Data Science Tutors (DSTs) are R&ID Research Tutors who specialize in data science. During their shifts, they can assist students with understanding and interpreting data, providing peer research assistance, and instructing them in the use of data analysis tools. We have four Data Science Tutors in Fall '23: Andrew Gou, Brian Hu, Adam Koplik, Cynthia Yang, and Via Zhou. See their friendly faces here!
You can contact them in the following ways during their shifts:
Our DSTs are available on the following days during the academic semester for drop-in consultations.
|Tae Hyun Lim (he/him)
Data Science/Analysis Research Librarian
Burke Library 354 (3rd Floor):
|Adam Koplik (he/him)||
|Andrew Gou (he/him)||
|Brian Hu (he/him)||
|Cynthia Yang (she/her)||
|Via Zhou (she/her)||
The Data Science Tutors specialize in different data analysis tools, complementing each other's skill sets. See their specialties below:
Questions? Send us an email at email@example.com.
1. Define Your Research Question
State your research question without describing the sources or data. This will help you identify a variable or variables (underlined below).
Q1. Which characteristics of voters explain their vote choices in the 2020 presidential election?
Q2. Do free trade agreements (FTAs) promote the members' bilateral international trade?
2. Define Your Measurements
Find a specific language that best describes your concepts. This will help you find the data set that best captures your interest.
Q1, characteristics of voters:
Q2, countries' bilateral international trade
3. Identify Population, Unit of Analysis, and Unit of Observation
Who are you interested in studying? Who or what is being described by your variable(s)? What is the unit in your data set? This will also help you choose a data set.
(Micro-level) <-----Individuals, households, cities, states/provinces, countries -----> (Macro-level)
Q1: Those who are eligible to vote in the 2020 presidential election, at the individual-level
Q2: Country dyads in the world, at the country-level
4. Identify Time Frame and Frequency
At what point in time do you want to know this about the people, institutions, or products you identified? How often do you want to know about them?
Q1: The data are collected in 2020 (before the election), and are not repeated over time.
Q2: As many years as possible, repeated every year
5. Identify the Group Structure of Your Data
Are you looking for data collected at regular intervals over time? Identifying what sort of time series may be helpful as you search for data.
6. Think about Your Data Analysis Methods and Tools
Don't know which data analysis method to choose? The model choice depends on your variable types and your data structure. Use this flowchart and the UCLA IDRE's guide to find the method for your analysis and its example code.
Also, choose a data analysis tool. For example, for quantitative analyses, researchers often use R, SPSS, Stata, Python, MATLAB, or Mathematica. For qualitative analyses, NVivo and MAXQDA are often used. All of these programs are available to Hamilton students, faculty, and staff free of charge.
We also have online training resources for these programs. Contact AskUs (firstname.lastname@example.org) if you have any questions about the resources.
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