Table of Contents
Why do you want to pursue a career in data science?
Data Science gives meaning to raw data and converts it into meaningful insights that can be used to grow the business and recognize market trends. With so less supply of specialized Data Scientists and a rapid demand, Data Science has become a lucrative career.
Is data science a meaningful career?
Yes, data science is a very good career with tremendous opportunities for advancement in the future. Already, demand is high, salaries are competitive, and the perks are numerous – which is why Data Scientist has been called “the most promising career” by LinkedIn and the “best job in America” by Glassdoor.
Is data science a good career for the future?
For four years in a row, data scientist has been named the number one job in the U.S. by Glassdoor. What’s more, the U.S. Bureau of Labor Statistics reports that the demand for data science skills will drive a 27.9 percent rise in employment in the field through 2026.
Why do we expect so much from data scientists?
There are multiple reasons for this and they can vary from one data scientist to another. The level of experience also plays a part in this expectation chasm. Let’s take the example of aspiring data scientists. They are typically self-learned and have gathered their knowledge from books and online courses.
Are data scientists quitting or changing jobs?
But despite all of these positive trends, there is an underlying feeling of discomfort. Data scientists are quitting or changing their jobs at a rapid pace. Why is this happening?
What are the most common issues in data science?
1. Expectation vs. Reality – Here Lies A Wide, Wide Gap! This is one of the most prevalent issues in the data science field. There is an ever-widening gap between what data scientists expect and what they actually work on in the industry. There are multiple reasons for this and they can vary from one data scientist to another.
Who should be in charge of data science decision-making?
Most executives in charge of data science decision-making are neither educated nor trained in actual data science theory and techniques. Instead, they have relied upon non-data-driven, plug-and-play features that can be launched in a timely manner. Few teams have a Head of Data, Data Science Manager or other relevant role.