Open Sources
Embedded Files
Useful R
Useful R
Ggplot2 is the one of the best library for data visualization in R. The ggplot2 library implements a “grammar of graphics” (Wilkinson, 2005).
Read this R documentation to know about ggplot2 functions, click here: https://bit.ly/2DBo0VK
Shiny: When you want to share your content with others and make it simpler for them to comprehend and explore, you can utilize shiny. It's the best friend of a Data Scientist. Shiny facilitates the creation of interactive web applications. You can host standalone applications on a website, embed them in R Markdown documents, and construct dashboards. Shiny applications can also be extended with CSS themes, htmlwidgets, and JavaScript actions. http://rstudio.github.io/shinydashboard/
Lubridate: This library serves its purpose admirably. It is primarily used for data manipulation. It simplifies dealing with date-time in R. This library allows you to accomplish everything you've ever wanted to do with date arithmetic, albeit understanding and applying available features can be a little tricky. When examining time series data and want to aggregate the data by month, use floor date from the lubridate package; it will accomplish the job quickly. It serves a variety of purposes. The documentation is available here: https://bit.ly/2AbTEpf
Useful Python
Pandas: Pandas, one of the most widely used Python packages, is largely used for data analysis. It gives you some of the most helpful tools for exploring, cleaning, and analyzing your data. Pandas allows you to load, prepare, modify, and analyze any type of structured data. Machine learning packages also use Pandas DataFrames as input.
Scikit-learn: Scikit-learn is perhaps Python's most essential machine learning library. Scikit-learn is used to build machine learning models after cleaning and processing your data with Pandas or NumPy. It provides a plethora of tools for predictive modeling and analysis. There are numerous reasons to employ scikit-learn. To name a few, scikit-learn may be used to create supervised and unsupervised machine learning models, cross-validate model correctness, and perform feature importance analysis.
Useful Data Science
Privacy Policy for the Student Investment Class - University of Tulsa
Last Updated: 11/16/2023
Introduction: This Privacy Policy outlines how the Student Investment Class at the University of Tulsa, facilitated by Dr. K and the digital assistant Tally, collects, uses, and protects any information provided by the students during their use of the class resources and tools.
What Information We Collect:
Personal Identification Information (name, email address, student ID, etc.)
Academic Performance Data (grades, assignment submissions, quiz results)
Usage Data (interaction with Tally, access times to class materials, etc.)
How We Collect Information: Information is collected through:
Direct submissions by students (assignment uploads, email communication, etc.)
Automated means (usage data and interaction logs with Tally)
Purpose of Data Collection: The data collected is used for:
Academic support and progress monitoring
Enhancing the functionality and user experience of Tally
Communication regarding class updates and requirements
Data Sharing and Disclosure:
No personal data will be shared with third parties without explicit consent, except as required by law.
Aggregated and anonymized data may be used for educational research or class improvement purposes.
Data Security: We are committed to ensuring that your information is secure. We have implemented suitable physical, electronic, and managerial procedures to safeguard and secure the information we collect.
Your Rights: As a user, you have the right to:
Access the personal information we hold about you
Request correction of any inaccuracies in your personal data
Opt-out of certain data collection practices
Changes to This Policy: We may update this Privacy Policy from time to time. We will notify you of any changes by posting the new Privacy Policy on this page.
Contact Information: For any questions or concerns about this Privacy Policy, please contact kazim-topuz@utulsa.edu
Acknowledgment and Consent: By using the resources and tools provided in the Student Investment Class, you acknowledge that you have read and understood this Privacy Policy and agree to its terms.
Page updated
Google Sites
Report abuse