Challenge No. 130: Consider a warehouse operating with an initial value of 0 and following a First-In, First-Out (FIFO) approach.
Solved using:Excel (FILTER, TAKE), Google Sheets, Power Query (Table.SelectRows), Python, Python in Excel, and R.
Financial Problems
Addressing and solving common financial and accounting-related data issues.
Payments Duration!
Challenge No. 120: In challenge 60, we attempted to calculate the source of payment for each receipt.
Solved using:Excel, Google Sheets, Power Query (List.Transform), Python in Excel, and R.
Dso!
Challenge No. 118: Using the sales transactions from the provided table and the account balance as of 31/08/2024, calculate the Daily Sales Outstanding (DSO) for each customer.
Solved using:Excel, Google Sheets, Power Query, Python, Python in Excel, and R.
Calculate financial year running totals
Challenge No. 39: Calculate Running Total for all individuals.
Solved using: Python in Excel.
Reconciliation!
Challenge No. 110: After reconciling the company’s bank transactions with the records in the financial department, discrepancies were found between 5 rows of bank transactions and 7 rows of financial records.
Solved using: Excel.
Price List!
Challenge No. 87: The question tables provide product price lists on various dates and transaction records.
Solved using:Excel (FILTER, LAMBDA, LOOKUP), Power Query (Table.AddColumn, Table.SelectRows), Python, Python in Excel, R, and VBA.
Match Payments!
Challenge No. 60: The customer receipt costs and their payments are provided in Question Tables 1 and 2.
Solved using:Excel, Power Query, and R.
Stepped Tax!
Challenge No. 58: In this challenge, we aim to find an efficient way to calculate the stepped tax based on the tax rates presented in the question table.
Solved using:Excel (LAMBDA, LET, MAP), Power Query (Table.AddColumn), Python, and R.
Purchasing Together!
Challenge No. 51: In the Question Table, historical sales data are provided.
Solved using:Excel (BYROW, HSTACK, LAMBDA), Power Query (List.Contains, List.ContainsAll, List.Transform), Python, and R.
Risk Analysis
Challenge No. 30: In risk analysis, activities are categorized into various groups according to their likelihood of occurrence and their impact.
Solved using:Excel, Power Query, Python, and R.