Data Peer Consulting

Data science support for all Berkeley students, staff, and faculty 

Drop-in Office Hours 

Consultation Appointment 

Join our virtual drop-in office hours and speak to a live consultant any time on Mondays-Fridays from 11am-4pm. 

Click here button

See the calendar below to find which consultants are available during specific office hours. 

Consider scheduling an appointment if:

  • you are not able to attend any of our drop-in office hours

  • you would like to meet with a specific consultant 

  • you think your question is more involved and would like the consultant to do some preparation in advance

To request a consultation appointment, please submit an intake form.

For all other inquiries, please email us at ds-peer-consulting@berkeley.edu 

Connect with a Data Peer Consultant

Learn about each consultant's expertise areas and availability.

Anderson

Languages: Python, R, SQL, Java, C, Tableau, MATLAB, iPython (Jupyter notebooks), LaTex, Git, Excel

Packages: Pandas, MatplotLib, Seaborn, NumPy, datascience (Data 8), BeautifulSoup (or other webscraping), ggplot (R)

Fundamentals: Probability Theory, Data Cleaning/ Manipulation, Webscraping, AWS

Barry

Languages: Python, SQL, Java, iPython (Jupyter notebooks), LaTex, Git, STATA

Packages: Pandas, MatplotLib, Seaborn, NumPy, BeautifulSoup (or other webscraping)

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Probability Theory, Data Cleaning/ Manipulation, Machine Learning, Linear Algebra, Webscraping

Carlos

Languages: Python, SQL, Java, Front End (HTML, CSS, JS and JS frameworks), iPython (Jupyter notebooks), LaTex, Git, Excel

Packages: Pandas, MatplotLib, Seaborn, NumPy, SciPy, datascience (Data 8), PlotLy, BeautifulSoup (or other webscraping), GeoPandas

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Data Cleaning/ Manipulation, Machine Learning, Linear Algebra, Webscraping, Databases

Daniel

Languages: Python, R, SQL, Java, Tableau, LaTex, Git

Packages: Pandas, MatplotLib, Seaborn, NumPy, SciPy, datascience (Data 8), ggplot (R)

Fundamentals: Probability Theory, Linear Algebra, Databases

Gary

Languages: Python, R, SQL, Java, MATLAB, Front End (HTML, CSS, JS and JS frameworks), iPython (Jupyter notebooks), LaTex, Git, Excel, C++

Packages: Pandas, MatplotLib, Seaborn, NumPy, SciPy, datascience (Data 8), PlotLy, BeautifulSoup (or other webscraping), ggplot (R)

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Probability Theory, Data Cleaning/ Manipulation, Machine Learning, Linear Algebra, Webscraping, Databases, Google Cloud Platform

Jillian

Languages: Python, SQL, Java, iPython (Jupyter notebooks), LaTex, Git, Excel

Packages: Pandas, MatplotLib, Seaborn, NumPy, SciPy, datascience (Data 8), PlotLy, Tensorflow

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Probability Theory, Data Cleaning/ Manipulation, Machine Learning, Linear Algebra, Databases

John

Languages: Python, SQL, Java, Front End (HTML, CSS, JS and JS frameworks), iPython (Jupyter notebooks), Git

Packages: Pandas, MatplotLib, NumPy, datascience (Data 8)

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Probability Theory, Data Cleaning/ Manipulation, AWS

Nayan

Languages: Python, SQL, Java, iPython (Jupyter notebooks), Git, Excel

Packages: Pandas, MatplotLib, Seaborn, NumPy, datascience (Data 8), PlotLy, BeautifulSoup (or other webscraping)

Fundamentals: Probability Theory, Data Cleaning/ Manipulation, Machine Learning, Webscraping, Databases

Richa

Languages: Python, R, SQL, Java, MATLAB, Front End (HTML, CSS, JS and JS frameworks), iPython (Jupyter notebooks), Git, Excel

Packages: Pandas, MatplotLib, Seaborn, NumPy, ggplot (R)

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Probability Theory, Data Cleaning/ Manipulation, Machine Learning, Linear Algebra

Samantha

Languages: Python, R, SQL, Java, Tableau, Front End (HTML, CSS, JS and JS frameworks), iPython (Jupyter notebooks), Git, Excel

Packages: Pandas, MatplotLib, Seaborn, NumPy, datascience (Data 8), ggplot (R)

