How you can learn Data Analytics in 28 days:
Week 1: Excel
• Learn functions (VLOOKUP, Pivot Tables)
• Clean and format data
• Analyze trends
Week 2: SQL
• Learn SELECT, WHERE, JOIN
• Query real datasets
• Aggregate and filter data
Week 3: Power BI/Tableau
• Build dashboards
• Create data visualizations
• Tell stories with data
Week 4: Real-World Project
• Analyze a data
• Share insights
• Build a portfolio
One skill at a time → Real progress in a month! Start today
https://t.iss.one/jobs_SQL
Week 1: Excel
• Learn functions (VLOOKUP, Pivot Tables)
• Clean and format data
• Analyze trends
Week 2: SQL
• Learn SELECT, WHERE, JOIN
• Query real datasets
• Aggregate and filter data
Week 3: Power BI/Tableau
• Build dashboards
• Create data visualizations
• Tell stories with data
Week 4: Real-World Project
• Analyze a data
• Share insights
• Build a portfolio
One skill at a time → Real progress in a month! Start today
https://t.iss.one/jobs_SQL
❤1
9 tips to learn Python for Data Analysis:
🐍 Start with the basics: variables, loops, functions
🧹 Master Pandas for data manipulation
🔢 Use NumPy for numerical operations
📊 Visualize data with Matplotlib and Seaborn
📂 Work with real datasets (CSV, Excel, APIs)
🧼 Clean and preprocess messy data
📈 Understand basic statistics and correlations
⚙️ Automate repetitive analysis tasks with scripts
💡 Build mini-projects to apply your skills
Free Python Resources: https://t.iss.one/pythonanalyst
Like for more daily tips 👍 ♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
🐍 Start with the basics: variables, loops, functions
🧹 Master Pandas for data manipulation
🔢 Use NumPy for numerical operations
📊 Visualize data with Matplotlib and Seaborn
📂 Work with real datasets (CSV, Excel, APIs)
🧼 Clean and preprocess messy data
📈 Understand basic statistics and correlations
⚙️ Automate repetitive analysis tasks with scripts
💡 Build mini-projects to apply your skills
Free Python Resources: https://t.iss.one/pythonanalyst
Like for more daily tips 👍 ♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤2🔥1
5 Essential Skills Every Data Analyst Must Master in 2025
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wrangling—removing duplicates, handling missing values, and standardizing formats—will help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story that’s easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you don’t need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wrangling—removing duplicates, handling missing values, and standardizing formats—will help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story that’s easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you don’t need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤2
Essential Skills Excel for Data Analysts 🚀
1️⃣ Data Cleaning & Transformation
Remove Duplicates – Ensure unique records.
Find & Replace – Quick data modifications.
Text Functions – TRIM, LEN, LEFT, RIGHT, MID, PROPER.
Data Validation – Restrict input values.
2️⃣ Data Analysis & Manipulation
Sorting & Filtering – Organize and extract key insights.
Conditional Formatting – Highlight trends, outliers.
Pivot Tables – Summarize large datasets efficiently.
Power Query – Automate data transformation.
3️⃣ Essential Formulas & Functions
Lookup Functions – VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH.
Logical Functions – IF, AND, OR, IFERROR, IFS.
Aggregation Functions – SUM, AVERAGE, MIN, MAX, COUNT, COUNTA.
Text Functions – CONCATENATE, TEXTJOIN, SUBSTITUTE.
4️⃣ Data Visualization
Charts & Graphs – Bar, Line, Pie, Scatter, Histogram.
Sparklines – Miniature charts inside cells.
Conditional Formatting – Color scales, data bars.
Dashboard Creation – Interactive and dynamic reports.
5️⃣ Advanced Excel Techniques
Array Formulas – Dynamic calculations with multiple values.
Power Pivot & DAX – Advanced data modeling.
What-If Analysis – Goal Seek, Scenario Manager.
Macros & VBA – Automate repetitive tasks.
6️⃣ Data Import & Export
CSV & TXT Files – Import and clean raw data.
Power Query – Connect to databases, web sources.
Exporting Reports – PDF, CSV, Excel formats.
Here you can find some free Excel books & useful resources: https://t.iss.one/excel_data
Hope it helps :)
#dataanalyst
1️⃣ Data Cleaning & Transformation
Remove Duplicates – Ensure unique records.
Find & Replace – Quick data modifications.
Text Functions – TRIM, LEN, LEFT, RIGHT, MID, PROPER.
Data Validation – Restrict input values.
2️⃣ Data Analysis & Manipulation
Sorting & Filtering – Organize and extract key insights.
Conditional Formatting – Highlight trends, outliers.
Pivot Tables – Summarize large datasets efficiently.
Power Query – Automate data transformation.
3️⃣ Essential Formulas & Functions
Lookup Functions – VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH.
Logical Functions – IF, AND, OR, IFERROR, IFS.
Aggregation Functions – SUM, AVERAGE, MIN, MAX, COUNT, COUNTA.
Text Functions – CONCATENATE, TEXTJOIN, SUBSTITUTE.
4️⃣ Data Visualization
Charts & Graphs – Bar, Line, Pie, Scatter, Histogram.
Sparklines – Miniature charts inside cells.
Conditional Formatting – Color scales, data bars.
Dashboard Creation – Interactive and dynamic reports.
5️⃣ Advanced Excel Techniques
Array Formulas – Dynamic calculations with multiple values.
Power Pivot & DAX – Advanced data modeling.
What-If Analysis – Goal Seek, Scenario Manager.
