Whether you want to learn a new skill for a probable career change, or hone an existing skill for better job opportunities, there’s always a battle between what to choose – a bootcamp or an online course. If you too are facing the same dilemma, we would suggest you to opt for bootcamps (like Data Science, Machine Learning etc). Wondering why? Here are the top four benefits bootcamps have over online courses:
Data Science Blog
The increasing buzz around data science has already attracted a lot of computing enthusiasts and the demand for data science professionals seems to go up with each passing day. In addition, the gap between the availability of skilled professionals and the demand is quite substantial, which makes it even more of an exciting career path.
The Glassdoor Report last year (Glassdoor’s 50 Best Jobs In America For 2018) named data scientist as the best job in the US for three years running. The report took into consideration three key factors, namely job satisfaction rating, median annual base salary, and the number of job openings. Each of these three factors was given equal importance, and it was found that data science jobs excelled across all three.
Data science has emerged as an extremely lucrative career choice where big companies pay their data scientists top dollars. As experts are calling this domain the one with hot jobs for the future, more and more people are rushing to learn ways that would help them crack their data science interviews.
During the past few years, we’ve been experiencing an upward trend in talent acquisition in the field of machine learning. Though this field has traditionally been considered as something that only institutions working with huge amount of resources could utilize, wide implementation of machine learning today has transformed the scenario completely. From e-commerce to software product to different business landscapes – machine learning is being implemented to a great extent. As a result, there’s a huge demand of machine learning professionals across industries, throughout the globe.
In the past few years, the field of data science has grown exponentially. In today’s information-driven world, data is playing a crucial role in every industry – from cybersecurity, healthcare, online retail, banking and insurance, to digital marketing, SEO and several others. No wonder why businesses have started relying on data heavily. And this triggers a boom in diverse job openings related to data science. Among all these positions, perhaps the most overlapping two are that of a data scientist and a data analyst. There’re many who get confused between these two titles and some of them even think that data scientist is just another glammed up word for data analyst.
We already know that data science is one of the most trending buzzwords in today’s tech world, with an exceptional potential of opportunities for aspirants. If you belong to this league and are planning to pursue a career in this field, being familiar with the fundamental concepts is of utmost importance. You mayn’t need a Ph.D. to excel in data science, but you’ve to have a solid understanding of the basic algorithms.
Do you want to know what the sexiest job of the 21st century is? According to HBR, it’s a data scientist. Although the term has recently become a huge buzzword in the industry, the field of data science isn’t new. Lots of data scientists have already been working in different organizations for quite some time now. The goal of making computers as intelligent as human beings has also been pursued for a significant amount of time.
In today’s technology-driven world, businesses have access to a huge amount of data that can be leveraged to an enormous extent. With the emergence of data, there comes a dire need of professionals who’re able to mine that data and draw valuable insights from it. In every aspect of data, there’s a growing demand for those who truly understand what can actually be done with a huge amount of data. Yes, we’re talking about data scientists here – the buzzword in today’s technology-driven landscape.