So, you think you can be a data scientist. But, are you sure you have it what it takes to excel in the data science field? It's a very complicated field, and getting competitive day by day. In this post, we will go through what the industry demands of a modern data scientist in the real world, how to become a data scientist, top platforms and resources to learn the data science skills, and career advice & job search tips from data science experts. The data scientist job is definitely one of the most lucrative and hyped job roles out there. More and more businesses are becoming data-driven, the world is increasingly becoming more connected and looks like every business will need a data science practice. So, the demand for data scientists is huge. Even better, everyone acknowledges the shortfall of talent in the industry. But, becoming a data scientist is extremely complicated and competitive. The career path of a data scientist is not going to be easy.
So, you want to go for the "Sexiest Job of the 21st Century"? You should get started with LinkedIn. It's not only a great place to network and find your next career opportunity. LinkedIn is also a great site for learning and staying updated with the latest tools and industry trends. In order to build a great Data Science LinkedIn feed follow these top Data Science Experts on LinkedIn.
So you've decided data science is the field for you. More and more businesses are becoming data driven, the world is increasingly becoming more connected and looks like every business will need a data science practice. So, the demand for data scientists is huge. Even better, everyone acknowledges the shortfall of talent in the industry. However, becoming a data scientist does not come easy. It needs a mix of problem solving, structured thinking, coding and various technical skills among others to be truly successful. If you are from a non-technical and non-mathematical background, there's a good chance a lot of your learning happens through books and video courses. Most of these resources don't teach you what the industry is looking for in a data scientist.
In 2017 Big Data gave way to AI at center stage of the technology hype cycle. The practice of data science and machine learning capabilities are increasingly being adopted across a wide range of industries and applications. The challenges in data analytics are now being addressed by machine learning. Machine learning, AI, and Predictive Analytics have been the top buzzwords in 2017. We witnessed a growth in value-producing innovations around data this year.