Hey guys,

Today, I’m covering some Excel interview questions that often pop up in data analyst roles 👇👇

1. What are the most common functions used in Excel for data analysis?

- SUM(): Adds up values in a range.
- AVERAGE(): Finds the mean of a range of numbers.
- VLOOKUP() / XLOOKUP(): Searches for a value in a table and returns a related value.
- INDEX-MATCH: A more flexible alternative to VLOOKUP, allowing lookups in any direction.
- IF(): Performs logical tests and returns one value if TRUE, another if FALSE.
- COUNTIF(): Counts the number of cells that meet a specific condition.
- PivotTables: For summarizing, analyzing, and exploring large datasets.

2. What is the difference between VLOOKUP and XLOOKUP?

- VLOOKUP is an older function used to find data in a vertical column and return a value from another column to the right.

Example:

  =VLOOKUP("A2", B2:D10, 3, FALSE)
  

- XLOOKUP is more powerful, offering the flexibility to search both vertically and horizontally, and it doesn’t require the lookup value to be in the first column.

Example:

  =XLOOKUP(A2, B2:B10, C2:C10)
  

Tip: Explain the limitations of VLOOKUP (like not being able to search left or needing sorted data for approximate matches) and how XLOOKUP overcomes them.

3. How do you create a PivotTable in Excel, and why is it useful?

A PivotTable allows you to summarize large amounts of data quickly. Here’s how to create one:

1. Select your data.
2. Go to the Insert tab and click on PivotTable.
3. Choose where to place the PivotTable.
4. Drag and drop fields into the Rows, Columns, Values, and Filters sections.

4. What is conditional formatting, and how do you use it?

Conditional formatting is used to change the appearance of cells based on their content. It helps highlight trends, patterns, and outliers.

For example, to highlight cells greater than 1000:
1. Select the range of cells.
2. Go to the Home tab, click on Conditional Formatting.
3. Choose Highlight Cell Rules > Greater Than and enter 1000.
4. Choose a format (e.g., cell color) to apply.

5. How do you handle large datasets in Excel without slowing it down?

Here are some strategies to improve efficiency:

- Turn off automatic calculations: Use manual recalculation to prevent Excel from recalculating formulas every time you make a change.

  File > Options > Formulas > Calculation Options > Manual
  

- Use fewer volatile functions: Functions like NOW(), TODAY(), and INDIRECT() recalculate every time a change is made.

- Use tables instead of ranges: Structured references in tables are more efficient.

- Split large datasets: If feasible, split your data across multiple sheets or workbooks.

- Remove unnecessary formatting: Too much formatting can bloat file size and slow down processing.

6. How do you use Excel for data cleaning?

Data cleaning is one of the first and most important steps in data analysis, and Excel provides multiple ways to do this:

- Remove duplicates: Easily eliminate duplicate entries.

- Text to Columns: Split data in one column into multiple columns (e.g., splitting full names into first and last names).

- TRIM(): Remove extra spaces from text.

- FIND() and SUBSTITUTE(): For locating and replacing specific characters or substrings.

7. What are some advanced Excel functions you’ve used for data analysis?

Aside from the basics, some advanced Excel functions you might mention include:

- ARRAYFORMULA(): Allows multiple calculations to be performed at once.
- OFFSET(): Returns a range that is offset from a starting point.
- FORECAST(): Predicts future values based on historical data.
- POWER QUERY: For data extraction, transformation, and loading (ETL) tasks.

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SQL Cheatsheet 📝

This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether you’re a beginner learning how to query databases or an experienced developer looking for a handy resource, this cheatsheet covers essential SQL topics.

