How much 𝗣𝘆𝘁𝗵𝗼𝗻 is enough to crack a 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄?

𝗕𝗮𝘀𝗶𝗰 𝗣𝘆𝘁𝗵𝗼𝗻 𝗦𝗸𝗶𝗹𝗹𝘀
- Data types: Lists, Dicts, Tuples, Sets
- Loops & conditionals (for, while, if-else)
- Functions & lambda expressions
- File handling (open, read, write)

 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝘁𝗵 𝗣𝗮𝗻𝗱𝗮𝘀
- read_csv, head(), info()  
- Filtering, sorting, and grouping data
- Handling missing values
- Merging & joining DataFrames

 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
- Matplotlib: plot(), bar(), hist()  
- Seaborn: heatmap(), pairplot(), boxplot()  
- Plot styling, titles, and legends

 𝗡𝘂𝗺𝗣𝘆 & 𝗠𝗮𝘁𝗵 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻
- Arrays and broadcasting
- Vectorized operations
- Basic statistics: mean, median, std

 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 & 𝗣𝗿𝗲𝗽
- Remove duplicates, rename columns
- Apply functions row-wise or column-wise
- Convert data types, parse dates

 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝗧𝗶𝗽𝘀
- List comprehensions
- Exception handling (try-except)
- Working with APIs (requests, json)
- Automating tasks with scripts

 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀
- Sales forecasting
- Web scraping for data
- Survey result analysis
- Excel automation with openpyxl or xlsxwriter  

 Must-Have Strengths: 
- Data wrangling & preprocessing
- EDA (Exploratory Data Analysis)
- Writing clean, reusable code

- Extracting insights & telling stories with data 

🧠 How much SQL is enough to crack a Data Analyst Interview?

  Basic Queries

⦁ SELECT, FROM, WHERE, ORDER BY, LIMIT
⦁ Filtering, sorting, and simple conditions

 Joins & Relations
⦁ INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
⦁ Using keys to combine data from multiple tables

 Aggregate Functions
⦁ COUNT(), SUM(), AVG(), MIN(), MAX()
⦁ GROUP BY and HAVING for grouped analysis

 Subqueries & CTEs
⦁ SELECT within SELECT
⦁ WITH statements for better readability

 Set Operations
⦁ UNION, INTERSECT, EXCEPT
⦁ Merging and comparing result sets

 Date & Time Functions
⦁ NOW(), CURDATE(), DATEDIFF(), DATE_ADD()
⦁ Formatting & filtering date columns

 Data Cleaning
⦁ TRIM(), UPPER(), LOWER(), REPLACE()
⦁ Handling NULLs & duplicates

 Real World Tasks
⦁ Sales by region
⦁ Weekly/monthly trend tracking
⦁ Customer churn queries
⦁ Product category comparisons

 Must-Have Strengths:
⦁ Writing clear, efficient queries
⦁ Understanding data schemas
⦁ Explaining logic behind joins/filters
⦁ Drawing business insights from raw data