Data Scientist

Summary of What they do:

Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Visualize, interpret, and report data findings. May create dynamic data reports.

What Data Scientists Do

Data scientists use analytical tools and techniques to extract meaningful insights from data.

Duties

To present their findings, these scientists often make use of data visualization.

Data scientists typically do the following:

• Determine which data are available and useful for the project
• Collect, categorize, and analyze data
• Create, validate, test, and update algorithms and models
• Use data visualization software to present findings
• Make business recommendations to stakeholders based on data analysis

Data scientists often begin a project by gathering or identifying relevant data sources, such as surveys. They may use a variety of methods to obtain data, including through access to other organizations’ databases or by using web-scraping tools (software that extracts specific information from websites). They may start with large, unstructured datasets, commonly referred to as raw data. To properly analyze the data, these scientists must “clean” the raw data, a process by which they structure the data to make them readable by software programs.

Data scientists develop algorithms (sets of instructions that tell computers what to do) and models to support programs for machine learning. They use machine learning to classify or categorize data or to make predictions related to the models. Scientists also must test the algorithms and models for accuracy, including for updates with newly collected data.

Data scientists often use data visualization software to present their findings as charts, maps, and other graphics. Visualization techniques allow data scientists to clearly communicate their analyses to technical and nontechnical audiences, including colleagues, managers, and clients. Ensuring that audiences understand the information helps data scientists make recommendations for business decisions or process changes based on the results of their analysis.

Some data scientists choose to focus on a particular area of work. For example, data scientists who have a strong coding or engineering background may develop or recommend systems, build machine learning algorithms, and devise ways to enhance web-browsing functions. Others conduct research for reports or academic journals. Still others focus on improving business strategy for activities such as marketing, sales, and user engagement.

Important Qualities

Analytical skills. Data scientists must be adept at researching and at examining and interpreting findings.

Computer skills. Data scientists must be able to write code, analyze data, develop or improve algorithms, and use data visualization tools.

Communication skills. Data scientists must be able to convey the results of their analysis to technical and nontechnical audiences to make business recommendations.

Logical-thinking skills. Data scientists must understand and be able to design and develop statistical models and to analyze data.

Math skills. Data scientists use statistical methods to collect and organize data.

Problem-solving skills. Data scientists must devise solutions to the problems they encounter in data collection and cleaning and in developing statistical models and algorithms.

Tasks On The Job

1. Analyze, manipulate, or process large sets of data using statistical software.
2. Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
3. Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
4. Clean and manipulate raw data using statistical software.
5. Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
6. Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
7. Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.
8. Design surveys, opinion polls, or other instruments to collect data.
9. Identify business problems or management objectives that can be addressed through data analysis.
10. Identify relationships and trends or any factors that could affect the results of research.
11. Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.
12. Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.
13. Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.
14. Recommend data-driven solutions to key stakeholders.
15. Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.
16. Write new functions or applications in programming languages to conduct analyses.