7 Questions with a Data Analytics Professor

Written by Coursera Staff • Updated on

University of Michigan Associate Professor Chris Brooks shares tips for pursuing a career in the rapidly transforming field of data and public policy analytics.

[Featured image] A portrait of University of Michigan Associate Professor Chris Brooks

Chris Brooks is an Associate Professor at the University of Michigan’s School of Information, an interdisciplinary school where students study topics like human-computer interaction (HCI), user experience (UX), and data science. He’s worked at the university for 10 years, specializing in data analytics. In particular, he teaches students how data can be used to inform public policy and positively impact the public sector.

In other words, he uses data to make society better.

“The most exciting and rewarding aspect of this work is that the people engaged in this field are positively impacting people’s lives. They are empowering the people through good governance backed with data-driven evidence,” Brooks says. “In my work at a public institution, my research, teaching, and service all aim toward creating a better future for the people of Michigan and beyond. This notion of working in service to others imbues life with a profound and beautiful purpose.”

Brooks arrived at this point in his career by following his curiosity, seeking to understand the reasons behind public policy decisions and how he could use traditional data analysis techniques to further instigate change. Here, he shares some of the wisdom he’s gained about pursuing a career in data and public policy analytics.

Embrace becoming an enthusiastic learner, delve deep into the 'why' of public policy and technology.

What are some of the main job tasks someone in this field should anticipate performing?

Individuals working in public sector data analysis can expect their main job tasks to include scrutinizing data critically, comprehending its source, and interpreting and communicating its meaning. They should also anticipate examining how varying approaches—from data collection through statistical analysis and visual representation—could alter our perception of the “truth” of the data. For instance, how you present data to decision-makers, where you tell a clear story and the implications of the findings, can be quite different from how you might present data to other analysts or technical experts, where you are critically questioning and exploring the data for nuance.

What type of education should those beginning a career in this field consider?

Individuals starting a career in public sector data analytics should consider educational initiatives that cultivate critical thinking and inquiry. With that foundation in place, adding technical skills is vital.

It's important to note that one doesn't necessarily need to have a background in computer science to succeed in data analytics. However, it is crucial to have the ability to accurately assess your own skill set, seek further training when needed, and examine critically the questions and data provided by others.

Placeholder

specialization

Data Analytics in the Public Sector with R

Using Data to Inform Public Policy. Learn data analytics skills using the R programming language and how to apply this work to positively impact the public sector.

4.8

(62 ratings)

6,456 already enrolled

Intermediate level

Average time: 2 month(s)

Learn at your own pace

Skills you'll build:

Public Policy, Data Analysis, Ggplot2, Data Visualization, Rstudio, Piblic Administration, R basics, Tydiverse, Custom R functions, Public Administration, R Programming, tidyverse, Politics and ethics, case study, Policy Analysis, Policy/Program evalaution

What tools should someone in this field plan to master in order to be successful in their career?

It is critical that students understand the R programming language and know how to use modern tools such as RStudio to help facilitate their data analysis and sharing of insights and recommendations. As they advance their careers, it could be important to add additional tools to their skill set, including the ability to use Python, STATA, Shiny, web programming, and more.

Keeping abreast of the rapidly changing fields of machine learning and artificial intelligence is key for most knowledge sector employees, and tools such as R allow one to utilize advances from those fields without getting bogged down in the details of how each method or algorithm works internally. 

What are some of this field’s most used skills?

The most useful skills in the field of data analytics in the public sector are technical skills like data analysis, computer programming and statistics, and non-technical skills like communication, critical thinking, and writing.

The overlap between these kinds of skills should not be underestimated! Data visualization, for instance, is often as much about computer programming and statistics as it is about communication and critical thinking. Having a good grounding in both technical and non-technical skills will allow public policy analysts to draw attention to their results in a more impactful way than just having technical skills.

Placeholder

specialization

Applied Data Science with Python

Gain new insights into your data . Learn to apply data science methods and techniques, and acquire analysis skills.

4.5

(26,165 ratings)

432,333 already enrolled

Intermediate level

Average time: 4 month(s)

Learn at your own pace

Skills you'll build:

Text Mining, Python Programming, Pandas, Matplotlib, Numpy, Data Cleansing, Machine Learning (ML) Algorithms, Machine Learning, Scikit-Learn, Graph Theory, Network Analysis, Social Network Analysis, Natural Language Toolkit (NLTK), Natural Language Processing, Data Virtualization, Data Visualization

What advice would you give to individuals seeking their first role in this field?

