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Data research analyst skills for your resume and career

Updated January 8, 2025

6 min read

Quoted experts

Stephen A. Matthews Ph.D.,

Pavel Chernyavskiy Ph.D.

Below we've compiled a list of the most critical data research analyst skills. We ranked the top skills for data research analysts based on the percentage of resumes they appeared on. For example, 10.4% of data research analyst resumes contained data analysis as a skill. Continue reading to find out what skills a data research analyst needs to be successful in the workplace.

15 data research analyst skills for your resume and career

1. Data Analysis

Here's how data research analysts use data analysis:

Created internal reports for projects and conducted data analysis and database programming using refined skills in statistics and database management.
Applied advanced statistical methods for longitudinal data analysis including generalized estimating equations and marginal structural models.

2. Data Collection

Data collection means to analyze and collect all the necessary information. It helps in carrying out research and in storing important and necessary information. The most important goal of data collection is to gather the information that is rich and accurate for statistical analysis.

Here's how data research analysts use data collection:

Research and evaluate the cost-effectiveness and efficiency of transferring data collection and storage systems from paper to computerized measures.
Developed end-user documentation and was liaison between administration and student teachers to ensure accurate data collection and reporting.

3. Patients

Here's how data research analysts use patients:

Researched medications used by patients ranging from legally, illegally, prescribed or non-prescribed, to pharmaceutical and non-pharmaceutical inhalants.
Interviewed postpartum seizure patients and performed surface physical exams on their offspring, translating clinical observations into rating scales.

4. Python

Python is a widely-known programming language. It is an object-oriented and all-purpose, coding language that can be used for software development as well as web development.

Here's how data research analysts use python:

Construct models utilizing supervised and unsupervised machine learning algorithms with Python.
Created scripts in Python for manipulating, parsing and converting data in excel sheets.

5. Research Projects

Here's how data research analysts use research projects:

Performed Internet market research; managed franchise data spreadsheet updates and exceeded weekly quotas regularly; performed additional research projects as requested
Evaluate and collect data for writing research projects and case studies for analysis/understanding information and distinguishing fact from opinion.

6. Data Management

The administrative process that involves collecting and keeping the data safely and cost-effectively is called data management. Data management is a growing field as companies rely on it to store their intangible assets securely to create value. Efficient data management helps a company use the data to make better business decisions.

Here's how data research analysts use data management:

Selected to support multiple grant-funded projects through effective GIS-based analysis, spatial data management, and cartographic production.
Monitor internal and external data management records and all key research documents.

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7. Stata

STATA is a statistical software package used for data visualization, manipulation, statistics, and automated reporting. Individuals with experience in using other types of statistical software or a background in data science may find it easier to absorb the concepts of STATA quickly.

Here's how data research analysts use stata:

Utilized variety of statistical computer programs like R, STATA etc.
Worked with data from various projects via SAS, SPSS, R, STATA, Win Bugs etc.

8. SAS

SAS stands for Statistical Analysis System which is a Statistical Software designed by SAS institute. This software enables users to perform advanced analytics and queries related to data analytics and predictive analysis. It can retrieve data from different sources and perform statistical analysis on it.

Here's how data research analysts use sas:

Analyzed large national longitudinal data set using SAS to assess the relationship between behavioral problems and obesity in children.
Utilize SAS, SQL, and occasionally R to conduct statistical analysis on a variety of institutional and academic related data.

9. Visualization

Here's how data research analysts use visualization:

Developed various statistical models in sandbox and integrated models in to Tableau to have better visualization.
Implemented data and analysis visualization using Java.

10. Statistical Analysis

Here's how data research analysts use statistical analysis:

Provided research and assemble target lists on potential customer with target market through statistical analysis and channel marketing.
Center for Population Economics: NIH-funded research with spatial vector and statistical analysis of veterans.

11. Analyze Data

Analyze data or data analysis refers to the practice of studying, organizing, and transforming data to make it more useful. It also includes the cleansing of non-useful information which helps in better decision making regarding any particular matter. Analyze data is a practice that is used widely in the field of business, social sciences, and science.

Here's how data research analysts use analyze data:

Analyze data and discussion of reading materials and experimental results.
Utilized Excel and online databases, including the Bureau of Labor Statistics (BLS), to process and analyze data.

12. Data Entry

Data entry means entering data into a company's system with the help of a keyboard. A person responsible for entering data may also be asked to verify the authenticity of the data being entered. A person doing data entry must pay great attention to tiny details.

Here's how data research analysts use data entry:

Conducted large amounts of data entry of sensitive customer information.
Managed daily office operations pertaining to performing Data Entry activities.

13. Healthcare

Healthcare is the maintenance or improvement of a person's health by the diagnosis and treatment of a person's injury, illness, or any other disease. Healthcare is a basic necessity of human life and is the responsibility of the country's government to ensure that each person gets healthcare. Providing healthcare is the job of certified health professionals that includes doctors, surgeons, nurses, and other physicians. Pharmaceutical companies, hospitals, dentistry, therapy, and health training all come under healthcare. Healthcare plays a vital role in the country's economy and its development.

Here's how data research analysts use healthcare:

Administered and maintained proprietary healthcare information in the company databases* Executed systematic presentation of data and research to internal and external users
Worked closely on product development projects to align current offerings post- Healthcare reform requirements.

