What does a Data Scientist actually do?

Today, the term modern data science has become a craze across all sectors and is poised to revolutionize the globe thanks to its ability to optimize Google search engines, LinkedIn recommendations as well as influence the headlines run by Buzzfeed editors.

From telecommunications, retail, health to agriculture, trucking and government systems, data science is certainly the future that is already here with us.

For most curious folks like myself, we are always interested in digging up more about anything that’s trending—and like many out there, I sort to really find out what data scientists actually do on a day to day basis.

Do they just build models all day long? Is the claim that they spend 75-85% of their time cleaning data really true? I bet at some point you’ve also wondered about this too.

But guess what, from my findings, the role of a data scientist could just be the sexiest job in the 21st century, but then, what is it about?

What do Data Scientists do?

While the term “data science” may not be as easy as ABCD to understand owing to the fact that it is commonly used to describe a broad range of actions closely related to data, this post will help you to understand what exactly it is that data scientists do.

Well, given that data science is what I do best while blogging/making noise about it on  Hilda-analyzes.com,

The many data scientists interviewed approached the question from different perspectives, with some describing a broad array of tasks including huge online experimental frameworks for new product developments on platforms such as Etsy and booking.com, techniques used by Buzzfeed to implement a multi-faceted bandit solution to optimize headlines as well as the impact of machine learning on business decisions.

While it is no brainer to understand how data science works at least for the tech industry, it’s worthwhile to note that data science can be used in different ways depending on not just the sector but on the specific business and its goals.

In the present day, data scientists lay a firm data foundation so as to deliver robust analytics, particularly in the tech industry. Furthermore, they rely on online experiments alongside other techniques to realize sustainable growth. They also develop machine learning pipelines and customized data products to better understand their customers and businesses at large in order to make better decisions. Ideally, data science is more about testing, infrastructure, data products and machine learning for efficient decision making.

In summary, a data scientist’s role is basically to analyze data for sensible insights.

Daily tasks of a data scientist 

Their specific tasks include:

  • Determine the correct data sets and variables
  • Identify problems with data-analytics that deliver the greatest opportunities to a business
  • Gather large sets of both structured and unstructured data from different sources
  • Validate and clean data to ensure accuracy, uniformity and completeness.
  • Develop and apply models and algorithms to mine stores of big data
  • Analyze data to ascertain trends and patterns
  • Interpret data to determine solutions and opportunities
  • Communicate findings to various stakeholders using visualization and  or other means

In conclusion

The authors of the book Doing Data Science describe the duties of data scientists in an interesting way that sums up this post:

“More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills—skills that are also necessary for understanding biases in the data, and for debugging logging output from code.

Once she gets the data into shape, a crucial part is exploratory data analysis, which combines visualization and data sense. She’ll find patterns, build models, and algorithms—some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications.”