People from outside the data science field are beginning to heed the appeal of the popularity of data science. As you explore different data science jobs and career paths, it is easy to learn more about the skills and requirements of each position but not so easy to figure out what data scientists actually do. There are often different tasks required of the same data science job, depending on industry, title, and level of responsibility. The following article offers an overview of what a Data Scientist is and what the average Data Scientist actually does.
What is a Data Scientist?
Much like the Data Science industry, the role of a Data Scientist is an amalgam of multiple skills and titles focused on garnering insights from information and data. Part statistician and part analyst, Data Scientists use programming languages, machine learning, engineering, and other tools to collect, organize, analyze, and visualize troves of data. And while there is a similarity between Data Analysts and Data Scientists, the data science industry is known for requiring more advanced technical skills. These skills are necessary primarily because Data Science focuses on making meaning out of “big data, ” which includes understanding how to develop data science projects and navigate complex database management systems.
The “Data Scientist” title is a general one. The role and responsibilities of a Data Scientist with a software engineering background working for a science and technology company are significantly different from those of a Data Scientist analyzing web traffic for an advertising and marketing company. In addition, there are many data science jobs that don’t fall under the title “Data Scientist” at all. It is essential to consider all these different factors when scrutinizing what a Data Scientist actually does.
Roles and Responsibilities of Data Scientists
Despite the different titles and expectations of a Data Scientist across industries, a few key roles and responsibilities are relevant across companies and positions. Data scientists are expected to have an understanding of the data science lifecycle, knowledge of how to start and finish data science projects, awareness of the process of storing and retrieving data that is held in a database, as well as turning data into information through analysis, visualization, and storytelling.
Data Analytics and Organization
One of a Data Scientist’s most important responsibilities is using data analysis and organization to glean insights from a data collection. Data analysis extracts meaning from data, while data organization focuses on cleaning and preparing data for analysis. Data analysis can take different forms. Some data scientists rely on business intelligence tools and platforms to generate predictive analytics about a dataset. In contrast, others use programming languages like Python to perform statistical analyses on a dataset. Another option is to use more traditional data analytics software, like Microsoft Excel, to organize data. Transitioning from the role of Data Analyst to Data Scientist requires expanding one's skills beyond organizing and analyzing a dataset.
Automation and Machine Learning
One of the methods separating data science from more traditional forms of data analysis and organization is the incorporation of automation and machine learning. By mobilizing algorithms and artificial intelligence in their work, data scientists can use statistical models and theories to streamline data analysis and cleaning. Whether through data analytics technologies or the development of unique programs, machine learning models are the foundation of modern-day data science, with many uses in the field. Data scientists commonly develop, deploy, and evaluate machine learning models which can be automated for unsupervised learning, meaning they continue gathering insights and looking for patterns without human intervention.
Data Visualization and Storytelling
Developing machine learning models and analyzing data also requires data scientists to understand data visualization and storytelling. Once a dataset is analyzed, or a machine learning model is used to clean the data or generate results, the next step is sharing that information with others using graphs, charts, images, reports, or digital dashboards. Data scientists are responsible for communicating their findings to clients, team members, or even a field or industry through data visualization. And while many data scientists have an in-depth knowledge of the nuances of graphs, charts, and other visualizations, not all audience members are trained in data literacy. Therefore, data scientists working within a company or industry that is not STEM-focused should also be able to use storytelling elements such as rhetoric and design principles, to present their findings to key stakeholders.
Database Design and Management
Finally, most companies or contracts that require a Data Scientist have access to a trove of data or require a large amount of data to be collected and stored. Many institutions have access to database administrators and managers who handle data collection, storage, and accessibility. So, database design and management is another essential skill for data scientists. Since many data scientists will have to access a database for retrieving datasets, knowledge of the SQL programming language and real-world experience with databases is typically required for a Data Scientist or team.
Day in the Life of a Data Scientist
A day in the life of a Data Scientist is primarily project-based. It often involves using data science tools to work collaboratively with a team, including individuals from within and outside of the field. Regardless of company or industry, most data scientists spend their day using data to solve problems by developing and managing short and long-term projects. Some of these projects are research-based and focus on providing insights into a problem that affects a larger population, while others use historical data to find a business solution to a problem within a company.
Fields like marketing and engineering also use project-based work for creating specific products or strategies to improve a brand or service and learn more about an audience. Data Scientists must have a firm understanding of project management to budget their time and resources as well as assign tasks on a day-to-day basis.
Interested in Building a Career As a Data Scientist?
Noble Desktop’s data science classes offer a variety of courses and certificate programs for beginning a career in the data science industry. Training in the Data Science Certificate course focuses on beginner-level programming languages and database management. The Data Analytics Certificate course covers prescriptive and predictive analytics for aspiring data analysts. The Python for Data Science Bootcamp includes training in automation and machine learning for data science professionals to build a career that is responsive to industry trends.