14. Data Quality

Here's how data research analysts use data quality:

Participate n Data Quality Assurance Process following QA methodology.
Implement and monitor data quality assurance.

15. Statistical Software

Statistical software is a specialized computer program used for statistical analysis of data collection, organization, analysis, interpretation, and presentation to provide scientific information on patterns and trends.

Here's how data research analysts use statistical software:

Used various statistical software packages to summarize and interpret statistical results
Verified the 2000 and 2005 Ethiopia Demographic and Health Survey data using statistical software's.

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What skills help Data Research Analysts find jobs?

Tell us what job you are looking for, we’ll show you what skills employers want.

What skills stand out on data research analyst resumes?

Stephen A. Matthews Ph.D.

Liberal Arts Professor of Sociology, Anthropology, Demography and Geography Director, Graduate Program in Demography Faculty Director, Graduate Programs in Applied Demography Editor, Spatial Demography Associate Editor, Mathematical Population Studies, Pennsylvania State University

Our program is too broad and complex to answer this in any meaningful way (see my opening paragraph). I hope our Ph.D. graduates have both soft skills (e.g., people skills, communication (writing/speaking), team science/work skills, critical thinking skills) as well as the technical skills (e.g., data analysis, data visualization, data ethics, IRB experience, etc.). As mentioned, I also hope they are flexible and adaptive vis-a-vis other perspectives (interdisciplinary outlook).

What data research analyst skills would you recommend for someone trying to advance their career?

Pavel Chernyavskiy Ph.D.

Assistant Professor of Statistics, University of Wyoming

Statisticians with subject-matter expertise tend to be more in-demand, all else being equal. For example, quantitative biologists, statistical geneticists, bioinformaticians, industrial statisticians can all be thought of as "statisticians with subject-matter expertise," at least in my opinion. With that in mind, if an applicant can develop their knowledge of a particular area of application during their gap year, it should help them in the job market.

What type of skills will young data research analysts need?

Zhixin Wu Ph.D.

Associate Professor, DePauw University

Problem solving skills, analytical skills, self-learning ability, and good communication skills.

What soft skills should all data research analysts possess?

Dr. Anne Paulet Ph.D.

Professor, History Department, Humboldt State University

In terms of soft skills, those probably won't change much, they will simply be practiced differently. Being flexible is important since jobs may switch between home and office and since one may be dealing with someone else working from home and the challenges that can present-what cat owner hasn't had their cat walk in front of the camera or step on the wrong computer key? The ability to work in groups will continue to have importance as well as the ability to manage your own time and meet deadlines. At the same time, the nature of computer camera interaction means that people will have to learn to "read" others differently than they would in an in-person environment. Many recent articles have talked about how it is harder to read facial cues or detect emotional responses on the computer. Again, those presently taking synchronous classes have the opportunity to practice these skills--providing students turn on their cameras rather than relying only on audio. If the past year has demonstrated anything, it is that people need to be more culturally aware and sensitive and also be able to work with people of diverse backgrounds. History classes are a great way for students to better understand what others have gone through and how that might impact interaction today. Additionally, history classes-as well as college in general-should provide students with the skills to help create the kind of changes in institutions and companies that need to be made to make them more inclusive. Perhaps the greatest skill college students have is the ability to learn. I never intended to teach online, yet here I am doing just that. It required learning new ways to approach teaching, reconsideration of the ways students learned in the new environment, and figuring out new online programs to make all this happen. I was forced to do this as a result of the pandemic but most students will find that this sort of adjustment-whether foreseen or not-will be a regular part of their career path. The ability to learn these new skills, to apply new methods and to approach issues in new and innovative ways will help them stand out when it comes to looking for a job.

What hard/technical skills are most important for data research analysts?

Bertrand Clarke Ph.D.

Professor and Chair, University of Nebraska - Lincoln

Linear models and their follow-on techniques -- generalized linear models, mixed models. Familiarity with nonlinear methods, including classification. Basic nonparametric techniques like PCA, clustering, etc.

Expertise in programming in various languages (R, Python, SAS, etc.)
Expertise in working with various data structures and software.

There are specialized areas as well -- time series, spatial statistics, etc. But they often rely on the methods in the first paragraph.

Do you want to know more?

List of data research analyst skills to add to your resume

The most important skills for a data research analyst resume and required skills for a data research analyst to have include:

Data Analysis
Data Collection
Patients
Python
Research Projects
Data Management
Stata
SAS
Visualization
Statistical Analysis
Analyze Data
Data Entry
Healthcare
Data Quality
Statistical Software
SPSS

PowerPoint
Power Bi
Market Research
Data Analytics
Data Research
Research Findings
R
Regression
Java
Data Integrity
IRB
Quantitative Analysis
Statistical Techniques
Access Database
Windows
Literature Reviews

Data Issues
MATLAB
Macro
Extraction
Data Reports
Time Series Analysis
SQL Server
Unix
HIV
ETL
Data Warehouse
Bloomberg
Survey Data
GIS
Financial Data
Medicaid

Updated January 8, 2025

Zippia Team

Editorial Staff

The Zippia Research Team has spent countless hours reviewing resumes, job postings, and government data to determine what goes into getting a job in each phase of life. Professional writers and data scientists comprise the Zippia Research Team.

Updated January 8, 2025

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