It’s the question that should be on every aspiring data scientist’s mind: “should I become a data scientist?” This question addresses the why before you try to answer the how..What is it about the field that draws you in and will keep you in it and excited for years to come?In order to answer this question, it’s important to understand how we got here and where we are headed..Because by having a full picture of the data science landscape, you can determine whether data science makes sense for you.Where it all started…Before the convergence of computer science, data technology, visualization, mathematics, and statistics into what we call data science today, these fields existed in siloes — independently laying the groundwork for the tools and products we are now able to develop, things like: Oculus, Google Home, Amazon Alexa, self-driving cars, recommendation engines, etc.The foundational ideas have been around for decades….early scientists dating back to the pre-1800s, coming from wide range of backgrounds, worked on developing our first computers, calculus, probability theory, and algorithms like: CNNs, reinforcement learning, least squares regression..With the explosion in data and computational power, we are able to resurrect these decade old ideas and apply them to real-world problems.In 2009 and 2012, articles were published by McKinsey and the Harvard Business Review, hyping up the role of the data scientist, showing how they were revolutionizing the way businesses are operating and how they would be critical to future business success..They not only saw the advantage of a data-driven approach, but also the importance of utilizing predictive analytics into the future in order to remain competitive and relevant..Around the same time in 2011, Andrew Ng came out with a free online course on machine learning, and the curse of AI FOMO (fear of missing out) kicked in.Where we are now…Companies began the search for highly skilled individuals to help them collect, store, visualize and make sense of all their data..“You want the title and the high pay?.You got it!.Just please come and come quick.” With very little knowledge of what they were looking for, job postings went up.If you searched ZipRecuiter today, you’d find over 190k open data science positions currently open, each one looking for their own data unicorn..Thus, in an effort to get talent in the door, the definition of what it meant to be a data scientist soon widened with definitions varying from company to company and person to person.On the other hand, candidates saw a great opportunity: a career with high pay, high demand, and the promise of job security and glory..Everyone rushed to develop all the right skills with one goal in mind: to hold the “sexist job of the 21st century”.We have the demand and we have the supply, so what’s the problem?. More details
- 7 Data Trends for 2020 (and one non-trend)
- What are Autoencoders? Learn How to Enhance a Blurred Image using an Autoencoder!
- Introducing Databricks Ingest: Easy and Efficient Data Ingestion from Different Sources into Delta Lake
- New Data Ingestion Network for Databricks: The Partner Ecosystem for Applications, Database, and Big Data Integrations into Delta Lake