Macros & VBA – Automate repetitive tasks.
6️⃣ Data Import & Export
CSV & TXT Files – Import and clean raw data.
Power Query – Connect to databases, web sources.
Exporting Reports – PDF, CSV, Excel formats.
Here you can find some free Excel books & useful resources: https://t.iss.one/excel_data
Hope it helps :)
#dataanalyst
❤4
Essential Excel Concepts for Beginners
1. VLOOKUP: VLOOKUP is a popular Excel function used to search for a value in the first column of a table and return a corresponding value in the same row from another column. It is commonly used for data lookup and retrieval tasks.
2. Pivot Tables: Pivot tables are powerful tools in Excel for summarizing and analyzing large datasets. They allow you to reorganize and summarize data, perform calculations, and create interactive reports with ease.
3. Conditional Formatting: Conditional formatting allows you to format cells based on specific conditions or criteria. It helps highlight important information, identify trends, and make data more visually appealing and easier to interpret.
4. INDEX-MATCH: INDEX-MATCH is an alternative to VLOOKUP that combines the INDEX and MATCH functions to perform more flexible and powerful lookups in Excel. It is often preferred over VLOOKUP for its versatility and robustness.
5. Data Validation: Data validation is a feature in Excel that allows you to control what type of data can be entered into a cell. You can set rules, create drop-down lists, and provide error messages to ensure data accuracy and consistency.
6. SUMIF: SUMIF is a function in Excel that allows you to sum values in a range based on a specific condition or criteria. It is useful for calculating totals based on certain criteria without the need for complex formulas.
7. CONCATENATE: CONCATENATE is a function in Excel used to combine multiple text strings into one. It is helpful for creating custom labels, joining data from different cells, and formatting text in a desired way.
8. Goal Seek: Goal Seek is a built-in tool in Excel that allows you to find the input value needed to achieve a desired result in a formula. It is useful for performing reverse calculations and solving what-if scenarios.
9. Data Tables: Data tables in Excel allow you to perform sensitivity analysis by calculating multiple results based on different input values. They help you analyze how changing variables impact the final outcome of a formula.
10. Sparklines: Sparklines are small, simple charts that provide visual representations of data trends within a single cell. They are useful for quickly visualizing patterns and trends in data without the need for larger charts or graphs.
1. VLOOKUP: VLOOKUP is a popular Excel function used to search for a value in the first column of a table and return a corresponding value in the same row from another column. It is commonly used for data lookup and retrieval tasks.
2. Pivot Tables: Pivot tables are powerful tools in Excel for summarizing and analyzing large datasets. They allow you to reorganize and summarize data, perform calculations, and create interactive reports with ease.
3. Conditional Formatting: Conditional formatting allows you to format cells based on specific conditions or criteria. It helps highlight important information, identify trends, and make data more visually appealing and easier to interpret.
4. INDEX-MATCH: INDEX-MATCH is an alternative to VLOOKUP that combines the INDEX and MATCH functions to perform more flexible and powerful lookups in Excel. It is often preferred over VLOOKUP for its versatility and robustness.
5. Data Validation: Data validation is a feature in Excel that allows you to control what type of data can be entered into a cell. You can set rules, create drop-down lists, and provide error messages to ensure data accuracy and consistency.
6. SUMIF: SUMIF is a function in Excel that allows you to sum values in a range based on a specific condition or criteria. It is useful for calculating totals based on certain criteria without the need for complex formulas.
7. CONCATENATE: CONCATENATE is a function in Excel used to combine multiple text strings into one. It is helpful for creating custom labels, joining data from different cells, and formatting text in a desired way.
8. Goal Seek: Goal Seek is a built-in tool in Excel that allows you to find the input value needed to achieve a desired result in a formula. It is useful for performing reverse calculations and solving what-if scenarios.
9. Data Tables: Data tables in Excel allow you to perform sensitivity analysis by calculating multiple results based on different input values. They help you analyze how changing variables impact the final outcome of a formula.
10. Sparklines: Sparklines are small, simple charts that provide visual representations of data trends within a single cell. They are useful for quickly visualizing patterns and trends in data without the need for larger charts or graphs.
❤7
𝐉𝐮𝐧𝐢𝐨𝐫 𝐯𝐬. 𝐒𝐞𝐧𝐢𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭
What’s the real difference between Junior and Senior Data Analyst?
It’s not just SQL skills or years on the job — it’s how they think.
📚Juniors code right away
🧠Seniors figure out the problem first
Example: Juniors query without asking, Seniors check the goal.
📚Juniors follow orders
🧠Seniors ask questions
Example: Juniors build blindly, Seniors confirm metrics.
📚Juniors patch data
🧠Seniors fix the source
Example: Juniors fill gaps, Seniors debug the ETL.
📚Juniors stall in chaos
🧠Seniors make a plan
Example: Juniors wait, Seniors step up.
📚Juniors focus on tasks
🧠Seniors see the big picture
Example: Juniors report, Seniors connect to goals.
📚Juniors guess
🧠Seniors clarify
Example: Juniors assume, Seniors ask the team.
📚Juniors stick to old tools
🧠Seniors try new ones
Example: Juniors love Excel, Seniors code in Python.
📚Juniors give data
🧠Seniors give insights
Example: Juniors share stats, Seniors spot trends.
Seniority is about mindset, not just time.
What’s the real difference between Junior and Senior Data Analyst?
It’s not just SQL skills or years on the job — it’s how they think.
📚Juniors code right away
🧠Seniors figure out the problem first
Example: Juniors query without asking, Seniors check the goal.