Individuals seeking their first role in this field must identify their capacity to handle the technical aspects of the job. Many organizations need someone who can effectively balance technical work with the critical evaluation of data and public communication. It's challenging to casually engage with the technical side; it demands consistent practice, hands-on experience, and a readiness to step outside of your comfort zone.

If you are considering one of these jack-of-all-trades roles, which are becoming more widespread as the demand dramatically increases across public sectors, it's important to seek the necessary training to build confidence in your abilities and confidently showcase this to prospective hiring managers.

What are some ways individuals can continue learning and growing in this field?

To continue learning and growing in the field of data analytics in the public sector, individuals need to commit time to stay updated with the technical trends. Don't passively wait for a course on the required topic to present itself, but proactively connect with communities of like-minded individuals, such as through meetups, on Reddit, or engaging existing communities and leaders on social media. As you start to familiarize yourself with the terminology they use, like trend analysis or multivariate analysis, seek out online learning opportunities across various platforms. In this interconnected world, the options for growth are more accessible than ever.

Placeholder

specialization

Python 3 Programming

Become a Fluent Python Programmer. Learn the fundamentals and become an independent programmer.

4.8

(17,338 ratings)

251,756 already enrolled

Beginner level

Average time: 3 month(s)

Learn at your own pace

Skills you'll build:

Json, Computer Programming, Python Programming, Sorting, Data Structure, Code Debugging, Data Evaluation, Code Testing, Data Collection, API Documentation

Anything else someone starting a career path in this field should know?

Those starting a career in public sector data analytics should know that we are in a period of dramatic job role transformation. One that is accelerating along with advancing technologies and social movements. Embrace becoming an enthusiastic learner, delve deep into the “why” of public policy and technology. This will guide you on an incredible path and help you stand out as a unique candidate committed to building a better future both for yourself and society.

Keep learning

Continue learning about data analytics in the public sector and beyond with the University of Michigan on Coursera. Learn directly from Brooks and his colleagues in the Data Analytics in the Public Sector with R Specialization, Applied Data Science with Python Specialization, Python 3 Programming Specialization, and Sports Performance Analytics Specialization.

Placeholder

specialization

Data Analytics in the Public Sector with R

Using Data to Inform Public Policy. Learn data analytics skills using the R programming language and how to apply this work to positively impact the public sector.

4.8

(62 ratings)

6,456 already enrolled

Intermediate level

Average time: 2 month(s)

Learn at your own pace

Skills you'll build:

Public Policy, Data Analysis, Ggplot2, Data Visualization, Rstudio, Piblic Administration, R basics, Tydiverse, Custom R functions, Public Administration, R Programming, tidyverse, Politics and ethics, case study, Policy Analysis, Policy/Program evalaution

Placeholder

specialization

Applied Data Science with Python

Gain new insights into your data . Learn to apply data science methods and techniques, and acquire analysis skills.

4.5

(26,165 ratings)

432,333 already enrolled

Intermediate level

Average time: 4 month(s)

Learn at your own pace

Skills you'll build:

Text Mining, Python Programming, Pandas, Matplotlib, Numpy, Data Cleansing, Machine Learning (ML) Algorithms, Machine Learning, Scikit-Learn, Graph Theory, Network Analysis, Social Network Analysis, Natural Language Toolkit (NLTK), Natural Language Processing, Data Virtualization, Data Visualization

Placeholder

specialization

Python 3 Programming

Become a Fluent Python Programmer. Learn the fundamentals and become an independent programmer.

4.8

(17,338 ratings)

251,756 already enrolled

Beginner level

Average time: 3 month(s)

Learn at your own pace

Skills you'll build:

Json, Computer Programming, Python Programming, Sorting, Data Structure, Code Debugging, Data Evaluation, Code Testing, Data Collection, API Documentation

Placeholder

specialization

Sports Performance Analytics

Predictive Sports Analytics with Real Sports Data. Anticipate player and team performance using sports analytics principles.

4.5

(234 ratings)

15,854 already enrolled

Intermediate level

Average time: 4 month(s)

Learn at your own pace

Skills you'll build:

Data Analysis, Python Programming, sports analytics

Updated on
Written by:
Coursera Staff

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.