COMPUTING BEST PRACTICES
Do you want to discuss a new data technique or troubleshoot issues in your code with a computational expert?
From conceptualization to implementation, our computational consultants can provide guidance, expertise, and best practices for many aspects of research computing including:
- Applying emerging technologies for research computing
- Habits for reproducible research
- Improving code speed, efficiency, and robustness to crashes
- Collecting and curating data
- Transforming large scale and difficult to process data
Do you need to run Python Selenium or Beautiful Soup to harvest web data on KLC? If so, we provide a primer on how to setup your code here.
COLLECT DATA
Do you want to build a novel dataset by downloading and parsing lots of web pages?
Kellogg computational consultants can help you determine a game plan for gathering your web or text data effectively and ethically. We provide consultations and assistance with:
- Understanding when it is ok or not ok to gather data through certain methods
- Using GET methods (like curl or wget), Beautiful Soup, or Selenium to harvest web data
- Helping setup your data collection workflow on KLC
TRANSFORM DATA
Do you have inconvenient data that needs serious transformation before you can use it?
Kellogg computational consultants can show you options and help you develop a game plan act to extract variables of interest and reshape your data, efficiently and accurately. They are experts in techniques such as:
- Rules-based regex parsing
- Entity recognition packages (like spaCy)
- Fuzzy matching algorithms
- Optical character recognition
- Crowdsourcing and microtasking
IMPLEMENT ADVANCED MODELS
Do you have a new statistical model or machine learning technique you would like assistance with?
As you choose which models to run for your project, Kellogg computational consultants can with implementing unfamiliar techniques. They are experts in techniques like:
- Advanced statistical modeling
- Machine Learning
- Emerging coud based services
- GIS Mapping techniques in R and Python