Excel Keyboard Shortcuts 👆

Useful Excel Formulas ✅

📌10 intermediate-level SQL interview questions

1. How would you find the nth highest salary in a table?
2. What is the difference between JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN?
3. How would you calculate cumulative sum in SQL?
4. How do you identify duplicate records in a table?
5. Explain the concept of a window function and give examples.
6. How would you retrieve records between two dates in SQL?
7. What is the difference between UNION and UNION ALL?
8. How can you pivot data in SQL?
9. Explain the use of CASE statements in SQL.
10. How do you use common table expressions (CTEs)?

#sql

Excel Shortcuts 👆

Here are some Excel shortcuts that are commonly used by data analysts:

1. Ctrl + C: Copy
2. Ctrl + V: Paste
3. Ctrl + X: Cut
4. Ctrl + Z: Undo
5. Ctrl + Y: Redo
6. Ctrl + S: Save
7. Ctrl + F: Find
8. Ctrl + H: Replace
9. Ctrl + Arrow Keys: Navigate to the edge of data
10. Ctrl + Shift + Arrow Keys: Select data range
11. Ctrl + Home: Go to cell A1
12. Ctrl + End: Go to last cell with data
13. Ctrl + Shift + L: Toggle filters
14. Alt + ; : Select visible cells only
15. F2: Edit active cell
16. Ctrl + Shift + Enter: Enter an array formula
17. Ctrl + D: Fill down
18. Ctrl + R: Fill right
19. Ctrl + 1: Format cells dialog box
20. Ctrl + Shift + 1, 2, 3, etc.: Apply different number formats

These shortcuts can significantly increase your efficiency when working with Excel as a data analyst.

One of the very important tool that a data analyst must be aware of is Excel

Why is Excel Often the Starting Point for SQL ?


Here's how Excel can help you before you dive into SQL:

✔️ 𝐕𝐋𝐎𝐎𝐊𝐔𝐏 = 𝐒𝐐𝐋 𝐉𝐎𝐈𝐍𝐒
In Excel, we use VLOOKUP to bring together data from different sheets. It's just like using JOINS in SQL to get data from more than one table.

✔️ 𝐒𝐔𝐌 𝐚𝐧𝐝 𝐂𝐎𝐔𝐍𝐓 𝐟𝐨𝐫 𝐒𝐐𝐋 𝐐𝐮𝐞𝐫𝐢𝐞𝐬
Excel's SUM and COUNT functions are like practice for SQL queries. They help you add up and count things, which is what you often do in SQL.

✔️ 𝐅𝐈𝐋𝐓𝐄𝐑 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬 & 𝐖𝐇𝐄𝐑𝐄 𝐢𝐧 𝐒𝐐𝐋
Excel's 𝐅𝐈𝐋𝐓𝐄𝐑 statements let you make choices with your data. This is similar to using WHERE in SQL to pick specific data.

✔️ 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐃𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐓𝐞𝐱𝐭
Both Excel and SQL have ways to work with dates and text. Learning these in Excel first can make it easier when you switch to SQL.

✔️ 𝐏𝐢𝐯𝐨𝐭 𝐓𝐚𝐛𝐥𝐞𝐬 & 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘 𝐢𝐧 𝐒𝐐𝐋
Ever used pivot tables in Excel? They're a good start for understanding the GROUP BY function in SQL, which helps you organize and summarize data.

✔️ 𝐗𝐋𝐎𝐎𝐊𝐔𝐏 & 𝐇𝐲𝐩𝐞𝐫𝐥𝐢𝐧𝐤𝐬
Excel's XLOOKUP and hyperlinks are like SQL's ways of finding and linking data. They give you a peek into how SQL finds and connects information.

Learning Excel first makes SQL easier to understand. It's not just about learning a tool, it's about getting ready for the bigger world of data!

You will be asked questions on SQL in interviews for sure! Make sure to practice 2-3 questions daily, it can't be mastered overnight!

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Hope this helps you 😊

Master these 6 things in Excel, and you will be irreplaceable at your job

1. Formulas and functions
2. Pivot Tables
3. Macros and VBA
4. Data Visualization
5. Power Pivot
6. Regular Practice

🧵 10 Basic Excel Formulas Everyone Needs to Know 👇

🔵 SUM =SUM(A1:A10) — Adds values.
🔵 AVERAGE =AVERAGE(A1:A10) — Finds average.
🔵 COUNT =COUNT(A1:A10) — Counts numbers.
🔵 COUNTA =COUNTA(A1:A10) — Counts non-empty cells.
🔵 IF =IF(A1>10, "Yes", "No") — Conditional result.
🔵 MIN =MIN(A1:A10) — Smallest value.
🔵 MAX =MAX(A1:A10) — Largest value.
🔵 VLOOKUP =VLOOKUP(B1, A1:D10, 2, FALSE) — Looks up value.
🔵 & =A1 & " " & B1 — Joins text.
🔵 LEN =LEN(A1) — Counts characters.

