https://public.tableau.com/app/profile/michael.m1522/viz/IS362Week1/Sheet3?publish=yes
Understanding challenges in Python data structures, zip and comprehensions using Python Jupyter Notebook. See Week2.ipynb
Summarize the bike ride from NY to Florida, by miles per day, using Numpy and Pandas
Project for analysis of Flights delays with SQL and Pandas
Assignment for the airports and weather data tidying.
Project 2: Tidy CSV files (preprocess) for analysis. I used the files from the following github: https://github.com/kwstat/untidydata2/blob/main/inst/messydata/combined_sales.xlsx
Conduct analysis of a survey conducted for 6 movies from 2024. Compare standardized and normalized statistics for this information.
Your task in this week’s assignment is to load a dataset, perform some minor cleanup and transformation tasks, then use exploratory data analysis to learn about the distribution of variables and the relationship between variables. Here is a link to the “Auto MPG” dataset in the UC Irvine data repository: https://archive.ics.uci.edu/ml/datasets/Auto+MPG
Project 3. Working with SQLite connect to Jupyter Notebook with SQLalchemy to use the Chinook database.
NYTimes API for Books called in Jupyter notebook and transformed in Python Data Frame.
Recommendation for a table of movies.Tmbd_5000 CSV file was used to create the recommendations.
Data analytics for CISA KEV and NIST checklist file. The importance of Vulnerability registries and analytics in cybersecurity reporting.
CISA Known Exploits Vulnerability presents an important step towards the analytics of cybersecurity analytics. A standardized model with consistent language and policy helps define a profession for data analytics professionals to follow.
NIST National Checklist program lists out the checklists useful for resolving vendor cybersecurity issues. This provides a consistent method for documenting registries of checklists. It is important when reviewing hundreds of vendor softwares and hardwares and having a process to follow.
Vulnerability registries can be seen like disease registries. Its useful to have public information. Because vendor information is private, government agencies can establish policies for data collection and processes that enable data analytics professionals to deliver high quality services.