📚Juniors follow orders
🧠Seniors ask questions
Example: Juniors build blindly, Seniors confirm metrics.
📚Juniors patch data
🧠Seniors fix the source
Example: Juniors fill gaps, Seniors debug the ETL.
📚Juniors stall in chaos
🧠Seniors make a plan
Example: Juniors wait, Seniors step up.
📚Juniors focus on tasks
🧠Seniors see the big picture
Example: Juniors report, Seniors connect to goals.
📚Juniors guess
🧠Seniors clarify
Example: Juniors assume, Seniors ask the team.
📚Juniors stick to old tools
🧠Seniors try new ones
Example: Juniors love Excel, Seniors code in Python.
📚Juniors give data
🧠Seniors give insights
Example: Juniors share stats, Seniors spot trends.
Seniority is about mindset, not just time.
❤5🍾1
Complete Syllabus for Data Analytics interview:
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting
- Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Hope it helps :)
SQL:
1. Basic
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Creating and using simple databases and tables
2. Intermediate
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries and nested queries
- Common Table Expressions (WITH clause)
- CASE statements for conditional logic in queries
3. Advanced
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Basic
- Syntax, variables, data types (integers, floats, strings, booleans)
- Control structures (if-else, for and while loops)
- Basic data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages
2. Pandas & Numpy
- Creating and manipulating DataFrames and Series
- Indexing, selecting, and filtering data
- Handling missing data (fillna, dropna)
- Data aggregation with groupby, summarizing data
- Merging, joining, and concatenating datasets
3. Basic Visualization
- Basic plotting with Matplotlib (line plots, bar plots, histograms)
- Visualization with Seaborn (scatter plots, box plots, pair plots)
- Customizing plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Basic
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Introduction to charts and basic data visualization
- Data sorting and filtering
- Conditional formatting
2. Intermediate
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- PivotTables and PivotCharts for summarizing data
- Data validation tools
- What-if analysis tools (Data Tables, Goal Seek)
3. Advanced
- Array formulas and advanced functions
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
- Dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from various sources
- Creating and managing relationships between different datasets
- Data modeling basics (star schema, snowflake schema)
2. Data Transformation
- Using Power Query for data cleaning and transformation
- Advanced data shaping techniques
- Calculated columns and measures using DAX
3. Data Visualization and Reporting
- Creating interactive reports and dashboards
- Visualizations (bar, line, pie charts, maps)
- Publishing and sharing reports, scheduling data refreshes
Statistics Fundamentals:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.
Hope it helps :)
❤7💋1
Here's how I would learn Microsoft Excel for data analysis fast if I had to start from zero:
1) I would ignore most Excel courses/tutorials.
I'm going to be honest here.
Most Excel educational content does not teach you how to analyze data.
In most organizations, Excel is "business process glue."
This is what most courses teach.
2) I would start with Excel tables.
For analysis, you must have tables where:
Each row is an analytical item of interest (e.g., customers, patients, claims, etc.).
Each column is an attribute of these items.
Learn tables.
3) I would learn only PivotTable fundamentals.
For data analysis, tables of any kind are good for:
1. Looking up exact values.
2. Comparing exact values.
PivotTables are great, but most professionals overuse them.
Learn PivotTable fundamentals and then move on.
4) Learn data visualization.
Humans are visual creatures.
So learn:
Histograms
Line charts
Bar charts
Line charts
To visually analyze data.
This is way more powerful than only using PivotTables.
BTW - The best use for PivotTables is to feed PivotCharts!
5) Learn Power Query.
If you're serious about analyzing data with Excel, do yourself a favor and learn Power Query.
PQ skills allow you to clean and transform your data in powerful ways.
It also automates this as a repeatable process.
Use PQ instead of convoluted formulas.
6) Expand your skillset.
When you're ready, it's time to learn specific analysis techniques to up your game:
RFM analysis
Logistic regression
Market basket analysis
K-means cluster analysis
Decision tree machine learning
Some of these you can implement using Solver.
Others require...
7) Python in Excel
Microsoft is including Python in Excel as part of Microsoft 365 subscriptions.
That effectively makes it free for millions of professionals.
Like Power Query, Python in Excel is for those serious about analyzing data with Excel.
1) I would ignore most Excel courses/tutorials.
I'm going to be honest here.
Most Excel educational content does not teach you how to analyze data.
In most organizations, Excel is "business process glue."
This is what most courses teach.
2) I would start with Excel tables.
For analysis, you must have tables where:
Each row is an analytical item of interest (e.g., customers, patients, claims, etc.).
Each column is an attribute of these items.
Learn tables.
3) I would learn only PivotTable fundamentals.
For data analysis, tables of any kind are good for:
1. Looking up exact values.
2. Comparing exact values.
PivotTables are great, but most professionals overuse them.
Learn PivotTable fundamentals and then move on.
4) Learn data visualization.
Humans are visual creatures.
So learn:
Histograms
Line charts
Bar charts
Line charts
To visually analyze data.
This is way more powerful than only using PivotTables.
BTW - The best use for PivotTables is to feed PivotCharts!
5) Learn Power Query.
If you're serious about analyzing data with Excel, do yourself a favor and learn Power Query.
PQ skills allow you to clean and transform your data in powerful ways.
It also automates this as a repeatable process.
Use PQ instead of convoluted formulas.
6) Expand your skillset.
When you're ready, it's time to learn specific analysis techniques to up your game:
RFM analysis
Logistic regression
Market basket analysis
K-means cluster analysis
Decision tree machine learning
Some of these you can implement using Solver.