1. Database Basics
- CREATE DATABASE db_name;
- USE db_name;

2. Tables
- Create Table: CREATE TABLE table_name (col1 datatype, col2 datatype);
- Drop Table: DROP TABLE table_name;
- Alter Table: ALTER TABLE table_name ADD column_name datatype;

3. Insert Data
- INSERT INTO table_name (col1, col2) VALUES (val1, val2);

4. Select Queries
- Basic Select: SELECT * FROM table_name;
- Select Specific Columns: SELECT col1, col2 FROM table_name;
- Select with Condition: SELECT * FROM table_name WHERE condition;

5. Update Data
- UPDATE table_name SET col1 = value1 WHERE condition;

6. Delete Data
- DELETE FROM table_name WHERE condition;

7. Joins
- Inner Join: SELECT * FROM table1 INNER JOIN table2 ON table1.col = table2.col;
- Left Join: SELECT * FROM table1 LEFT JOIN table2 ON table1.col = table2.col;
- Right Join: SELECT * FROM table1 RIGHT JOIN table2 ON table1.col = table2.col;

8. Aggregations
- Count: SELECT COUNT(*) FROM table_name;
- Sum: SELECT SUM(col) FROM table_name;
- Group By: SELECT col, COUNT(*) FROM table_name GROUP BY col;

9. Sorting & Limiting
- Order By: SELECT * FROM table_name ORDER BY col ASC|DESC;
- Limit Results: SELECT * FROM table_name LIMIT n;

10. Indexes
- Create Index: CREATE INDEX idx_name ON table_name (col);
- Drop Index: DROP INDEX idx_name;

11. Subqueries
- SELECT * FROM table_name WHERE col IN (SELECT col FROM other_table);

12. Views
- Create View: CREATE VIEW view_name AS SELECT * FROM table_name;
- Drop View: DROP VIEW view_name;

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Power BI Cheat Sheet ✍

This Power BI cheatsheet is designed to be your quick reference guide for creating impactful reports and dashboards. Whether you’re a beginner exploring the basics or an experienced developer looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting Data
- Import Data: *Home > Get Data > Select Data Source*
- Direct Query: *Home > Get Data > Select Data Source > Direct Query*

2. Data Transformation
- Power Query Editor: *Home > Transform Data*
- Remove Columns: *Transform > Remove Columns*
- Split Columns: *Transform > Split Column by Delimiter*
- Replace Values: *Transform > Replace Values*

3. Data Modeling
- Create Relationships: *Model > Manage Relationships > New*
- Edit Relationships: *Model > Manage Relationships > Edit*

4. DAX Calculations
- New Measure: *Modeling > New Measure*
- Common DAX Functions:
- SUM: SUM(table[column])
- AVERAGE: AVERAGE(table[column])
- IF: IF(condition, true_value, false_value)
- COUNTROWS: COUNTROWS(table)
- CALCULATE: CALCULATE(expression, filter)

5. Creating Visuals
- Select Visualization: *Visualizations Pane > Select Visual Type*
- Bar Chart: *Bar Chart Icon*
- Pie Chart: *Pie Chart Icon*
- Map Visual: *Map Icon*

6. Formatting Visuals
- Change Colors: *Format > Data Colors*
- Customize Titles: *Format > Title > Text*
- Adjust Axis: *Format > Y-Axis / X-Axis*

7. Filters
- Visual Level Filter: *Filter Pane > Add Filter for Selected Visual*
- Page Level Filter: *Filter Pane > Add Filter for Entire Page*
- Report Level Filter: *Filter Pane > Add Filter for Entire Report*

8. Slicers
- Add Slicer: *Visualizations > Slicer Icon*
- Customize Slicer: *Format > Edit Interactions*

9. Drillthrough
- Add Drillthrough: *Pages > Right Click on Field > Drillthrough*
- Back Button: *Insert > Button > Back Button*

10. Publishing & Sharing
- Publish Report: *Home > Publish > Select Workspace*
- Share Report: *File > Share > Publish to Web or Power BI Service*

11. Dashboards
- Create Dashboard: *Power BI Service > New Dashboard*
- Pin Visuals: *Pin Icon on Visual > Pin to Dashboard*

