Hands-on Computer Science lessons with practical problem solving

Student and Tutor had an in-depth lesson on Microsoft Outlook, covering various functionalities for email management and communication. The Student practiced creating email signatures and contact lists, and learned about search features, email categorization, calendar event scheduling, and automatic replies. They also discussed recalling messages and were advised to practice the learned features before the next session, which will focus on Excel.

The class focused on advanced Excel financial functions, specifically reviewing the Future Value (FV) formula, introducing the Internal Rate of Return (IRR) for investment analysis, and covering common Excel error types with methods to resolve them using `IFERROR` and `ISERROR`. The Student also practiced several key Excel shortcuts for navigation and formatting. For homework, the Student was assigned to practice the learned Excel shortcuts.

The Student and Tutor focused on mastering several key Excel functions. They practiced financial calculations using PMT (for loan payments) and FV (for future savings), as well as implementing logical IF conditions for grading and data categorization. The session also covered VLOOKUP for data retrieval, with the Student actively applying these formulas. The plan for the next session is to cover Outlook, and the Tutor will share the current spreadsheet for review.

The Student and Tutor reviewed the VLOOKUP function and then delved into Excel's financial functions, including PMT for calculating loan payments, FV for determining future investment values, and NPV for evaluating business investments. The Student practiced applying these formulas to various real-world scenarios. Homework was assigned to further practice the FV and NPV functions by adjusting key parameters.

The Student and Tutor worked through Homework Six in R, focusing on data manipulation and statistical analysis. They imported and concatenated SAS datasets, applied complex inclusion/exclusion criteria to filter data, and created various categorical variables. The session culminated in generating a detailed "Table 1" for analysis, and the Student was advised to save the script.

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.