Others require...
7) Python in Excel
Microsoft is including Python in Excel as part of Microsoft 365 subscriptions.
That effectively makes it free for millions of professionals.
Like Power Query, Python in Excel is for those serious about analyzing data with Excel.
❤7🤬1
Essential Excel Concepts for Beginners
1. VLOOKUP: VLOOKUP is a popular Excel function used to search for a value in the first column of a table and return a corresponding value in the same row from another column. It is commonly used for data lookup and retrieval tasks.
2. Pivot Tables: Pivot tables are powerful tools in Excel for summarizing and analyzing large datasets. They allow you to reorganize and summarize data, perform calculations, and create interactive reports with ease.
3. Conditional Formatting: Conditional formatting allows you to format cells based on specific conditions or criteria. It helps highlight important information, identify trends, and make data more visually appealing and easier to interpret.
4. INDEX-MATCH: INDEX-MATCH is an alternative to VLOOKUP that combines the INDEX and MATCH functions to perform more flexible and powerful lookups in Excel. It is often preferred over VLOOKUP for its versatility and robustness.
5. Data Validation: Data validation is a feature in Excel that allows you to control what type of data can be entered into a cell. You can set rules, create drop-down lists, and provide error messages to ensure data accuracy and consistency.
6. SUMIF: SUMIF is a function in Excel that allows you to sum values in a range based on a specific condition or criteria. It is useful for calculating totals based on certain criteria without the need for complex formulas.
7. CONCATENATE: CONCATENATE is a function in Excel used to combine multiple text strings into one. It is helpful for creating custom labels, joining data from different cells, and formatting text in a desired way.
8. Goal Seek: Goal Seek is a built-in tool in Excel that allows you to find the input value needed to achieve a desired result in a formula. It is useful for performing reverse calculations and solving what-if scenarios.
9. Data Tables: Data tables in Excel allow you to perform sensitivity analysis by calculating multiple results based on different input values. They help you analyze how changing variables impact the final outcome of a formula.
10. Sparklines: Sparklines are small, simple charts that provide visual representations of data trends within a single cell. They are useful for quickly visualizing patterns and trends in data without the need for larger charts or graphs.
1. VLOOKUP: VLOOKUP is a popular Excel function used to search for a value in the first column of a table and return a corresponding value in the same row from another column. It is commonly used for data lookup and retrieval tasks.
2. Pivot Tables: Pivot tables are powerful tools in Excel for summarizing and analyzing large datasets. They allow you to reorganize and summarize data, perform calculations, and create interactive reports with ease.
3. Conditional Formatting: Conditional formatting allows you to format cells based on specific conditions or criteria. It helps highlight important information, identify trends, and make data more visually appealing and easier to interpret.
4. INDEX-MATCH: INDEX-MATCH is an alternative to VLOOKUP that combines the INDEX and MATCH functions to perform more flexible and powerful lookups in Excel. It is often preferred over VLOOKUP for its versatility and robustness.
5. Data Validation: Data validation is a feature in Excel that allows you to control what type of data can be entered into a cell. You can set rules, create drop-down lists, and provide error messages to ensure data accuracy and consistency.
6. SUMIF: SUMIF is a function in Excel that allows you to sum values in a range based on a specific condition or criteria. It is useful for calculating totals based on certain criteria without the need for complex formulas.
7. CONCATENATE: CONCATENATE is a function in Excel used to combine multiple text strings into one. It is helpful for creating custom labels, joining data from different cells, and formatting text in a desired way.
8. Goal Seek: Goal Seek is a built-in tool in Excel that allows you to find the input value needed to achieve a desired result in a formula. It is useful for performing reverse calculations and solving what-if scenarios.
9. Data Tables: Data tables in Excel allow you to perform sensitivity analysis by calculating multiple results based on different input values. They help you analyze how changing variables impact the final outcome of a formula.
10. Sparklines: Sparklines are small, simple charts that provide visual representations of data trends within a single cell. They are useful for quickly visualizing patterns and trends in data without the need for larger charts or graphs.
❤1🔥1
Excel Hack of the Week—super simple and super useful! 😎
🧹 Remove Duplicates in Seconds!
1️⃣ Select your data range.
2️⃣ Go to Data > Remove Duplicates.
3️⃣ Pick the columns to check for duplicates and hit OK—done!
🔍 Example:
✅ Got a list of emails with repeats? Remove Duplicates keeps only unique ones!
✅ Cleaning up sales data? Instantly get rid of double entries!
📌 Bonus: Use this trick to tidy up contact lists, inventory records, or survey responses—no formulas needed!
Like this post if you want more Excel and data hacks every week! 👍✨
Credits: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
🧹 Remove Duplicates in Seconds!
1️⃣ Select your data range.
2️⃣ Go to Data > Remove Duplicates.
3️⃣ Pick the columns to check for duplicates and hit OK—done!
🔍 Example:
✅ Got a list of emails with repeats? Remove Duplicates keeps only unique ones!
✅ Cleaning up sales data? Instantly get rid of double entries!
📌 Bonus: Use this trick to tidy up contact lists, inventory records, or survey responses—no formulas needed!
