Data Science

In Silico Approach to Mining Viral Sequences from Bulk RNA-Seq Data

October 28, 2025
Carly Karrick
by Carly Karrick. Viruses play important roles in evolution and influence ecosystems and host health. However, isolating and studying them can be difficult. In lieu of using resource-intensive methods to concentrate viruses into a “virome,” bulk sequencing methods include data from all biological entities present in a sample. In this tutorial, we explore an approach to mine viral sequences from publicly available bulk RNA-Seq data. The output from this analysis paves the way for future statistical analyses comparing viral communities in different contexts. This approach can be applied to other datasets, including studies of human health.

A brief primer on Hidden Markov Models

April 25, 2022
Amy Van Scoyoc
by Amy Van Scoyoc. For many data science problems, there is a need to estimate unknown information from a sequence of observed events. There are many ways to tackle these types of sequential input problems. In the data science world, there is a tendency to use machine learning approaches to search for relations in the dataset. But in many cases, we don’t have enough data or the sequences are too long to train RNNs effectively. In such cases, simpler is better. Enter the Hidden Markov Model.

How to Get Involved in Computing Research as a Undergrad at UC Berkeley

October 15, 2025
Abby O'Neill
by Abby O'Neill. Are you an undergrad interested in getting involved in CS/DS research? This blog post gives some advice for navigating the Berkeley research landscape. It includes mentions of structured programs like DARE, URAP, and Data Science Discovery, as well as cold emailing strategies and using office hours effectively. The main takeaway: Know your why, don't filter yourself out, and focus on finding people and projects that align with your goals.

Python Introduction to Machine Learning: Parts 1-2

October 21, 2021, 1:00pm
This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.

R Fundamentals: Parts 1-4

October 25, 2021, 9:00am
This workshop is a four-part introductory series that will teach you R from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the open-sourced R Studio software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.

Python Data Wrangling and Manipulation with Pandas

October 19, 2021, 10:00am
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

Python Fundamentals: Parts 1-4

August 19, 2021, 1:00pm
This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Python Fundamentals: Parts 1-4

October 4, 2021, 12:00pm
This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Python Introduction to Machine Learning: Parts 1-2

September 27, 2021, 2:00pm
This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.

Python Fundamentals: Parts 1-4

October 26, 2021, 2:30pm
This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.