#ExcelTips

How to get a Client as an Excel Freelancer and earn Second Salary :)

Step 1: Build a Portfolio
Create a portfolio showcasing your best Excel work. Include a variety of projects that highlight your skills and expertise. If you're just starting, consider creating sample projects or offering your services at a reduced rate to friends or non-profits to build your portfolio.


Step 2: Set Up Profiles on Freelance Platforms
Register on freelance platforms like Upwork, Freelancer, Fiverr, and LinkedIn. Create detailed profiles emphasizing your Excel skills, experience, and the services you offer. Use a professional profile picture and a compelling bio.

Step 3: Networking
Join Excel and freelancing forums, LinkedIn groups, and other online communities. Engage with the community by answering questions, sharing insights, and offering helpful advice. Networking can lead to referrals and direct client opportunities.

Step 4: Direct Outreach
Identify potential clients or businesses that might benefit from your services. Craft personalized outreach emails explaining how your Excel skills can solve their problems or improve their operations. Focus on the value you can bring to their business.

Step 5: Social Proof and Testimonials
As you complete projects, ask your clients for testimonials. Positive reviews and ratings on your freelance platform profiles can significantly boost your credibility and attract more clients.

Step 6: Create Content
Share your Excel knowledge through blogs, tutorials, or videos. This can position you as an expert in your field and attract clients looking for your specific skills.

Step 7: Leverage SEO
If you have a personal website or blog, optimize it for search engines using relevant keywords related to Excel services. This can help potential clients find you through Google searches.

Step 8: Offer Packages
Instead of offering generic Excel services, create packages tailored to specific needs, such as "Excel Financial Models for Startups" or "Data Analysis and Visualization for Marketing Teams". This makes your offering clearer and more attractive to potential clients.

Step 9: Follow Up
After sending proposals or quotes, follow up with potential clients. A gentle reminder can sometimes turn a maybe into a yes.

Step 10: Provide Excellent Service
Deliver high-quality work on time, communicate effectively, and go the extra mile for your clients. Satisfied clients are more likely to return for more work or refer you to others.

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Most people suck at using Microsoft Excel.

I'm not talking about formatting data/reports or writing formulas.

I'm talking about using Excel to analyze data and make an impact.

Here are 7 ways to stand out from the crowd:

1) Don't make PivotTables your hammer and every problem a nail.

PivotTables are like any other data analysis technique.

They have pros and cons.

Tables are good primarily at two things:

Looking up exact values
Comparing exact values

This alone is not enough for most analyses.

2) Use more charts.

Humans are visual creatures, and we can use this to analyze data.

The best use of PivotTables is to create PivotCharts.

For example, bar charts that use three or more columns of data.

It's way more powerful than a PivotTable.

3) Use line charts.

I can't stress this one enough.

The single most valuable data visualization in business analytics is a line chart.

Line charts allow you to see:

Trends
Variability
Cycles
Rate of change
Exceptions

Oh, and make sure to use line charts in your dashboards!

4) Learn data analysis fundamentals.

Microsoft Excel can be a potent tool - if you know how to analyze data.

Here are two fundamentals that 99% of Excel users don't know:

Distribution analysis
Correlation analysis

While this sounds scary, it isn't.

No fancy math is required.

5) Time to step up to PowerQuery.

It's a crying shame PowerQuery isn't more popular.

It's exceedingly powerful (pun intended) and allows you to automate many steps in your data analyses.

In 2025, however, PowerQuery is more critical than ever because of the following three words.

6) Python in Excel

Shortly, there will be two kinds of Excel users:

Those who use Python in Excel to have an impact at work using DIY data science.

Those that do not.

BTW - If you're the first kind of Excel user, you can make the most of AI by...

7) Use Copilot in Excel with Python

I'm going to be honest.

Vanilla Copilot in Excel isn't very impressive.

However, using the Copilot AI to generate Python code for DIY data science is a different story.

But you must have DIY data science skills to use Copliot, or you're playing with 🔥.

Free Excel Resources: https://t.iss.one/excel_data

Microsoft Excel is used by 99% of the World’s businesses.

But the truth is most people don't know how to use it.

