Data scientists are interdisciplinary experts who use a range of skills to synthesize, process, and interpret large volumes of structured and unstructured data. They can create visual data models to help identify patterns, trends, and anomalies. This is helpful for academic researchers and engineers, but it also helps inform day-to-day business decisions. As big data becomes a more integral part of innovation, data scientists are playing a more integral role in core business intelligence processes.
What does a data scientist do?
In a business setting, a data scientist is responsible for making raw data useful in a variety of strategy and planning decisions. They prepare visualized data and analysis for other departments. For example, a budget meeting may rely on a data scientist to provide a risk assessment or a projection of consumer habits based on historical data. Data scientists may specialize in a particular industry or area of data science, such as machine learning, artificial intelligence, or research and development.
What skills are needed to be a data scientist?
First and foremost, data scientists must excel across math, statistics, and computer science studies. They must be fluent in programming languages and compilers for database applications, such as Python, Java, C, and Ruby. Data scientists must also have an understanding of software development, data mining, and statistical analysis techniques. In addition to the technical skills required to be effective in their jobs, data scientists also need excellent communication skills. Most professionals do not have an in-depth understanding of big data, so data scientists are responsible for clearly articulating what the data means.
What tools does a data scientist use?
Data scientists use database management and visualization systems to control their data. These include Tableau, Microsoft PowerBI, and Qlik. Data scientists also frequently use cloud computing tools like Amazon Web Services and Microsoft Azure. Sometimes data scientists also use simpler spreadsheet tools like Microsoft Excel or Google Sheets when working with small amounts of data, though the capacity and capabilities are much more limited than a full database management system. For unstructured and hierarchical data, data scientists use tools like NoSQL databases to create dynamic schema.
Data scientist vs. data analyst
Data scientists and data analysts play similar roles, depending on the company. Data scientists are usually more assertive and inquisitive with the data they use, whereas data analysts are usually tasked with finding a solution to someone else’s problem. Data scientists also typically have a broader range of responsibilities, including aggregating data, creating algorithms, and building predictive models. This means data scientists often find more data to contextualize business decisions. In comparison, data analysts are primarily focused on drawing conclusions from data that has already been collected.