A data scientist collects, analyzes, and interprets big data to identify patterns and insights, make predictions, and create actionable plans. Big data can be defined as data sets that are more diverse, larger, and faster than earlier data management methods were suited to handle. Data scientists work with many types of big data, including:

Structured data, which is usually organized into rows and columns and includes words and numbers, such as names, dates, and credit card data. For example, a data scientist in the utility industry might analyze tables of electricity generation and usage data to reduce costs and identify patterns that could lead to equipment failures.
Unstructured data that is unorganized and includes text in document files, social media and mobile data, Web site content and videos. For example, a retail data scientist might answer a question about improving customer service by analyzing unstructured call center notes, emails, surveys and social media posts.
In addition, data set characteristics can be described as quantitative, structured numerical data or qualitative or categorical data that are not represented by numerical values and can be grouped based on category. It is important for data scientists to know the type of data they work with because it directly affects the type of analysis they perform and the types of graphs they can use to visualize the data.

To gain knowledge of all of these types of data, data scientists use their skills in the following areas:

Computer programming. Data scientists write queries using languages such as Julia, R, or Python to extract data from their company’s database. Python is the language of choice for many data scientists because it is easy to learn and use, even for people with no programming experience, and it offers ready-to-use data processing modules for data analysis.
Math, Statistics, and Probability. Data scientists use these skills to analyze data, test hypotheses, and create machine learning models – files that data scientists train to recognize certain types of patterns. Data scientists use trained machine learning models to discover relationships in data, make predictions about data, and find solutions to problems. Rather than creating and training models from scratch, data scientists can also take advantage of machine learning to access production-ready machine learning models.
Subject matter expertise. To translate data into relevant and meaningful insights that impact business outcomes, data scientists also need subject matter expertise – an understanding of the industry and the company in which they work. Here are a few examples of how data scientists can apply their subject matter knowledge to solve industry problems.