and the post-pandemic job market saw an upswing of data science jobs. The development of the data science domain is evidenced by the high salaries drawn by analytics professionals across the organization, with Analytics professionals doing relatively well in spite of the pandemic.
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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.
#kdnuggets 2021 issues #analytics #careers #data engineer #data engineering #data science #data scientist #poll #survey
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.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
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!
#2021 apr opinions #career advice #data engineer #data engineering #data science #data scientist
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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