To unleash the power of AI
Technologies like Copilot in Excel
👇
https://daveondata.com/newsletter
Copilot in Excel does an excellent job of using column names to understand the nature of your data.
So I decided to test Copilot in Excel using column names like:
Column1
Column2
Column3
Here's what happened...
Copilot in Excel does an excellent job of using column names to understand the nature of your data.
So I decided to test Copilot in Excel using column names like:
Column1
Column2
Column3
Here's what happened...
Copilot in Excel does an excellent job of using column names to understand the nature of your data.
So I decided to test Copilot in Excel using column names like:
Column1
Column2
Column3
Here's what happened...
Leaders aren't excited about this in 2025:
- Using PivotTables
- Knowing many functions
You know what they're excited about:
AI
And that means Copilot in Excel.
Here are 4 ways to build skills to impress your leaders:
Leaders aren't excited about this in 2025:
- Using PivotTables
- Knowing many functions
You know what they're excited about:
AI
And that means Copilot in Excel.
Here are 4 ways to build skills to impress your leaders:
Don't overcomplicate it.
Use logistic regression.
It's powerful, interpretable, and works great with real business data.
Start with Python in Excel.
Don't overcomplicate it.
Use logistic regression.
It's powerful, interpretable, and works great with real business data.
Start with Python in Excel.
Here's the mistake your executives are probably making:
To make the most out of AI in Microsoft Excel, you need to know 2 things:
1 - Python
2 - DIY data science
Copilot in Excel relies heavily on Python.
👇
Here's the mistake your executives are probably making:
To make the most out of AI in Microsoft Excel, you need to know 2 things:
1 - Python
2 - DIY data science
Copilot in Excel relies heavily on Python.
👇
That's the real progression for DIY data science.
You don't jump to neural networks.
You grow step by step.
From formulas to forecasts.
That's the real progression for DIY data science.
You don't jump to neural networks.
You grow step by step.
From formulas to forecasts.
ML with Python in Excel.
It will be the first in a series of tutorials showing how Microsoft Excel is the platform for DIY data science.
Want in?
Click the link in my profile to get started.
ML with Python in Excel.
It will be the first in a series of tutorials showing how Microsoft Excel is the platform for DIY data science.
Want in?
Click the link in my profile to get started.
That's the future Microsoft envisions.
Here's what Microsoft won't tell your executives.
If you can't prompt it well, you're playing with 🔥.
Learn enough Python to guide the AI...
And leave 99% of Excel users in your dust.
That's the future Microsoft envisions.
Here's what Microsoft won't tell your executives.
If you can't prompt it well, you're playing with 🔥.
Learn enough Python to guide the AI...
And leave 99% of Excel users in your dust.
Here’s the biggest mistake you can make in 2025:
Ignoring Python in Excel.
Here's why:
Here’s the biggest mistake you can make in 2025:
Ignoring Python in Excel.
Here's why:
Formulas = pain
SQL = elegant
Learn JOIN, GROUP BY, CASE WHEN
Drop the SQL into Power Query.
Your Excel workflow will never be the same.
Formulas = pain
SQL = elegant
Learn JOIN, GROUP BY, CASE WHEN
Drop the SQL into Power Query.
Your Excel workflow will never be the same.
Here are 5 reasons why (with business scenarios):
Here are 5 reasons why (with business scenarios):
Cluster analysis will.
Tools like k-means help you group customers by many behaviors.
So many behaviors it makes PivotTables cry.
Try it with Python in Excel.
It's like discovering new tribes in your data.
Cluster analysis will.
Tools like k-means help you group customers by many behaviors.
So many behaviors it makes PivotTables cry.
Try it with Python in Excel.
It's like discovering new tribes in your data.
But Python in Excel is the biggest upgrade to the tool in 30+ years.
Here’s why it changes everything for people who live in spreadsheets:
But Python in Excel is the biggest upgrade to the tool in 30+ years.
Here’s why it changes everything for people who live in spreadsheets:
Analysis tells you why.
Prediction tells you what's next.