Like this post if you want more Excel and data hacks every week! 👍✨
Credits: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
❤11
50 essential Excel formulas
SUM: =SUM(A1:A5)
AVERAGE: =AVERAGE(A1:A10)
VLOOKUP: =VLOOKUP(B1, A2:D10, 3, FALSE)
IF: =IF(A1 > 10, "Yes", "No")
CONCATENATE (or CONCAT): =CONCATENATE(A1, " ", B1)
COUNT: =COUNT(A1:A10)
MAX: =MAX(A1:A10)
MIN: =MIN(A1:A10)
ROUND: =ROUND(A1, 2)
TRIM: =TRIM(A1)
LOWER: =LOWER(A1)
UPPER: =UPPER(A1)
LEFT: =LEFT(A1, 5)
RIGHT: =RIGHT(A1, 5)
MID: =MID(A1, 2, 3)
LEN: =LEN(A1)
FIND: =FIND("search_text", A1)
REPLACE: =REPLACE(A1, 3, 2, "new_text")
SUBSTITUTE: =SUBSTITUTE(A1, "old_text", "new_text")
INDEX: =INDEX(A1:A10, 3)
MATCH: =MATCH(B1, A1:A10, 0)
OFFSET: =OFFSET(A1, 1, 2)
SUMIF: =SUMIF(A1:A10, ">5")
COUNTIF: =COUNTIF(A1:A10, "apple")
AVERAGEIF: =AVERAGEIF(A1:A10, "<>0")
SUMIFS: =SUMIFS(A1:A10, B1:B10, "apple", C1:C10, ">5")
COUNTIFS: =COUNTIFS(A1:A10, ">5", B1:B10, "apple")
AVERAGEIFS: =AVERAGEIFS(A1:A10, B1:B10, "apple", C1:C10, ">5")
IFERROR: =IFERROR(A1/B1, "Error")
AND: =AND(A1>5, A1<10)
OR: =OR(A1="apple", A1="banana")
NOT: =NOT(A1="apple")
DATE: =DATE(2022, 12, 31)
TODAY: =TODAY()
NOW: =NOW()
DATEDIF: =DATEDIF(A1, A2, "D")
YEAR: =YEAR(A1)
MONTH: =MONTH(A1)
DAY: =DAY(A1)
EOMONTH: =EOMONTH(A1, 3)
NETWORKDAYS: =NETWORKDAYS(A1, A2)
WEEKDAY: =WEEKDAY(A1)
HLOOKUP: =HLOOKUP(B1, A1:D10, 3, FALSE)
MATCH: =MATCH(B1, A1:A10, 0)
INDEX-MATCH: =INDEX(A1:A10, MATCH(B1, C1:C10, 0))
TRANSPOSE: =TRANSPOSE(A1:D10)
PIVOT TABLE: =PIVOT_TABLE(A1:D10, "Sales", "Region", "Sum")
RANK: =RANK(A1, A1:A10, 1)
RAND: =RAND()
CHOOSE: =CHOOSE(B1, "Option 1", "Option 2", "Option 3")
Share our channel link with your true friends: https://t.iss.one/excel_analyst
Hope this helps you 😊
SUM: =SUM(A1:A5)
AVERAGE: =AVERAGE(A1:A10)
VLOOKUP: =VLOOKUP(B1, A2:D10, 3, FALSE)
IF: =IF(A1 > 10, "Yes", "No")
CONCATENATE (or CONCAT): =CONCATENATE(A1, " ", B1)
COUNT: =COUNT(A1:A10)
MAX: =MAX(A1:A10)
MIN: =MIN(A1:A10)
ROUND: =ROUND(A1, 2)
TRIM: =TRIM(A1)
LOWER: =LOWER(A1)
UPPER: =UPPER(A1)
LEFT: =LEFT(A1, 5)
RIGHT: =RIGHT(A1, 5)
MID: =MID(A1, 2, 3)
LEN: =LEN(A1)
FIND: =FIND("search_text", A1)
REPLACE: =REPLACE(A1, 3, 2, "new_text")
SUBSTITUTE: =SUBSTITUTE(A1, "old_text", "new_text")
INDEX: =INDEX(A1:A10, 3)
MATCH: =MATCH(B1, A1:A10, 0)
OFFSET: =OFFSET(A1, 1, 2)
SUMIF: =SUMIF(A1:A10, ">5")
COUNTIF: =COUNTIF(A1:A10, "apple")
AVERAGEIF: =AVERAGEIF(A1:A10, "<>0")
SUMIFS: =SUMIFS(A1:A10, B1:B10, "apple", C1:C10, ">5")
COUNTIFS: =COUNTIFS(A1:A10, ">5", B1:B10, "apple")
AVERAGEIFS: =AVERAGEIFS(A1:A10, B1:B10, "apple", C1:C10, ">5")
IFERROR: =IFERROR(A1/B1, "Error")
AND: =AND(A1>5, A1<10)
OR: =OR(A1="apple", A1="banana")
NOT: =NOT(A1="apple")
DATE: =DATE(2022, 12, 31)
TODAY: =TODAY()
NOW: =NOW()
DATEDIF: =DATEDIF(A1, A2, "D")
YEAR: =YEAR(A1)
MONTH: =MONTH(A1)
DAY: =DAY(A1)
EOMONTH: =EOMONTH(A1, 3)
NETWORKDAYS: =NETWORKDAYS(A1, A2)
WEEKDAY: =WEEKDAY(A1)
HLOOKUP: =HLOOKUP(B1, A1:D10, 3, FALSE)
MATCH: =MATCH(B1, A1:A10, 0)
INDEX-MATCH: =INDEX(A1:A10, MATCH(B1, C1:C10, 0))
TRANSPOSE: =TRANSPOSE(A1:D10)
PIVOT TABLE: =PIVOT_TABLE(A1:D10, "Sales", "Region", "Sum")
RANK: =RANK(A1, A1:A10, 1)
RAND: =RAND()
CHOOSE: =CHOOSE(B1, "Option 1", "Option 2", "Option 3")
Share our channel link with your true friends: https://t.iss.one/excel_analyst
Hope this helps you 😊
❤6🔥5👍1
If I need to teach someone data analytics from the basics, here is my strategy:
