10 must-have Excel skills everyone should know: 📊

Sparklines:

One of my personal favorites.

Sparklines allow you to insert miniature graphs inside an individual cell to easily visualize data.

•Select range
•Insert ⇢ Sparkline
• Choose type
People Graph:

I'm a big fan of visual representations of data.

Using 'People Graph' is an easy way to liven up your spreadsheet, especially when tracking metrics related to, well, humans.

Create an infographic to visualize social media engagement, app users, customers, & more.
Tables:

If you're not using tables in Excel, you should be.

• Clean formatting
• Auto-expand as new data is added
• Total row quick and easy data summaries
• Formulas carry through entire columns
• Create dynamic charts that update automatically & much more.

Here's how:
Slicer:

Now that you're using tables, let's insert one of my favorite filtering tools.

The Slicer

•Click anywhere inside a table
•Go to table ⇢ insert slicer
•Select the fields you want

A dialog box will appear.

Click any slicer button & data is filtered accordingly.
Data Validation:

If you've spent any time working in spreadsheets at all you know one thing:

They're only as good as the data entered.

My favorite way to ensure consistency & accuracy is Drop Down lists.

It's easy & guarantees the data will be entered correctly––every time.
Flash Fill:

Let's say you've got all the data you need––it just isn't in the right place.

"Flash Fill" is your friend.

Quickly extract pieces of text from one cell and move them to another OR combine text from multiple cells into a single place.
Consolidate:

Take data from multiple tabs.

Consolidate into one.

Sum, average, count, it's your choice.

By creating links to the source data, your consolidation tab will update automatically.

The best part? No formulas required.
Automatic Abbreviations:

If you're like me, autocorrect is more of an annoyance than a help.

Let's change that.

Set up your own custom list of abbreviations, and bend its powers to your will.

Type 'TBD'
Press 'Enter"
Output = 'To be determined'
Conditional Formatting:

As you can tell, I'm a big fan of visualizing data.

Conditional formatting is one of the best (and easiest) ways to do this.

Let's face it, if your data looks good, you look good.

Spice it up by adding data bars, heat maps, or icon sets to show trends:
Format Paint:

This one will save you time & bring consistency to your worksheets.

With Format paint, you can copy formatting with the click of a button.

•Select the cells you'd like to copy
•Click the paintbrush
•Select where you want the formatting to go.

It's that easy.
Bonus: Camera Snapshot

Here's a little-known trick to move data between workbooks.

First, add "camera" to your quick access toolbar.

•Select data ⇢ click 📷
•Paste to new workbook

What's cool?: When you update the main workbook, the data in the picture updates as well! 🤯
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More from Blake Burge đź’ˇ

More from All

How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)

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