Challenge No. 8: The Question’s table outlines warehouse transactions with records for Initial value (warehouse product count), Add (purchased and incoming products), and Reduce (sold and outgoing products).
Solved using:Excel, and Power Query.
LAMBDA
Creates custom reusable functions in Excel.
K-Means Clustering Algorithm!
Challenge No. 6: The k-means clustering algorithm is a popular method used in data science and machine learning for partitioning a dataset into k distinct, non-overlapping subsets (clusters) with the below steps.
Solved using:Excel, and Power Query.
Analyze Customer Purchasing Patterns!
Challenge No. 5: Determine how frequently products are bought together (same invoice number).
Solved using:Excel, and Power Query.
Advanced Calculation
Challenge No. 4: Calculate the mission’s income, where the daily rate starts at $1 on the first day and increases by $1 for the next continuous days (the rate for the third day in the mission is 3$).
Solved using: Excel.
Row Combinations
Challenge No. 3: Extract all possible combinations of ID and result in the right-side table.
Solved using:Excel (BYROW, COUNTA, DROP), Power Query (List.Transform), Python, and Python in Excel.
Custom Grouping! Part 1
Challenge No. 2: Group the rows from the top, which in each group the total cost be lower than 130$.
Solved using:Excel (HSTACK, IF, LAMBDA), and Power Query (Table.Group).
Table Transformation! Part 1
Challenge No. 1: On various dates, the cumulative sales values (from the beginning of the year) for different products are presented (in the Question table).
Solved using:Excel (DROP, FILTER, HSTACK), and Power Query (Table.AddColumn, Table.TransformColumnTypes).