12. Export Options
- Export to PDF: *File > Export > PDF*
- Export Data: *Visual Options > Export Data*

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Python CheatSheet 📚 ✅

1. Basic Syntax
- Print Statement: print("Hello, World!")
- Comments: # This is a comment

2. Data Types
- Integer: x = 10
- Float: y = 10.5
- String: name = "Alice"
- List: fruits = ["apple", "banana", "cherry"]
- Tuple: coordinates = (10, 20)
- Dictionary: person = {"name": "Alice", "age": 25}

3. Control Structures
- If Statement:

     if x > 10:
         print("x is greater than 10")
     

- For Loop:

     for fruit in fruits:
         print(fruit)
     

- While Loop:

     while x < 5:
         x += 1
     

4. Functions
- Define Function:

     def greet(name):
         return f"Hello, {name}!"
     

- Lambda Function: add = lambda a, b: a + b

5. Exception Handling
- Try-Except Block:

     try:
         result = 10 / 0
     except ZeroDivisionError:
         print("Cannot divide by zero.")
     

6. File I/O
- Read File:

     with open('file.txt', 'r') as file:
         content = file.read()
     

- Write File:

     with open('file.txt', 'w') as file:
         file.write("Hello, World!")
     

7. List Comprehensions
- Basic Example: squared = [x**2 for x in range(10)]
- Conditional Comprehension: even_squares = [x**2 for x in range(10) if x % 2 == 0]

8. Modules and Packages
- Import Module: import math
- Import Specific Function: from math import sqrt

9. Common Libraries
- NumPy: import numpy as np
- Pandas: import pandas as pd
- Matplotlib: import matplotlib.pyplot as plt

10. Object-Oriented Programming
- Define Class:

      class Dog:
          def __init__(self, name):
              self.name = name
          def bark(self):
              return "Woof!"
      

11. Virtual Environments
- Create Environment: python -m venv myenv
- Activate Environment:
- Windows: myenv\Scripts\activate
- macOS/Linux: source myenv/bin/activate

12. Common Commands
- Run Script: python noscript.py
- Install Package: pip install package_name
- List Installed Packages: pip list

This Python checklist serves as a quick reference for essential syntax, functions, and best practices to enhance your coding efficiency!

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Excel Cheat Sheet 📔

This Excel cheatsheet is designed to be your quick reference guide for using Microsoft Excel efficiently.

1. Basic Functions
- SUM: =SUM(range)
- AVERAGE: =AVERAGE(range)
- COUNT: =COUNT(range)
- MAX: =MAX(range)
- MIN: =MIN(range)

2. Text Functions
- CONCATENATE: =CONCATENATE(text1, text2, ...) or =TEXTJOIN(delimiter, ignore_empty, text1, text2, ...)
- LEFT: =LEFT(text, num_chars)
- RIGHT: =RIGHT(text, num_chars)
- MID: =MID(text, start_num, num_chars)
- TRIM: =TRIM(text)

3. Logical Functions
- IF: =IF(condition, true_value, false_value)
- AND: =AND(condition1, condition2, ...)
- OR: =OR(condition1, condition2, ...)
- NOT: =NOT(condition)

4. Lookup Functions
- VLOOKUP: =VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])
- HLOOKUP: =HLOOKUP(lookup_value, table_array, row_index_num, [range_lookup])
- INDEX: =INDEX(array, row_num, [column_num])
- MATCH: =MATCH(lookup_value, lookup_array, [match_type])

5. Data Sorting & Filtering
- Sort: *Data > Sort*
- Filter: *Data > Filter*
- Advanced Filter: *Data > Advanced*

6. Conditional Formatting
- Apply Formatting: *Home > Conditional Formatting > New Rule*
- Highlight Cells: *Home > Conditional Formatting > Highlight Cells Rules*

