Data scientists are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. The data scientist role is becoming increasingly important as businesses rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies.
A data scientist’s main objective is to organize and analyze large amounts of data, often using software specifically designed for the task. The final results of a data scientist’s analysis needs to be easy enough for all invested stakeholders to understand — especially those working outside of IT.
A data scientist’s approach to data analysis depends on their industry and the specific needs of the business or department they are working for. Before a data scientist can find meaning in structured or unstructured data, business leaders and department managers must communicate what they’re looking for. As such, a data scientist must have enough business domain expertise to translate company or departmental goals into data-based deliverables such as prediction engines, pattern detection analysis, optimization algorithms, and the like.
A data scientist’s chief responsibility is data analysis, a process that begins with data collection and ends with business decisions made on the basis of the data scientist’s final data analytics results.
The data that data scientists analyze, often called big data, draws from a number of sources. There are two types of data that fall under the umbrella of big data: structured data and unstructured data. Structured data is organized, typically by categories that make it easy for a computer to sort, read and organize automatically. This includes data collected by services, products and electronic devices, but rarely data collected from human input. Website traffic data, sales figures, bank accounts or GPS coordinates collected by your smartphone — these are structured forms of data.
Unstructured data, the fastest growing form of big data, is more likely to come from human input — customer reviews, emails, videos, social media posts, etc. This data is typically more difficult to sort through and less efficient to manage with technology. Because it isn’t streamlined, unstructured data can require a big investment to manage. Businesses typically rely on keywords to make sense of unstructured data as a way to pull out relevant data using searchable terms.
Programming: Chen cites this as the “most fundamental of a data scientist’s skill set,” noting it adds value to data science skills. Programming improves your statistics skills, helps you “analyze large datasets” and gives you the ability to create your own tools.
Quantitative analysis: An important skill for analyzing large datasets, Chen says quantitative analysis will improve your ability to run experimental analysis, scale your data strategy and help you implement machine learning.
Product intuition: Understanding products will help you perform quantitative analysis, says Chen. It will also help you predict system behavior, establish metrics and improve debugging skills.
Communication: Possibly the most important soft skills across every industry, strong communication skills will help you “leverage all of the previous skills listed,” says Chen.
Teamwork: Much like communication, teamwork is vital to a successful data science career. It requires being selfless, embracing feedback and sharing your knowledge with your team, says Chen.