Hands-on Computer Science lessons with practical problem solving

Student and Tutor discussed techniques for improving data search sensitivity using partial word matching and identified additional terms to enhance medication flagging. They also addressed how to avoid false positives in data extraction and practiced calculating means and standard deviations for patient data, with specific instructions for rounding results. The session concluded with the student working on applying these statistical calculations.

The Student and Tutor worked through a Data Science assignment in R Studio, focusing on importing a delimited text file and performing initial data exploration. They calculated descriptive statistics and created multiple indicator variables by identifying specific text patterns within the dataset, summarizing the counts and percentages for each. The session concluded with a plan to continue the assignment at a later time.

The Tutor and Student explored various statistical functions in Excel, including COUNT, SUM, AVERAGE, MEDIAN, MODE, MAX, MIN, STDEV.P, VAR.P, COUNTIF, SUMIF, LARGE, PERCENTILE, and IQR. The Student practiced applying these functions to a dataset of scores. For homework, the Student was assigned to practice these functions and the previously discussed VLOOKUP, with the next session focusing on financial functions.

The Tutor and Student worked through an R programming assignment focused on data manipulation and analysis. They practiced importing data, cleaning and preparing it by filtering and converting variable types, and calculating various statistics like age, incidence rates, and readmissions. The session concluded with preparing R scripts for submission.

Student and Tutor reviewed how to set up tables in Google Sheets and then focused on applying Excel functions. They practiced creating nested IF functions for assigning grades and determining pass/fail statuses. The main academic content covered was the VLOOKUP function, including its parameters, exact match functionality, and error handling with IFERROR. The Student was assigned homework to practice VLOOKUP by calculating employee bonuses based on performance ratings.

The Student and Tutor worked through a public health data analysis assignment in R, focusing on two main scenarios involving disease prevalence and BRFSS data replication. They covered various R programming tasks including data loading, manipulation, statistical calculations like cumulative sums and moving averages, data visualization, and verifying results against expected outcomes. The session successfully completed all coding requirements for the assignment, and future sessions are planned.