Since the term was coined in 2008, data science has exploded in business environments. While data scientist has been called the sexiest job of the 21st century, critics argue that data science is a buzzword without a clear definition and has simply replaced “business analytics”—which actually replaced “data mining” as the term of choice for describing data analysis.
Prominent statistician and editor-in-chief of FiveThirtyEight.com Nate Silver says, “I think data-scientist is a sexed up term for a statistician…. Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician.”
Whatever the case, there’s no question that tech companies the world over are snatching up scarce data scientists. Check out the percentage growth from indeed.com over the past five years.
What is data science?
Data science is an interdisciplinary field surrounding systems and processes to extract knowledge or insights from data in numerous forms. There are several types of data scientist:
- Data Businesspeople: Profit-focused leaders, managers, and entrepreneurs—only with a strong technical side.
- Data Creatives: Jack-of-all-trades types, able to use a broad range of tools and data to excel at data visualization.
- Data Developers: Programmers used to working with big data to focus on writing software to complete analytic, statistical, and machine-learning tasks.
- Data Researchers: Big brains, usually with PhDs, able to apply the tools and techniques learned in school to organizational data to provide valuable insights.
How much do data scientists make?
Data science may be the new hot Silicon Valley term for a reason. In today’s big-data crazy world, data scientists may be the most coveted workers in the IT industry. According to Glassdoor.com, the average salary for data scientists across the nation is around $119k with entry-level positions averaging roughly $60k.
What skills do you need to become a data scientist?
The skillset of a data scientist includes
- knowledge of programming CS fundamentals;
- statistics and machine-learning optimization skills;
- communication skills (storytelling);
- knowledge of big data and cloud computing;
- business domain knowledge; and
- insightful data visualization capability.
How Python can help you get your foot in the door
We won’t sit here and act like the majority of data scientists out there aren’t highly educated (88 percent have a Master’s degree and 46 percent have PhDs). While having a strong analytical background is certainly a plus, Python can help you get your foot in the door to many businesses with data science openings. Python is intuitive and easy to learn, and its ecosystem has grown dramatically in recent years, making it one of the most capable statistical analysis languages available. Python has also become increasingly popular for data science in the past few years. Companies worldwide are using Python for data science to harvest insights from their data and gain a competitive edge.
So do I need a Master’s degree or what?
To land the lead data science roles at Facebook and Twitter, yes—you’ll need more than just Python under your belt. However, Python has also become increasingly popular for data science in the past few years. Companies worldwide are constantly hiring for various data science positions, most of which require a general knowledge of Python to harvest insights from data.
Dave Holtz, data scientist with Airbnb, describes his job search this way: “You don’t need a comprehensive mastery of a number of fields to break into data science—such as software development, data munging, databases, statistics, machine learning, and data visualization. Instead, learn to read data science job descriptions closely. This will enable you to apply to jobs for which you already have necessary skills, or develop specific data skill sets to match the jobs you want.”