A complete practicum report covering 8 data science tasks in R: dynamic multi-formula functions, nested sales simulation, performance categorization, multi-company HR dataset generation, Monte Carlo Pi estimation, data normalization & feature engineering, KPI dashboard, and automated report generation. Built with ggplot2, dplyr, and kableExtra. Institut Teknologi Sains Bandung — Week 5.

The data shows the total rate as well as rates based on sex, age, and race. Rates are also shown for three specific kinds of cancer: breast cancer, colorectal cancer, and lung cancer.​ This dataset comes from the CORGISD ( The collection of Really Great Interesting , Situated Data) where data is collected and posted onto this website. ​ https://corgis-edu.github.io/corgis/csv/cancer/​ AI WAS USED TO FIGURE OUT HOW TO RENDER AND CLEAN UP MY DATA SINCE THAT WAS THE ISSUE

This is an analysis of the NCBI study GSE244187, links in document, that covers a recent uterine fibroid study in high throughput sequencing of RNA of uterine fibroid in the myometrium, adjacent and at risk tissue of myometrium for fibroid development, and normal myometrium tissue not next to the fibroids of the uterus. There are 3 samples each of normal White female, normal Black female, at risk White female, at risk Black female, fibroid White female, and fibroid Black female. No machine learning done, just seeing if any of the fold change values of previous studies on EBV associated diseases, the last few studies' genes of a handful show changes. There is a difference and similarity. There could be an EBV connection. We will add these fibroid genes to our dataset and see if our large pathology database can predict fibromyalgia, Lyme disease, Hodgkins, Burkitts Lymphoma, Gastric carcinoma, nasopharyngeal carcinoma, HIV, normal, intraductal cholangiocarcinoma, and now uterine fibroid, when we get to it. still more genes of top genes and their respective pathology study selected to analyze before adding them to the database.