Fundamentals: Data Cleaning/ Manipulation, Machine Learning, Webscraping, AWS

Sammy

Languages: Python, SQL, Tableau, iPython (Jupyter notebooks), LaTex, Git, Excel

Packages: Pandas, MatplotLib, Seaborn, NumPy, datascience (Data 8)

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Data Cleaning/ Manipulation, Linear Algebra, Databases

Simran

Languages: Python, SQL, Java, MATLAB, iPython (Jupyter notebooks), LaTex, Excel

Packages: Pandas, MatplotLib, Seaborn, NumPy, SciPy, datascience (Data 8), PlotLy, BeautifulSoup (or other webscraping)

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Probability Theory, Data Cleaning/ Manipulation, Linear Algebra, Databases

Spencer

Languages: Python, SQL, Java, iPython (Jupyter notebooks), Git

Packages: Pandas, MatplotLib, Seaborn, NumPy, SciPy, datascience (Data 8), PlotLy, Keras, GeoPandas

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Probability Theory, Data Cleaning/ Manipulation, Machine Learning, Linear Algebra, AWS

Tianjia (Teresa)

Languages: Python, R, SQL, MATLAB, iPython (Jupyter notebooks), LaTex, STATA, Excel

Packages: Pandas, MatplotLib, NumPy, Keras, Tensorflow

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Machine Learning, Linear Algebra, Databases

Vincent

Languages: Python, R, SQL, Java, Tableau, iPython (Jupyter notebooks), LaTex, Git, STATA, Excel

Packages: Pandas, MatplotLib, Seaborn, NumPy, SciPy, datascience (Data 8), ggplot (R)

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Probability Theory, Data Cleaning/ Manipulation, Machine Learning, Linear Algebra, Databases

Yash

Languages: Python, SQL, Java

Packages: Pandas, MatplotLib, NumPy

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Probability Theory, Data Cleaning/ Manipulation

Yuchen (Adrian)

Languages: Python, R, SQL, Java, C, Git

Packages: Pandas, MatplotLib, Seaborn, NumPy, SciPy, Keras, Tensorflow, ggplot (R)

Fundamentals: Statistical Testing (Hypothesis Testing, A/B Testing, T-Tests, etc.), Probability Theory, Data Cleaning/ Manipulation, Machine Learning, Linear Algebra

About Us

Students in our data consulting network help make data science accessible across the broader campus community.

Launched in Fall 2017, these services are provided as a collaboration between:

Starting in Fall 2019, this project has also been brought to you by the Student Tech Fund. 

Contact

Email 

ds-peer-consulting@berkeley.edu

Data Peer Consulting FAQ

How do I get help from Peer Consultants?

We're available to help you via live drop-in office hours M-F 11am-4pm and/or via appointment consultations.

If you would like to meet with a consultant, you can find the zoom link for the live office hours at the top of our webpage. If you would like to set up an appointment, or give us more information before you drop in to office hours at a later time, please fill out this intake form so that we can learn more about what you need help with.

I can’t make it to any of the drop-in hours, would it be possible to set up a consultation appointment?

Yes. Peer Consultants can also consult on an appointment basis if you're not able to make their drop-in hours. Please request a consultation appointment by submitting an intake form and we will get back to you as soon as we can. Email us at ds-peer-consulting@berkeley.edu with any other inquiries.

My question is kind of specific, would it be possible to set up a consultation appointment?

Yes. If you have a question or topic that you would like to get help on that you believe would require a Peer Consultant's preparation in advance prior to you dropping in, please submit an intake form or send us an email at ds-peer-consulting@berkeley.edu with any other inquiries.

How are data peer consultants trained?

  • All Data Peer Consultants receive training on consulting best practices, triage processes, and more (in collaboration with our partners at the D-Lab, Research IT, and UC Berkeley Library) at the beginning of each semester.
  • All Data Peer Consultants in their first semester participate in the DATA 198 Data Science Instructional Support Seminar course that runs every Fall & Spring.
  • Data Peer Consultants also receive mentorship throughout the semester from our partners' graduate consultants and staff consultants. 

How are peer consultants selected?

Undergraduate UC Berkeley students apply through an application and must go through an interview process. We accept applications to join the team every semester, please check the Data Science Education Program's Student Opportunities Page to learn more.