Stop wasting time pasting rows manually. Try using VSTACK instead to combine your tables instantly! #ExcelTips #SpreadsheetHacks #OfficeProductivity
Top 15 Excel functions
XLOOKUP - looks any direction, no column counting, optional fallback for missing values
VLOOKUP - old, still everywhere. Forgetting the FALSE argument returns a wrong value silently
INDEX-MATCH - more flexible than VLOOKUP, doesn't need the return column to be on the right
IF - the backbone every other advanced formula is built on
SUMIFS - add up numbers across multiple conditions at once
COUNTIFS - same structure as SUMIFS, counts instead of sums
IFERROR - turns a raw error into something that looks finished
TRIM - strips invisible spaces that silently break lookups
TEXTJOIN - combines cells with a separator, skips blanks automatically
ROUND - changes the actual stored value, not just how it displays
DATEDIF - years, months, or days between two dates. Hidden from autocomplete, still works
RANK - ranks a value against a full list
SUMPRODUCT - multiplies and sums in one shot, no helper column
UNIQUE - pulls distinct values instantly, updates live
CONCATENATE or & - stitches text and numbers together for a couple of cells
Q: Table orders(order_id, customer_id, order_date, amount). Find customers who ordered in every month of 2024.
A) GROUP BY customer_id HAVING COUNT(*)=12
B) GROUP BY customer_id HAVING COUNT(DISTINCT MONTH(order_date))=12
C) GROUP BY customer_id, MONTH(order_date) HAVING COUNT(*)=12
D) SELECT DISTINCT customer_id WHERE YEAR(order_date)=2024
Answer: B - counts distinct months per customer; A/C/D don't enforce all 12 months.
#sql #sqlbasics #sqlinterview #sqldatatypes
STOP. 90% of beginners quit right around Day 2. Here's why you won't. Scratch to Data Scientist, Day 2 of 100.
Variables = labeled boxes that hold values:
name = "Aria"
age = 25
age = age + 1 # reassigning
One label, one value, reusable anywhere below it.
Starting a 100 day thing. Scratch to Data Scientist. No fluff, just the actual path from zero to job ready, one skill at a time. Day 1 below.
Where this goes: python basics, then real messy data, then stats, then ML, then you build an actual portfolio, then you're job ready.