7. Charts and Graphs
- Insert Chart: *Insert > Select Chart Type*
- Customize Chart: *Chart Tools > Design/Format*

8. PivotTables
- Create PivotTable: *Insert > PivotTable*
- Refresh PivotTable: *Right-click on PivotTable > Refresh*

9. Data Validation
- Set Validation: *Data > Data Validation*
- List: *Allow: List > Source: range or items*

10. Protecting Data
- Protect Sheet: *Review > Protect Sheet*
- Protect Workbook: *Review > Protect Workbook*

11. Shortcuts
- Copy: Ctrl + C
- Paste: Ctrl + V
- Undo: Ctrl + Z
- Redo: Ctrl + Y
- Save: Ctrl + S

12. Printing Options
- Print Area: *Page Layout > Print Area > Set Print Area*
- Page Setup: *Page Layout > Page Setup*

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Python Interview Questions:

Ready to test your Python skills? Let’s get started! 💻


1. How to check if a string is a palindrome?

def is_palindrome(s):
    return s == s[::-1]

print(is_palindrome("madam"))  # True
print(is_palindrome("hello"))  # False

2. How to find the factorial of a number using recursion?

def factorial(n):
    if n == 0 or n == 1:
        return 1
    return n * factorial(n - 1)

print(factorial(5))  # 120

3. How to merge two dictionaries in Python?

dict1 = {'a': 1, 'b': 2}
dict2 = {'c': 3, 'd': 4}

# Method 1 (Python 3.5+)
merged_dict = {**dict1, **dict2}

# Method 2 (Python 3.9+)
merged_dict = dict1 | dict2

print(merged_dict)

4. How to find the intersection of two lists?

list1 = [1, 2, 3, 4]
list2 = [3, 4, 5, 6]

intersection = list(set(list1) & set(list2))
print(intersection)  # [3, 4]

5. How to generate a list of even numbers from 1 to 100?

even_numbers = [i for i in range(1, 101) if i % 2 == 0]
print(even_numbers)

6. How to find the longest word in a sentence?

def longest_word(sentence):
    words = sentence.split()
    return max(words, key=len)

print(longest_word("Python is a powerful language"))  # "powerful"

7. How to count the frequency of elements in a list?

from collections import Counter

my_list = [1, 2, 2, 3, 3, 3, 4]
frequency = Counter(my_list)
print(frequency)  # Counter({3: 3, 2: 2, 1: 1, 4: 1})

8. How to remove duplicates from a list while maintaining the order?

def remove_duplicates(lst):
    return list(dict.fromkeys(lst))

my_list = [1, 2, 2, 3, 4, 4, 5]
print(remove_duplicates(my_list))  # [1, 2, 3, 4, 5]

9. How to reverse a linked list in Python?

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

def reverse_linked_list(head):
    prev = None
    current = head
    while current:
        next_node = current.next
        current.next = prev
        prev = current
        current = next_node
    return prev

# Create linked list: 1 -> 2 -> 3
head = Node(1)
head.next = Node(2)
head.next.next = Node(3)

# Reverse and print the list
reversed_head = reverse_linked_list(head)
while reversed_head:
    print(reversed_head.data, end=" -> ")
    reversed_head = reversed_head.next

10. How to implement a simple binary search algorithm?

def binary_search(arr, target):
    low, high = 0, len(arr) - 1
    while low <= high:
        mid = (low + high) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            low = mid + 1
        else:
            high = mid - 1
    return -1

print(binary_search([1, 2, 3, 4, 5, 6, 7], 4))  # 3

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Tableau Cheat Sheet ✅

This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting to Data
- Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.).

2. Data Preparation
- Data Interpreter: Clean data automatically using the Data Interpreter.
- Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
- Union Data: Stack data from multiple tables with the same structure.

3. Creating Views
- Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
- Show Me: Use the *Show Me* panel to select different visualization types.

4. Types of Visualizations
- Bar Chart: Compare values across categories.
- Line Chart: Display trends over time.
- Pie Chart: Show proportions of a whole (use sparingly).
- Map: Visualize geographic data.
- Scatter Plot: Show relationships between two variables.