Most professionals stop at the first one.
Want more impact?
Learn the other two.
Analysis tells you why.
Prediction tells you what's next.
Most professionals stop at the first one.
Want more impact?
Learn the other two.
It’s not formulas.
It’s not macros.
It’s Power Query.
Here’s why Power Query is your secret weapon for doing real analytics in Excel:
It’s not formulas.
It’s not macros.
It’s Power Query.
Here’s why Power Query is your secret weapon for doing real analytics in Excel:
But SQL lets you:
- Query millions of rows
- Filter like a data whisperer
- Prep data BEFORE it hits Excel
SQL + Excel == next-level spreadsheets.
And now with Python in Excel?
It's a must-learn.
But SQL lets you:
- Query millions of rows
- Filter like a data whisperer
- Prep data BEFORE it hits Excel
SQL + Excel == next-level spreadsheets.
And now with Python in Excel?
It's a must-learn.
But what if you just want to solve real business problems?
Here’s the 5-step path I teach working professionals:
But what if you just want to solve real business problems?
Here’s the 5-step path I teach working professionals:
It’s this:
- Profile your data
- Engineer useful features
- Use decision trees
ML isn’t magic.
It’s just pattern-finding + good inputs.
Start with intuition.
Scale with trees.
It’s this:
- Profile your data
- Engineer useful features
- Use decision trees
ML isn’t magic.
It’s just pattern-finding + good inputs.
Start with intuition.
Scale with trees.
It’s a full-blown data stack.
But most users are stuck in 2007.
Here’s how you become a modern Excel power user in 2025:
It’s a full-blown data stack.
But most users are stuck in 2007.
Here’s how you become a modern Excel power user in 2025:
I've been doing analytics for 13 years.
Here are 6 hard-won lessons I've learned when using SQL for DIY data science:
I've been doing analytics for 13 years.
Here are 6 hard-won lessons I've learned when using SQL for DIY data science:
PivotTable to count items by category?
In Python:
table.groupby('category').size()
Boom!
Instant PivotTable in one line.
PivotTable to count items by category?
In Python:
table.groupby('category').size()
Boom!
Instant PivotTable in one line.
A better way to look at it is this:
Microsoft Excel is now the ultimate choose-your-own-adventure analytics tool.
Building data analysis skills with Excel is like visiting various stops on a wandering journey...
A better way to look at it is this:
Microsoft Excel is now the ultimate choose-your-own-adventure analytics tool.
Building data analysis skills with Excel is like visiting various stops on a wandering journey...
Need to remove duplicates in Excel?
You'd use Data -> Remove Duplicates.
In SQL:
SELECT DISTINCT customer_id
FROM transactions
DISTINCT shows you unique values.
No clicks needed.
**AND**
You've automated it for reproducibility.
Need to remove duplicates in Excel?
You'd use Data -> Remove Duplicates.
In SQL:
SELECT DISTINCT customer_id
FROM transactions
DISTINCT shows you unique values.
No clicks needed.
**AND**
You've automated it for reproducibility.
While you can wrangle data using Python formulas, here are good reasons to prefer Power Query to Python.
If you're a "Pythonista," this opening might anger you.
Please read on before 🔥 me...
While you can wrangle data using Python formulas, here are good reasons to prefer Power Query to Python.
If you're a "Pythonista," this opening might anger you.
Please read on before 🔥 me...
Here's the "why" in two words:
Power
Flexibility
First, let's talk about Python in Excel.
Here's the "why" in two words:
Power
Flexibility
First, let's talk about Python in Excel.
Need a new "level" column on a table with logic like:
=IF(C2 > 1000, "High", "Low")
In Python:
table['level'] = np.where(table['sales'] > 1000, 'High', 'Low')
Your Excel knowledge can unlock the power of Python.
Need a new "level" column on a table with logic like:
=IF(C2 > 1000, "High", "Low")
In Python:
table['level'] = np.where(table['sales'] > 1000, 'High', 'Low')
Your Excel knowledge can unlock the power of Python.