1. I will first remove the fear of tools from that person
2. i will start with the excel because it looks familiar and easy to use
3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things
4. I will release the person from the tutorial hell and move into a more action oriented person
5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily
6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance
7. It helps the person to develop the analytical thinking
8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life
9. Then I move the person to power bi to do again 5 projects by using either sql or excel files
10. Now the fear is removed.
11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills
12. Further it helps you to clear case study round given by most of the companies
13. Now i help the person how to present them in resume and also how these tools are used in real world.
14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos.
15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not.
16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
1. I will first remove the fear of tools from that person
2. i will start with the excel because it looks familiar and easy to use
3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things
4. I will release the person from the tutorial hell and move into a more action oriented person
5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily
6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance
7. It helps the person to develop the analytical thinking
8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life
9. Then I move the person to power bi to do again 5 projects by using either sql or excel files
10. Now the fear is removed.
11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills
12. Further it helps you to clear case study round given by most of the companies
13. Now i help the person how to present them in resume and also how these tools are used in real world.
14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos.
15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not.
16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
❤4
For data analysts, mastering these top 10 Excel concepts is crucial:
1. Formulas and Functions: Understand basic to advanced functions like SUM, AVERAGE, VLOOKUP, INDEX-MATCH, and IF statements.
2. PivotTables: Learn to summarize, analyze, and visualize data efficiently using PivotTables.
3. Data Cleaning and Formatting: Familiarize yourself with tools and techniques for cleaning and formatting messy data, such as text-to-columns, remove duplicates, and conditional formatting.
4. Charts and Graphs: Explore various chart types (e.g., bar, line, scatter) and understand when to use each for effective data visualization.
5. Data Validation: Implement data validation rules to ensure data integrity and accuracy, such as drop-down lists and input restrictions.
6. Data Analysis Tools: Utilize Excel's built-in data analysis tools like Goal Seek, Solver, and Data Tables for scenario analysis and optimization.
7. Conditional Formatting: Apply formatting based on specific conditions to highlight trends, outliers, or anomalies in data.
8. Named Ranges: Organize data efficiently by assigning meaningful names to ranges, making formulas more readable and easier to manage.
9. Data Tables and What-If Analysis: Use data tables to perform sensitivity analysis and scenario modeling for decision-making.
10. Power Query and Power Pivot: Explore advanced data manipulation and analysis capabilities using Excel's Power Query for data extraction, transformation, and loading (ETL) and Power Pivot for data modeling and analysis.
Give credits while sharing: https://t.iss.one/excel_analyst
ENJOY LEARNING 👍👍
1. Formulas and Functions: Understand basic to advanced functions like SUM, AVERAGE, VLOOKUP, INDEX-MATCH, and IF statements.
2. PivotTables: Learn to summarize, analyze, and visualize data efficiently using PivotTables.
3. Data Cleaning and Formatting: Familiarize yourself with tools and techniques for cleaning and formatting messy data, such as text-to-columns, remove duplicates, and conditional formatting.
4. Charts and Graphs: Explore various chart types (e.g., bar, line, scatter) and understand when to use each for effective data visualization.
5. Data Validation: Implement data validation rules to ensure data integrity and accuracy, such as drop-down lists and input restrictions.
6. Data Analysis Tools: Utilize Excel's built-in data analysis tools like Goal Seek, Solver, and Data Tables for scenario analysis and optimization.
7. Conditional Formatting: Apply formatting based on specific conditions to highlight trends, outliers, or anomalies in data.
8. Named Ranges: Organize data efficiently by assigning meaningful names to ranges, making formulas more readable and easier to manage.
9. Data Tables and What-If Analysis: Use data tables to perform sensitivity analysis and scenario modeling for decision-making.
10. Power Query and Power Pivot: Explore advanced data manipulation and analysis capabilities using Excel's Power Query for data extraction, transformation, and loading (ETL) and Power Pivot for data modeling and analysis.
Give credits while sharing: https://t.iss.one/excel_analyst
ENJOY LEARNING 👍👍
❤4
Excel tips to help in interviews! 🌟
1. Practice core features: Be comfortable with formulas (SUM, IF, VLOOKUP), pivot tables, charts, and conditional formatting.
2. Know your references: Understand the difference between relative, absolute, and mixed cell references—they often come up.
3. Showcase real examples: Be ready to explain how you’ve used Excel to solve problems or improve processes, like automating reports with macros or cleaning data with filters.
4. Data validation & error handling: Mention using data validation to restrict inputs and functions like COUNTIF or ISBLANK to spot errors or missing data.
5. Communicate clearly: Practice explaining complex Excel data or dashboards in simple terms for non-technical audiences.
6. Stay updated: Mention any recent Excel features you know, like XLOOKUP or Power Query, to show you’re keeping up.
React ❤️ for more
1. Practice core features: Be comfortable with formulas (SUM, IF, VLOOKUP), pivot tables, charts, and conditional formatting.
2. Know your references: Understand the difference between relative, absolute, and mixed cell references—they often come up.
