You are invited to SSDA training workshop series I.1 Introduction to Python, if you would like to learn Python from the very beginning; If you might be interested in a more advanced Python workshop during the summer; if you would like to get started with programming in a more supportive group environment; if you never learned any programming language before, but would like to give it a try. Please come and join us on both days or the day works best for you!
SSDA is delighted to sponsor the 2019 Women in Data Science East Lansing Symposium at Michigan State University! Connect. Discover. Inspire.
You are invited to SSDA training workshop series II.1 Introduction to R, if you would like to learn R from the very beginning; If you might be interested in a more advanced R workshop during the summer; if you would like to get started with programming in a more supportive group environment; if you never learned any programming language before, but would like to give it a try. Please come and join us! If you can not make it or would like to study at your own pace, please register at SSDA online workshops instead.
SSDA is excited to announce its special workshop: Random Forests on Spatial Data. While the course will highlight some special aspects of spatial data, anyone with an interest in gaining introductory experience about machine learning and classification will benefit from this course. Learn some basics about working with spatial data, data classification approaches including kmeans and classification trees, and of course random forests, and the standard approach to machine learning through model training and validation. This will be a hands-on workshop with code you can apply to your own projects.
If you already know the very basics of Python, and you know how to work with the basic Python variables, lists, functions and packages, such as Numpy, you are more than welcome to join us on May 29, 2019 (Wednesday) 9:30 A.M. to 12:30 A.M., to explore plotting in Python using Matplotlib, working with Pandas (a powerful tabular data module), and having fun with a case study.
We invite all MSU faculty, staff, and students to join us this summer to have fun @ SSDA Data Visualization Challenge! Please pre-register for this event and have digital files of your best story-telling research graphs with descriptions submitted to ssda@msu.edu by Friday, July 26, 2019. Each participant can submit no more than three graphs, which should be original work created and owned by the participant. Online voting will be open to the public from July 29 (Monday) to August 2 (Friday), which will also be judged by 5 faculty from SSDA, CMSE, CBSA to select top 3 finalists for prize awards. Please contact ssda@msu.edu with any questions or comments. We look forward to seeing your work!
SSDA is excited to announce another special intermediate level workshop: Data Visualization in R, which will be led by Yingjie Li and Veronica Frans. This workshop will highlight (1) how to tidy raw data, such as time series data, geo-spatial data, etc., (2) how to choose the appropriate type of graphic to report your findings, and (3) how to make publication quality figures using popular packages, such as ggplot2, ggmap and sf. This will be a hands-on workshop with code can be easily modified and applied to your own projects.
This SSDA Brownbag features some exploratory work using dimensionality analysis on multivariate neuroimaging data. Basics of the approach will be introduced, along with results. Discussion and feedback is strongly encouraged from those working on large multivariate data in different thematic contexts.
After we rolled out the SSDA-Student Engagement Program late June this year, we have received encouraging positive feedbacks from both faculty/staff and students, and good number of member registrations. We thus host this member only event to welcome our SSDA members/SSDA associates, and invite an open discussion to form a SSDA student chapter to serve students who are interested in data science in the MSU community better.
If you would like to learn Python from the very beginning, you are invited to join us on September 9, 2019 (Monday) 9:30 A.M. - 12:30 A.M. to explore Python lists, functions, packages, DataFrame, and even plotting in Python using Matplotlib.
If you would like to learn Python from the very beginning, but is not available on September 9 (Monday) to attend the first Python introductory training workshop, then you are invited to join us on September 26, 2019 (Thursday) 9:30 A.M. - 12:30 A.M. to explore Python lists, functions, packages, DataFrame, and even plotting in Python using Matplotlib.
This workshop will focus on the collection and storage of Twitter data. It will cover basic social media mining, including managing ‘big data’ and issues related to empirical analysis. We will work directly with the Twitter API and collect/clean/visualize/analyze tweets during the workshop. No prior experience/knowledge is necessary beyond being comfortable with basic R programming.
Come and have fun with drawing your own jack-o’-lantern using the Turtle graphics package in Python programming language. NO CODING EXPERIENCE NEEDED. With Python Turtle Graphics, kids can easily learn and write their first Python code. It’s Halloween, of course, so candy will be provided together with fun Python programing activities!
If you would like to learn Python from the very beginning, you are invited to join us on November 11, 2019 (Monday) 9:30 A.M. - 12:30 A.M. to explore Python lists, functions, packages, DataFrame, and even plotting in Python using Matplotlib. This would be the last introductory Python workshop that SSDA will offer in this academic year. We will have introductory R workshops in the next semester. If you are interested to get started with Python, don't miss this one. Please pre-register by November 8, 2019 (Friday) for this free SSDA training workshop. Please bring your own laptop.
By incorporating fixed and random effects, multilevel (or mixed-effects) modeling allows researchers to statistically account for dependency in the data due to a nested data structure, that is, a higher level unit (e.g., an individual) contributing multiple observations (e.g., data points from repeated measures) to the data set. In this workshop, we will go through some of the basics in multilevel modeling, covering both conceptual and practical aspects of modeling building. Participants are expected to have basic knowledge of multiple regression and feel comfortable using RStudio (scripts will be provided). Open data from the speaker’s own research will be used.