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Top 6 Data Science Interview Red Flags

Top 6 Data Science Interview Red Flags

Excited to face your first data science interview? Probably, you must have double-checked your practical skills and theoretical knowledge. Technical interviews are tough yet interesting. Cracking them and bagging your dream job is no mean feat.

Thus, to lend you a helping hand, we’ve compiled a nifty list of some common red flags that plague data science interviews. Go through them and decide how to handle them well!

Boring Portfolio

Having a monotonous portfolio is not a crime. Nevertheless, it’s the most common allegation against data scientists by the recruiters. Given the scope, you should always exhibit your organizational and communication abilities in an interesting way to the hiring company. A well-crafted portfolio will give you instant recognition, so why not try it!

Deep Learning and AI using Python

Sloppy Code

Of course, your analytical skills, including coding is going to be put to test during any data science interview. A quick algorithm coding test will bring out the technical value you would add to the company. In such circumstances, writing a clumsy code or a code with too many bugs would be the last thing you want to do. Improving the quality of coding will accelerate your hiring process for sure.

Confusion about Job Role

No wonder if you walk up to your interviewer having no idea about your job responsibilities, your expertise and competence will be questionable. The domain of data science includes a lot of closely related job profiles. But, they differ widely in terms of skills and duties. This is why it’s very important to know your field of expertise and the skills your hiring company is looking for.

Zero Hands-on Experience

A decent, if not rich, hands-on experience in Machine Learning or Data Science projects is a requisite. Organizations prefer candidates who have some experience. The latter may include data cleaning projects, data-storytelling projects or even end-to-end data projects. So, keep this in mind. It will help you score well in the upcoming data science interview.

Lack of Knowledge over Data Science Technicalities

Data analytics, data science, machine learning and AI – are all closely associated with one another. To excel in each of these fields you need to possess high technical expertise. Being technically sound is the key. An interview can go wrong if the recruiter feels you lack command over data science technicalities, even though you have presented an excellent portfolio of projects.

Therefore, you have to be excellent in coding and harbor a vast pool of technical knowledge. Also, be updated with the latest industry trends and robust set of algorithms.

Ignoring the Basics

It happens. At times, we fumble while answering some very fundamental questions regarding our particular domain of work. However, once we come out of the interview venue, we tend to know everything. Reason: lack of presence of mind. Therefore, the key is to be confident. Don’t lose your presence of mind in the stifling interview room.

Thus, beware of these drooping gaps; being a victim of these critical objections might keep you away from bagging that dream data analyst job. Instead, work on them and win a certain edge over others while cracking the toughest data science interview session.

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Note:

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The blog has been sourced from upxacademy.com/eleven-most-common-objections-in-data-science-interviews-and-how-to-handle-them

 

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Harnessing Big Data for Water Management

World Water Day: Save Water with Big Data

Appalling forces are re-establishing the relationship between humans and water.

In the past, communities developed slowly, while the weather remained constant. Water sources never depleted at tumultuous rates as it has today. Water is no longer a dependable resource. That’s why many countries and cities are embracing smart technologies to manage water efficiently and preserve it for the coming generations.

As we observe the United Nations World Water Day on Wednesday, 22nd March, it is apt to assess the development being made in conserving this diminishing resource.

World-Water-Day-Save-Water-Save-Water-Save-Nature

 Today, the Internet of Things (IoT) – a blooming worldwide network of devices and appliances linked to the internet – has materialized as a propitious solution to save water and protect clean drinking water, especially in cities. 

To begin our discussion, Netherlands is on its way to develop a pioneering program to address the relevant problems of increasing sea levels, surging number of droughts and the effect of extreme weather changes on its trains, bus networks and roadways, and the efficiency with which the entire nation tackles situations like this. The ambitious project, Digital Delta draws in local and regional water jurisdictions, top-notch scientists and proliferating businesses to implement Big Data technology for upgrading the systems of its €7 billion water management, while restricting the costs of preserving water by 15%.

Prophecies about Urban Centres
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Plummeting freshwater resources: a serious challenge faced by the global population is now at its apex. An overwhelming 89 percent of the world population thrives on enhanced water supply systems, which results in a loss of more than 32 billion cubic meters of fresh water, through physical leakage. Thereby, more than 50 percent of world population will be vulnerable in water-stressed regions by 2025. And by 2040, the figures will further push the energy demand by 56%, making US the second highest energy consumer across the globe.

Saving Water Globally

In the meantime, most of the world cities should re-invent and re-structure their assets to pull together advanced functions encompassing different complex systems and to associate with new powerful allies. Urbanization comes with its own costs. Day by day, these networks are growing more complicated and even more expensive. By delving deeper into the interconnections of systems, the societies will be in a better position to grasp how to run them more efficiently.

Water has never grabbed eyeballs, as it has today. Many countries are not at all prepared to manage such burgeoning complexities of water management. Besides, water management authorities are constantly under pressure to harness their power for flood protection and drinking water standards.

Reality Check: Water demand is set to rise by 30% by 2030. Ever increasing population and swelling urbanization are the reasons behind such calamitous figures.

Smart City Technology – The Key to Urban Sustainability

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New Jersey Institute of Technology (NJIT) revealed that by 2025 smart city technologies would multiply to an industry estimating $27.5 billion. Moreover, nearly 88 smart cities will develop by the end of 2025. Smart cities whirl around the concept of using improved, interconnecting technologies to make environment safe, lives easier and urban living cost-effective and more efficient.

Societies are enduring new weather extremes. It is the high time to use big data and analytical science to cure the growing complexities in managing our water systems. Smart technology is the only viable option that can take future generations towards a sustainable future.

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