Will “Data Analytics Specialist” be Automated?

Core Skills & Abilities

• Report results of statistical analyses in peer-reviewed papers and technical manuals.

• Develop software applications or programming for statistical modeling and graphic analysis.

• Develop and test experimental designs, sampling techniques, and analytical methods.

• Determine whether statistical methods are appropriate, based on user needs or research questions of interest.

• Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.

• Prepare and structure data warehouses for storing data.

• Analyze and interpret statistical data to identify significant differences in relationships among sources of information.

• Report results of statistical analyses, including information in the form of graphs, charts, and tables.

• Apply sampling techniques, or use complete enumeration bases to determine and define groups to be surveyed.

• Plan data collection methods for specific projects, and determine the types and sizes of sample groups to be used.

• Supervise and provide instructions for workers collecting and tabulating data.

• Identify relationships and trends in data, as well as any factors that could affect the results of research.

• Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.

• Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.

• Process large amounts of data for statistical modeling and graphic analysis, using computers.

• Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.

• Evaluate sources of information to determine any limitations, in terms of reliability or usability.

• Design research projects that apply valid scientific techniques, and use information obtained from baselines or historical data to structure uncompromised and efficient analyses.

• Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.