Hiring Companies

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66% OFF OFFER Dell Ms116 275-BBCB Optical  w ired Mouse at just 219/-              Data science consulting companies are a hot choice if you’re looking for a job in the field. They offer numerous development opportunities, access to the latest technologies, and provide data-based solutions for top-notch companies across the globe. Furthermore, on top of generous salaries, they seem to have tons of cool perks – from unlimited vacation days and free meals to hair salons and masseuses on site. This doesn’t make your choice any simpler, though. With so many industries and companies out there, it’s hard to keep track of who-offers-what-and-where. So, watch this video to find out which companies provide the best overall employee experience in 2020! Freshers for Data Science Roles:   Mu Sigma, Fractal Analytics, Exponentia, Clover Infotech,...
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how to became data scientist

Introduction:
I am pretty sure that many of us come across the article from the Harvard Business Review back in 2012. A data scientist is a professional known as the sexiest job of the 21st century. Also, research conducted by McKinsey Global Institute back in 2013 projected that there will be approximately 425,000 and 475,000 unfilled data analytics’ positions in North America by 2018. The take-home message here is that there will be a constant stream of analytic talent will be required in all industries, where companies collect and use data for their competitive advantages.
What exactly a data scientist?
In an over-simplified description, a data scientist is a professional who can work with a large amount of data and extract analytical insights. They communicate their findings to the stakeholders (i.e., senior leadership, management, and clients). Thus, companies can benefit from making the best-informed decisions to drive their business growth and profitability (i.e., depends on the context of industries).
Why is it so hard to become a data scientist?
The nature of data science is a hybrid of many disciplines. Where it composed of different subject areas like math (i.e., statistics, calculus, etc.), database management, data visualization, programming/software engineering, domain knowledge, etc. In my opinion, this may be the primary reason why people interested in jumping into the entry-level data science career often feel completely lost. Most people don’t know where to start because you may lack in one area completely or multiple areas depend on one’s educational background and work experience.
However, the good news is that you don’t need to worry too much about it. These days, we face completely opposite side of an issue. There are simply too many resources out there to pick. So, you don’t necessarily know which one might work out best for you. In this article, I will be focused on how to become a data scientist from three perspectives.
Follow the tutorial from the end 
1.python 2.jupyter Notebook 3.pandas 4.matplotlib

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