3. Showcase real examples: Be ready to explain how you’ve used Excel to solve problems or improve processes, like automating reports with macros or cleaning data with filters.
4. Data validation & error handling: Mention using data validation to restrict inputs and functions like COUNTIF or ISBLANK to spot errors or missing data.
5. Communicate clearly: Practice explaining complex Excel data or dashboards in simple terms for non-technical audiences.
6. Stay updated: Mention any recent Excel features you know, like XLOOKUP or Power Query, to show you’re keeping up.
React ❤️ for more
❤8
Step-by-step guide to become a Data Analyst in 2025—📊
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React ❤️ for more
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React ❤️ for more
❤5
Here's a short roadmap to crack an IT job with a non-CS background 🚀
1. 📚 Learn basics of CS and programming.
2. 🎯 Choose a specialization (e.g., web dev, data analysis).
3. 🏆 Complete online courses and certifications.
4. 🛠️ Build a portfolio of projects.
5. 🤝 Network with professionals.
6. 💼 Seek internships for experience.
7. 📚 Keep learning and stay updated.
8. 🧠 Develop soft skills.
9. 📝 Prepare for interviews.
10. 💪 Stay persistent and positive! Good luck!
React to This Message so I share Content like this ❤️
1. 📚 Learn basics of CS and programming.
2. 🎯 Choose a specialization (e.g., web dev, data analysis).
3. 🏆 Complete online courses and certifications.
4. 🛠️ Build a portfolio of projects.
5. 🤝 Network with professionals.
6. 💼 Seek internships for experience.
7. 📚 Keep learning and stay updated.
8. 🧠 Develop soft skills.
9. 📝 Prepare for interviews.
10. 💪 Stay persistent and positive! Good luck!
React to This Message so I share Content like this ❤️
❤10
7 Must-Have Tools for Data Analysts in 2025:
✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.
Perfect blend of code, visuals, and storytelling.
React with ❤️ for free tutorials on each tool
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.
Perfect blend of code, visuals, and storytelling.
React with ❤️ for free tutorials on each tool
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤4
How to create a new workbook
1. Do any of the following:
• If Excel is not running, start Excel, and then on the Start screen, double-click
Blank workbook.
• If Excel is already running, click the File tab of the ribbon, click New to display the New page of the Backstage view, and then double-click Blank workbook.
• If Excel is already running, press Ctrl+N.
To save a workbook under a new name or in a new location
1. Display the Backstage view, and then click Save As.
2. On the Save As page of the Backstage view, click the folder where you want to save the workbook.
3. In the Save As dialog box, in the File name box, enter a new name for the workbook.
4. To save the file in a different format, in the Save as type list, click a new file type.
5. If necessary, use the navigation controls to move to a new folder.
6. Click Save.
1. Do any of the following:
• If Excel is not running, start Excel, and then on the Start screen, double-click
Blank workbook.
• If Excel is already running, click the File tab of the ribbon, click New to display the New page of the Backstage view, and then double-click Blank workbook.
• If Excel is already running, press Ctrl+N.
To save a workbook under a new name or in a new location
1. Display the Backstage view, and then click Save As.
2. On the Save As page of the Backstage view, click the folder where you want to save the workbook.
3. In the Save As dialog box, in the File name box, enter a new name for the workbook.
4. To save the file in a different format, in the Save as type list, click a new file type.
5. If necessary, use the navigation controls to move to a new folder.
6. Click Save.
❤5
Top Excel Formulas Every Data Analyst Should Know
SUM():
Purpose: Adds up a range of numbers.
Example: =SUM(A1:A10)
AVERAGE():
Purpose: Calculates the average of a range of numbers.
Example: =AVERAGE(B1:B10)
COUNT():
Purpose: Counts the number of cells containing numbers.
Example: =COUNT(C1:C10)
IF():
Purpose: Returns one value if a condition is true, and another if false.
Example: =IF(A1 > 10, "Yes", "No")
VLOOKUP():
Purpose: Searches for a value in the first column and returns a value in the same row from another column.
Example: =VLOOKUP(D1, A1:B10, 2, FALSE)
HLOOKUP():
Purpose: Searches for a value in the first row and returns a value in the same column from another row.
Example: =HLOOKUP("Sales", A1:F5, 3, FALSE)
INDEX():
Purpose: Returns the value of a cell based on row and column numbers.
Example: =INDEX(A1:C10, 2, 3)
MATCH():
Purpose: Searches for a value and returns its position in a range.
Example: =MATCH("Product B", A1:A10, 0)
CONCATENATE() or CONCAT():
Purpose: Joins multiple text strings into one.
Example: =CONCATENATE(A1, " ", B1)
TEXT():
Purpose: Formats numbers or dates as text.
Example: =TEXT(A1, "dd/mm/yyyy")
Excel Resources: t.iss.one/excel_data
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
SUM():
Purpose: Adds up a range of numbers.
Example: =SUM(A1:A10)
AVERAGE():
Purpose: Calculates the average of a range of numbers.
Example: =AVERAGE(B1:B10)
COUNT():
Purpose: Counts the number of cells containing numbers.
Example: =COUNT(C1:C10)
IF():
Purpose: Returns one value if a condition is true, and another if false.
Example: =IF(A1 > 10, "Yes", "No")
VLOOKUP():
Purpose: Searches for a value in the first column and returns a value in the same row from another column.
Example: =VLOOKUP(D1, A1:B10, 2, FALSE)
HLOOKUP():
Purpose: Searches for a value in the first row and returns a value in the same column from another row.