5. Filters
- Dimension Filters: Filter data based on categorical values.
- Measure Filters: Filter data based on numerical values.
- Context Filters: Set a context for other filters to improve performance.

6. Calculated Fields
- Create calculated fields to derive new data:
- Example: Sales Growth = SUM([Sales]) - SUM([Previous Sales])

7. Parameters
- Use parameters to allow user input and control measures dynamically.

8. Formatting
- Format fonts, colors, borders, and lines using the Format pane for better visual appeal.

9. Dashboards
- Combine multiple sheets into a dashboard using the *Dashboard* tab.
- Use dashboard actions (filter, highlight, URL) to create interactivity.

10. Story Points
- Create a story to guide users through insights with narrative and visualizations.

11. Publishing & Sharing
- Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.

12. Export Options
- Export to PDF or image for offline use.

13. Keyboard Shortcuts
- Show/Hide Sidebar: Ctrl+Alt+T
- Duplicate Sheet: Ctrl + D
- Undo: Ctrl + Z
- Redo: Ctrl + Y

14. Performance Optimization
- Use extracts instead of live connections for faster performance.
- Optimize calculations and filters to improve dashboard loading times.

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Let's go through each of the above topics one by one ✅

1. Writing Complex JOIN Queries

Complex JOINs can be intimidating, especially when working with multiple tables, but with a structured approach, you can simplify them. Here’s how:

Understand Different Types of JOINs: Ensure you're familiar with the four basic types of JOINs—INNER JOIN (returns only matching rows), LEFT JOIN (returns all rows from the left table and matching rows from the right), RIGHT JOIN (returns all rows from the right table and matching rows from the left), and FULL OUTER JOIN (returns rows when there is a match in one of the tables).

Visualize Relationships: Before you write the query, map out how tables are connected.

✅ Are they linked by foreign keys?

✅ Do they share columns like IDs or other identifiers?

✅ Drawing a diagram helps avoid confusion.

Start Simple: Instead of jumping straight into a complex multi-table JOIN, start by querying just two tables. Test that the result is correct before adding more tables.

Alias Tables: Use short aliases for table names. This not only makes your query easier to read but reduces the chance of making mistakes in longer queries.

Use Filters Wisely: When using multiple JOINs, WHERE clauses can affect the outcome significantly. Always check the data returned after each JOIN to make sure your filters are applied correctly.

Test with Different Data Sets: Always test your complex JOIN queries with edge cases—such as when one table has missing or NULL values—to make sure you are handling these situations properly.


Example:

SELECT 
  employees.name,
  departments.department_name,
  projects.project_name
FROM employees
INNER JOIN departments ON employees.department_id = departments.department_id
LEFT JOIN projects ON employees.project_id = projects.project_id
WHERE departments.department_name = 'IT'; 

In this query, we use an INNER JOIN to match employees with their departments and a LEFT JOIN to include project details, even if an employee isn't currently assigned to a project.

If you’re stuck, use CTEs (Common Table Expressions) or break the query into smaller parts to debug it.

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Today, let's go through second important topic of the poll

✅ Optimizing Query Performance in SQL

When working with large datasets, optimizing query performance becomes crucial. Slow queries can impact application speed and user experience. Here’s how you can ensure your SQL queries run efficiently:

Indexing Matters: One of the easiest ways to speed up queries is by creating indexes on columns frequently used in WHERE, JOIN, or GROUP BY clauses. However, avoid over-indexing, as this can slow down write operations like INSERT or UPDATE.

Use SELECT * Sparingly: Always specify the columns you need instead of using SELECT *. Pulling unnecessary columns can increase query execution time, especially with large tables.

Analyze Execution Plans: Most database systems allow you to view the execution plan for a query. This shows you how the query is processed, which can help identify bottlenecks such as table scans or improper index usage.

Limit the Results: If you only need a subset of the data (e.g., the top 10 rows), use LIMIT or TOP to reduce the data load.

