Beginning February 7, 2023, Cornell’s Center for Advanced Computing and Weill Cornell Medicine Scientific Computing, ITS, and Clinical and Translational Science Center are launching the Winter and Spring Scientific Computing Training Series. The Zoom-based training is available for free to all workforce members and students of Cornell, WCM, WCM-Q, and Cornell Tech. Upcoming classes include Data Management in Science Research, R Basics, Python for Scientific Computing and Data Science, Python for Digital Humanities and Social Science, Creating the Best Visualizations for your Data, Revision Control with Git, Python for Data Visualization, Research Project Software Continuity, Working with Excel Files in Python and C#, Case Study - Scripting ImageJ and PowerPoint with Python, Using the Whole Processor, and Using Relational Databases for Research. For course descriptions, prerequisites, and Zoom links, visit the Scientific Computing Training Series page.

Upcoming Classes

Data Management in Science Research

Instructor: Adam Brazier Date: February 7, 2023, 9am-10am EST An overview of managing data workflows for scientific computing, starting with data collection and aggregation, through processing and storing in an accessible form. We will cover some issues relating to security policy, integration of Identity and Access Management and retention policy (but this is not a security policy workshop!), possible storage venues and formats, models for aggregating and distributing data such as the pub/sub model, and modes of data storage such as relational database, file system, cloud, noSQL, Data Lake, etc. Register via the Scientific Computing Training Series page.

R Basics

Instructor: Christopher Cameron Date: February 14, 2023, 9am-10am EST Learn to read R analysis scripts in this introduction to the R language. We will examine language fundamentals like built-in in data types, conditional execution, flow control, and indexing, then look at some basic data summary and modeling functions with an emphasis on how R is meant to be used. Register via the Scientific Computing Training Series page.

Python for Scientific Computing and Data Science

Instructor: Chris Myers Date: March 14, 2023, 9am-10am EST An examination of the core components of the Python software ecosystem for scientific computing and data science, with a particular focus on numpy, scipy, and pandas. This lecture will describe the overall design and structure of these packages and some of their components, complemented by code examples that demonstrate some of the key functionality. Also addressed will be issues of performance and the integration of these core packages in the larger Python ecosystem. Register via the Scientific Computing Training Series page.

Python for Digital Humanities and Social Science

Instructor: Christopher Cameron Date: March 21, 2023, 9am-10am EST Humans generate messy data. While statistics-focused environments like R and Stata are great for data analysis, these specialized tools can be difficult to use with data that defies tabular representation. Human data, like written language, social relationships, images, and social media content, require flexible tools that can handle complexity. In this talk, we will provide an overview of Python, highlight how this free and open-source programming language supports digital humanities and social science research, and discuss Cornell and web-based resources to help you get started using Python in your research. Register via the Scientific Computing Training Series page.

Creating the Best Visualizations for your Data

Instructor: Ben Trumbore Date: March 28, 2023, 9am-10am EST An introduction to choosing the best type of chart to use for the data you have and the message you want to convey. Includes a breakdown of the different types of data you might have and descriptions of the main types of 2D data visualization. Does not include instruction for any particular visualization tool. Register via the Scientific Computing Training Series page.

Revision Control with Git

Instructor: Steve Lantz Date: April 11, 2023, 9am-10am EST Git is a widely used tool for revision tracking and collaborative code development. The talk introduces Git and how to use it effectively in conjunction with a repository hosting service like GitHub. Register via the Scientific Computing Training Series page.

Python for Data Visualization

Instructor: Chris Myers Date: April 25, 2023, 9am-10am EST An examination of some of the Python packages that support data visualization for various use cases, providing both a general discussion of capabilities and multiple code examples demonstrating specific functionality. This lecture will address the generation of both static images suitable for inclusion in publications and presentations, and interactive data visualizations useful for exploring complex datasets and steering computations. Packages examined include matplotlib, pandas, seaborn, plotnine, bokeh, plotly, and possibly others. Register via the Scientific Computing Training Series page.

Research Project Software Continuity

Instructor: Adam Brazier Date: May 2, 2023, 9am-10am EST While producing long-lasting software in academic research domains shares many of the same problems as commercial development, the environment is often different. In particular, the number of coders is often smaller, the people writing code may be learning as they go, development of software is often not their main career goal, and the funding model is different. This means that industry approaches to producing, maintaining, and operating software may not apply, or may have to be modified for the research environment. In this talk we will see some ideas, based on experience of research software at a variety of scales, to suit the different situations in which researchers develop software. Register via the Scientific Computing Training Series page.

Working with Excel Files in Python and C#

Instructor: Ben Trumbore Date: May 9, 2023, 9am-10am EST An introduction to working with Excel spreadsheets from within computer programs and scripts. Python and C# examples will be given for reading Excel files and accessing their contents, as well as populating, formatting, and writing new Excel files. Register via the Scientific Computing Training Series page.

Case Study - Scripting ImageJ and PowerPoint with Python

Instructor: Christopher Cameron Date: May 23, 2023, 9am-10am EST Do you have a workflow with elements that can be automated? Sometimes the hardest part is knowing what might be possible. This case study involves using Python to process multichannel confocal microscopy images with ImageJ and then organize the output into PowerPoint slides. Register via the Scientific Computing Training Series page.

Using the Whole Processor

Instructor: Steve Lantz Date: June 6, 2023, 9am-10am EST Parallel processing is no longer just a concern for supercomputers--these days, it takes place in nearly all computing devices down to laptops and cell phones. This presentation describes parallel computing capabilities that are found within single processors and how applications can access them through techniques such as multithreading and vectorization. Register via the Scientific Computing Training Series page.

Using Relational Databases for Research

Instructor: Adam Brazier Date: June 20, 2023, 9am-10am EST An introduction to the use of relational (SQL) databases, with a brief overview of database structure then covering SQL queries, some information on best practices, and development tools. We will mostly deal with ANSI SQL which will run on most Relational Database Management Systems (RDBMs), noting some important inter-RDBMS differences. Covered will be SQL queries for data retrieval, insertion and deletion, correlated subqueries and how to construct a complicated query. We will also discuss the interface between the database and the code, including the use of Object-Relational Model tools and stored procedures. Register via the Scientific Computing Training Series page.