Example: =HLOOKUP("Sales", A1:F5, 3, FALSE)
INDEX():
Purpose: Returns the value of a cell based on row and column numbers.
Example: =INDEX(A1:C10, 2, 3)
MATCH():
Purpose: Searches for a value and returns its position in a range.
Example: =MATCH("Product B", A1:A10, 0)
CONCATENATE() or CONCAT():
Purpose: Joins multiple text strings into one.
Example: =CONCATENATE(A1, " ", B1)
TEXT():
Purpose: Formats numbers or dates as text.
Example: =TEXT(A1, "dd/mm/yyyy")
Excel Resources: t.iss.one/excel_data
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
❤4
Essential Excel Functions for Data Analysts 🚀
1️⃣ Basic Functions
SUM() – Adds a range of numbers. =SUM(A1:A10)
AVERAGE() – Calculates the average. =AVERAGE(A1:A10)
MIN() / MAX() – Finds the smallest/largest value. =MIN(A1:A10)
2️⃣ Logical Functions
IF() – Conditional logic. =IF(A1>50, "Pass", "Fail")
IFS() – Multiple conditions. =IFS(A1>90, "A", A1>80, "B", TRUE, "C")
AND() / OR() – Checks multiple conditions. =AND(A1>50, B1<100)
3️⃣ Text Functions
LEFT() / RIGHT() / MID() – Extract text from a string.
=LEFT(A1, 3) (First 3 characters)
=MID(A1, 3, 2) (2 characters from the 3rd position)
LEN() – Counts characters. =LEN(A1)
TRIM() – Removes extra spaces. =TRIM(A1)
UPPER() / LOWER() / PROPER() – Changes text case.
4️⃣ Lookup Functions
VLOOKUP() – Searches for a value in a column.
=VLOOKUP(1001, A2:B10, 2, FALSE)
HLOOKUP() – Searches in a row.
XLOOKUP() – Advanced lookup replacing VLOOKUP.
=XLOOKUP(1001, A2:A10, B2:B10, "Not Found")
5️⃣ Date & Time Functions
TODAY() – Returns the current date.
NOW() – Returns the current date and time.
YEAR(), MONTH(), DAY() – Extracts parts of a date.
DATEDIF() – Calculates the difference between two dates.
6️⃣ Data Cleaning Functions
REMOVE DUPLICATES – Found in the "Data" tab.
CLEAN() – Removes non-printable characters.
SUBSTITUTE() – Replaces text within a string.
=SUBSTITUTE(A1, "old", "new")
7️⃣ Advanced Functions
INDEX() & MATCH() – More flexible alternative to VLOOKUP.
TEXTJOIN() – Joins text with a delimiter.
UNIQUE() – Returns unique values from a range.
FILTER() – Filters data dynamically.
=FILTER(A2:B10, B2:B10>50)
8️⃣ Pivot Tables & Power Query
PIVOT TABLES – Summarizes data dynamically.
GETPIVOTDATA() – Extracts data from a Pivot Table.
POWER QUERY – Automates data cleaning & transformation.
You can find Free Excel Resources here: https://t.iss.one/excel_data
Hope it helps :)
#dataanalytics
1️⃣ Basic Functions
SUM() – Adds a range of numbers. =SUM(A1:A10)
AVERAGE() – Calculates the average. =AVERAGE(A1:A10)
MIN() / MAX() – Finds the smallest/largest value. =MIN(A1:A10)
2️⃣ Logical Functions
IF() – Conditional logic. =IF(A1>50, "Pass", "Fail")
IFS() – Multiple conditions. =IFS(A1>90, "A", A1>80, "B", TRUE, "C")
AND() / OR() – Checks multiple conditions. =AND(A1>50, B1<100)
3️⃣ Text Functions
LEFT() / RIGHT() / MID() – Extract text from a string.
=LEFT(A1, 3) (First 3 characters)
=MID(A1, 3, 2) (2 characters from the 3rd position)
LEN() – Counts characters. =LEN(A1)
TRIM() – Removes extra spaces. =TRIM(A1)
UPPER() / LOWER() / PROPER() – Changes text case.
4️⃣ Lookup Functions
VLOOKUP() – Searches for a value in a column.
=VLOOKUP(1001, A2:B10, 2, FALSE)
HLOOKUP() – Searches in a row.
XLOOKUP() – Advanced lookup replacing VLOOKUP.
=XLOOKUP(1001, A2:A10, B2:B10, "Not Found")
5️⃣ Date & Time Functions
TODAY() – Returns the current date.
NOW() – Returns the current date and time.
YEAR(), MONTH(), DAY() – Extracts parts of a date.
DATEDIF() – Calculates the difference between two dates.
6️⃣ Data Cleaning Functions
REMOVE DUPLICATES – Found in the "Data" tab.
CLEAN() – Removes non-printable characters.
SUBSTITUTE() – Replaces text within a string.
=SUBSTITUTE(A1, "old", "new")
7️⃣ Advanced Functions
INDEX() & MATCH() – More flexible alternative to VLOOKUP.
TEXTJOIN() – Joins text with a delimiter.
UNIQUE() – Returns unique values from a range.
FILTER() – Filters data dynamically.
=FILTER(A2:B10, B2:B10>50)
8️⃣ Pivot Tables & Power Query
PIVOT TABLES – Summarizes data dynamically.
GETPIVOTDATA() – Extracts data from a Pivot Table.
POWER QUERY – Automates data cleaning & transformation.
You can find Free Excel Resources here: https://t.iss.one/excel_data
Hope it helps :)
#dataanalytics
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