Most Important Skills for a Data Analytics and Reporting Manager at University of San Diego
For a Data Analytics and Reporting Manager at the University of San Diego, Christian emphasizes that "hard skills" like data analytics using R and Python, data cleaning, and asking the right questions with the data are crucial, but equally important are "soft skills" such as communication for disseminating statistical findings to a non-expert audience and critical thinking to understand the data's origins and validity.
Data Analysis, Communication Skills, Critical Thinking, Statistical Interpretation, Data Questioning
Advizer Information
Name
Job Title
Company
Undergrad
Grad Programs
Majors
Industries
Job Functions
Traits
Christian Guerra
Data Analytics and Reporting Manager
University of San Diego
Cal State University Los Angeles
UC Riverside - PhD
Anthropology, Sociology, Criminal Justice
Education
Data and Analytics
Transfer Student, First Generation College Student
Video Highlights
1. Hard skills such as data analytics, R, and Python are crucial for data manipulation and analysis.
2. Effective communication is essential for explaining statistical findings to individuals without a statistical background.
3. Critical thinking skills, including questioning data sources and survey methodologies, are vital for data analysis.
Transcript
What skills are most important for a job like yours?
Some of the hard skills include data analytics, which involves using R or Python, cleaning data, knowing how to merge, and asking the right questions with the data. You need to know exactly what you're going to do with the data.
For soft skills, I would recommend communication. Many people have trouble with statistics, so you need to find a good way to disseminate statistical findings. You need to be able to explain your findings to everyday people who might not have expertise in statistics.
This includes the way you convey messages and your wording. Other critical thinking skills are super important. You also need to ask the right questions about the data: where it comes from, who requested it, if it came from a survey, how many people answered the survey, and how the survey was administered.