Avoid N+1 Queries: Instead of running multiple queries in a loop (like fetching records one by one), use a single query with IN or a JOIN to retrieve all needed data at once.

Partition Large Tables: If you're dealing with massive tables, partitioning the data can improve query speed by allowing the database to search only a segment of the table.

Optimize Subqueries and Joins: For complex queries with multiple subqueries or joins, sometimes rewriting them as CTEs (Common Table Expressions) can improve readability and performance. Additionally, avoid correlated subqueries when possible, as they tend to be slower than joins.


Example:

SELECT customers.customer_name,
  orders.order_date
FROM customers
JOIN orders ON customers.customer_id = orders.customer_id
WHERE orders.order_date > '2023-01-01'
ORDER BY orders.order_date
LIMIT 10;

In this query:

Indexes on customer_id and order_date will speed up the JOIN and WHERE clauses. Using LIMIT 10 ensures the query fetches only 10 results, reducing the load on the database.

Continuously monitor query performance in production environments. Even small improvements (e.g., reducing query time from 2 seconds to 1 second) can make a significant difference when queries are run frequently.

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Going live for the first time, lessssgooooooooo 😁

I enjoyed connecting with you all. Thanks everyone for the kind words, it really motivates me to post more content in the future ❤️

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Storytelling Resources

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How to Become a Data Analyst from Scratch! 🚀

Whether you're starting fresh or upskilling, here's your roadmap:

➜ Master Excel and SQL - solve SQL problems from leetcode & hackerank
➜ Get the hang of either Power BI or Tableau - do some hands-on projects
➜ learn what the heck ATS is and how to get around it
➜ learn to be ready for any interview question
➜ Build projects for a data portfolio
➜ And you don't need to do it all at once!
➜ Fail and learn to pick yourself up whenever required

Whether it's acing interviews or building an impressive portfolio, give yourself the space to learn, fail, and grow. Good things take time ✅

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Let's go through the next topic today

How to use Subqueries Effectively

Subqueries are incredibly useful when you need to perform a query within a query. However, they can sometimes be challenging to use efficiently. Here’s how to master subqueries for cleaner and more powerful SQL queries:

Types of Subqueries:

Scalar Subqueries: These return a single value and are often used in SELECT or WHERE clauses.

Row Subqueries: These return one row and are used with IN or EXISTS.

Table Subqueries: These return multiple rows and columns and can be used in the FROM clause as a derived table.


Use Cases: Subqueries are great for breaking complex logic into smaller, more manageable pieces. Common use cases include filtering records based on aggregate results or comparing data between two tables without using a JOIN.

Performance Considerations: While subqueries are powerful, they can sometimes be slower than JOINs, especially when nested multiple times. Consider using JOINs or Common Table Expressions (CTEs) as alternatives for performance optimization.

Avoid Correlated Subqueries: Correlated subqueries reference columns from the outer query, which means the subquery runs repeatedly for each row in the outer query. This can be inefficient for large datasets. Use them only when necessary, and always check performance.


Example:

SELECT customer_id, customer_name
FROM customers
WHERE customer_id IN (
  SELECT customer_id
  FROM orders
  WHERE order_date > '2023-01-01'
);

In this example, the subquery retrieves customer IDs that placed orders after a specific date. The outer query uses this subquery to filter the list of customers.

Alternative with JOIN:

While subqueries are useful, a JOIN can sometimes be more efficient. The query above could be rewritten as a JOIN:

SELECT DISTINCT customers.customer_id, customers.customer_name
FROM customers
JOIN orders ON customers.customer_id = orders.customer_id
WHERE orders.order_date > '2023-01-01';

Choose Wisely: Always consider whether a subquery or a JOIN makes more sense for the specific problem. JOINs are typically faster for larger datasets, but subqueries can be more readable in some cases.

When working with subqueries, always test their performance, especially if they are nested within other queries or return large result sets. Consider using indexing to improve speed where possible.


Writing Complex Joins

Optimise Complex SQL Queries

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