Data Scientists: Who are they?

Data Scientists are the trendiest positions in the world. It may not sound like the “best job of the year” or the “sexiest job of the 21st century” , but those already in the field know it to be true.

A quick google search will tell you a data scientist is someone employed with the purpose of analyzing and interpreting complex data to help make business decisions. Data scientists will use statistics, programming, machine learning, and other techniques to draw actionable insights from data.

Long story short, data scientists answer high level business questions with data.

Not everyone is a data scientist, but data scientists can where many different organizational hats. Master’s in Data Science say it best, “On any given day, a data scientist is a mathematician, a statistician, a computer programmer and an analyst equipped with a diverse and wide-ranging skill set, balancing knowledge in different computer programming languages with advanced experience in data mining and visualization.”

Below is a list of responsibilities (provided by Data Science Dojo and Master’s in Data Science) you can expect for a data scientist role:

  • Conduct undirected research and frame open-ended industry questions
  • Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling
  • Thoroughly clean and prune data to discard irrelevant information
  • Explore and examine data from a variety of angles to determine hidden weaknesses, trends and/or opportunities
  • Devise data-driven solutions to the most pressing challenges
  • Communicate predictions and findings to management and IT departments through effective data visualizations and reports
  • Recommend cost-effective changes to existing procedures and strategies
  • Implement machine learning algorithms for a variety of problems using structured and unstructured data sources
  • Configure end-to-end components of a big data pipeline for various data science problems
  • Present findings of analyses and explain the models to clients and class participants
  • Create automations to increase efficiency

You may have noticed this list doesn’t only share technical responsibilities. Being in a business setting, data scientists must also be experts in certain soft skills, with communication probably being the most important. In a business setting, you will often have to communicate your results to people with little to no technical background. Something that seems obvious to you may sound like solving the theory of relativity to someone else. If you can effectively communicate your results, you will be an asset to any team.

One more thing worth mentioning is not all these responsibilities belong solely to data scientists. Data scientists act like the data jack-of-all-trades of data professionals. Most of the responsibilities are those from other data related roles, like analysts and engineers. Data Analysts specialize in visualizing and presenting outcomes; Data Engineers are great at the tasks you don’t see, like building pipelines or data management systems. When put together, these roles evolve into a Data Scientist.

Data Scientist Salaries

Alright, now that you have an understanding for what a data scientist is and does, how much do these individuals get paid?

According to Glassdoor, data scientists earn a base pay of $116,840 a year, on average. Here’s how much they rake in, on average, at some of the hottest tech companies, according to Glassdoor employee salary reviews

  • Facebook: $133,841
  • Apple: $149,963
  • Airbnb: $117,229
  • Twitter: $134,861
  • Microsoft: $119,129
  • LinkedIn: $138,798

Now the question changes from “how much do data scientists make?” to “how do I make as much as a data scientist?”.

There are a few answers to that, but none of them are easy. You can attend an in-person data science bootcamp, a university, or an online training. In-person and online trainings can last anywhere from 5-days to 3-6 months, with university programs lasting up to a few years. Either way, I strongly believe attending one training, no matter the length, will not make you a data scientist, but they can give you the foundation necessary to continue growing.

One example is Data Science Dojo (DSD). It teaches a 5-day, in-person data science bootcamp available around the world. The company was founded on the principle that everyone can benefit from an understanding of data science. DSD doesn’t promise to make you a data scientist, but it will enable you to extract actionable insights from data and give you a platform to jumpstart your data science journey.

In Summary

Data scientists are trending up, and there’s no visible end in sight. Some of the largest tech companies are paying top dollar for people who can clean, explore, visualize, and communicate insights. Data Scientists wear many organization hats, but the path isn’t easy. It takes years of training, practice, and experience to reach data science stardom. Are you ready to take on the challenge?

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