10 must-have Excel skills to accelerate your career:

1. Wildcards
2. XLookup
3. Sparklines
4. Remove duplicates
5. Flash Fill
6. Transpose
7. Trim
8. Pivot tables
9. Upper, lower, proper case
10. Stock market data

💡Top 12 hidden shortcut

Enter full screen mode - Ctrl + Shift + F1
Record a macro - Alt + F8
Open a visual basic editor - Alt + F11
Autofit column height - Alt + H + O + A
Hide a sheet - Alt + H + O + U + S
Unhide a sheet - Alt + H + O + U + H
Insert slicers - Alt + N + SF
Apply or remove strikethrough - Ctrl + 5
Expand or collapse the formula bar - Ctrl + Shift + U
Collapse sheet outline - Alt + A + H / J
Create names from selection - Ctrl + Shift + F3
Insert new worksheet - Shift + F11

Data Analytics Roadmap
|
|-- Fundamentals
|   |-- Mathematics
|   |   |-- Descriptive Statistics
|   |   |-- Inferential Statistics
|   |   |-- Probability Theory
|   |
|   |-- Programming
|   |   |-- Python (Focus on Libraries like Pandas, NumPy)
|   |   |-- R (For Statistical Analysis)
|   |   |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
|   |-- Data Sources
|   |   |-- APIs
|   |   |-- Web Scraping
|   |   |-- Databases
|   |
|   |-- Data Storage
|   |   |-- Relational Databases (MySQL, PostgreSQL)
|   |   |-- NoSQL Databases (MongoDB, Cassandra)
|   |   |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
|   |-- Handling Missing Data
|   |-- Data Transformation
|   |-- Data Normalization and Standardization
|   |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
|   |-- Data Visualization Tools
|   |   |-- Matplotlib
|   |   |-- Seaborn
|   |   |-- ggplot2
|   |
|   |-- Identifying Trends and Patterns
|   |-- Correlation Analysis
|
|-- Advanced Analytics
|   |-- Predictive Analytics (Regression, Forecasting)
|   |-- Prescriptive Analytics (Optimization Models)
|   |-- Segmentation (Clustering Techniques)
|   |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
|   |-- Visualization Tools
|   |   |-- Power BI
|   |   |-- Tableau
|   |   |-- Google Data Studio
|   |
|   |-- Dashboard Design
|   |-- Interactive Visualizations
|   |-- Storytelling with Data
|
|-- Business Intelligence (BI)
|   |-- KPI Design and Implementation
|   |-- Decision-Making Frameworks
|   |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
|   |-- Tools and Frameworks
|   |   |-- Hadoop
|   |   |-- Apache Spark
|   |
|   |-- Real-Time Data Processing
|   |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
|   |-- Industry Applications
|   |   |-- E-commerce
|   |   |-- Healthcare
|   |   |-- Supply Chain
|
|-- Ethical Data Usage
|   |-- Data Privacy Regulations (GDPR, CCPA)
|   |-- Bias Mitigation in Analysis
|   |-- Transparency in Reporting

Free Resources to learn Data Analytics skills👇👇

1. SQL

https://mode.com/sql-tutorial/introduction-to-sql

https://t.iss.one/sqlspecialist/738

2. Python

https://www.learnpython.org/

https://t.iss.one/pythondevelopersindia/873

https://bit.ly/3T7y4ta

https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial

3. R

https://datacamp.pxf.io/vPyB4L

4. Data Structures

https://leetcode.com/study-plan/data-structure/

https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513

5. Data Visualization

https://www.freecodecamp.org/learn/data-visualization/

https://t.iss.one/Data_Visual/2

https://www.tableau.com/learn/training/20223

https://www.workout-wednesday.com/power-bi-challenges/

6. Excel

https://excel-practice-online.com/

https://t.iss.one/excel_data

https://www.w3schools.com/EXCEL/index.php

Join @free4unow_backup for more free courses

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ENJOY LEARNING 👍👍

90% of jobs require Excel skills.

But most people underestimate its importance.

Here're 7 Excel hacks you don't want to miss: 🧵 👇🏻

1. Quick Data Analysis:

• Select a cell in your data.
• Home > Analyze Data.
• Choose an option and click Insert PivotChart.

2. Freeze columns/rows:

• Select the cell below and to the right of what you want to freeze
• Click View > Freeze Panes > Freeze Panes

Excel for Data Analysis

3. If Function

• Open Excel and choose a cell.
• Insert IF function.
• Apply and repeat conditions.
• Close bracket and press Enter.

4. Quick Data Analysis:

• Select a cell in your data.
• Home > Analyze Data.
• Choose an option (Rank, Trend, Outlier, Majority) and click Insert PivotChart.

Excel for Data Analysis

5. Format numbers in cells:

• Press CTRL + 1 and select Number.
• Right-click the cell or cell range, select Format Cells… , and select Number.
• Select the small arrow, dialog box launcher, and then select Number.

Excel for Data Analysis