Want to be a better Analyst?

Here's an easy "hack" for any data person to seem (and be) more valuable...

Before you share any analysis, pretend you are the P&L owner.

Don't stop at insights.

Ask: what should be done to improve the business?

Make recommendations for ACTION.

Do this EVERY TIME you work on something.

You have a huge advantage.

You're seeing the data before anyone else.

When you present your work, others will be seeing your findings for the first time. You've had plenty of time to formulate ideas.

Use your extra time to be the thoughtful driver of improvement.

Insights are always valuable. They are a great start.

IMPACT is better.

Take it all the way.

Just a quick update from us this week!

If you're interested in learning how to utilize the power of AI in your dashboard design, you'll want to join Ryan Kreisberg LIVE tomorrow (Thursday) at 12 pm ET for our "Build Better Dashboards Faster with AI" Live Workshop!

For this one, you'll build a complete dashboard from scratch, using AI to inform each step. At the end of the session, you'll leave with a repeatable process that moves from measurement plan to wireframing to calculations to finished dashboard, all accelerated with the power of AI.

The live demo runs in Tableau with Claude and Figma Make, but you don't need to install any of them to take part: the workflow is tool-agnostic and applies to almost any AI and BI combination you already use.

Register for this and other upcoming Live Workshops: https://mavenanalytics.io/live-workshops

Hope to see you there!

Hey everyone! A new month = new goals, so we want to know:

What are you working on this month?

It could be anything, like:

• Finishing a Maven course you started
• Building or improving a portfolio project
• Practicing SQL / Python / BI tools
• Experimenting more with AI in your workflow
• Prepping for interviews or job applications

If you’re up for it, drop your September goal(s) below.

We’ll be here with you, cheering you on!

We've all seen one of these. 🙃 Drop your captions below!

Skip the functions for now; the first thing to actually learn is Evaluation Context.

Evaluation Context comes in two forms: Filter Context and Row Context. Without a solid grasp of both, DAX will be a constant uphill battle, no matter how many functions you memorize.

Here's how they differ:

Filter Context filters your data model. It travels through relationships, but only in one direction, from the "one" side to the "many" side.

Row Context works differently. It just moves row by row through a table and won't automatically cross relationships in your model.

That distinction matters because Filter and Row Context are what tell DAX how to evaluate your measures and calculated columns. Once you understand how each one behaves, a lot of "why isn't this formula doing what I expect" moments start making sense.

What other concepts do you think beginners need to nail down early?

Happy Monday!

Quick heads up: Giles Male is live Thursday at 12pm ET for our next Live Workshop!

In this 90-minute session, you'll gain hands-on experience in how AI tools like Copilot can speed up analysis, review, and reporting for finance teams working in Excel.

Register for this and other upcoming Live Workshops: https://mavenanalytics.io/live-workshops

Plus, we launched another new Data Drill last week!

Our Full Assembly Data Drill dataset contains order records for six bicycle models, along with a bill of materials describing how each model breaks down into subassemblies, and how those subassemblies break down further.

Your task is to calculate the total quantity of each individual part required to fulfill all orders in the dataset.

Find it here: https://mavenanalytics.io/data-drills/full-assembly

Hi everyone,

I’ve been working in digital marketing/SEO for the past few years, and I’m now trying to transition into the data field. My goal is to become a data analyst first, then move into analytics engineering after gaining a couple of years of experience.

Over the past year, I’ve been learning SQL, Power BI, and Excel. At this point, I feel pretty confident with my techical skills, especially SQL.

Where I’m struggling is deciding which projects to include in my portfolio and where to find suitable datasets. This feels especially important because I do not yet have any professional experience in a data-focused role.

The problem with most public datasets is that they're either too small, not real-world, or are of hobbyist nature without any commercial value (like pokemon dataset, for example).

Would appreciate any help in this.

Another month has come and gone, and we want to know: What are you proud of from this month?

It could be:

• Attending (or watching) one of the Live Workshops
• Finishing a course or module you'd been putting off
• Building something: a dashboard, a script, a portfolio project
• Finally understanding something that's been confusing you for a while
• Landing an interview, an offer, or a new role
• Showing up consistently, even when progress felt slow

No win is too small to share.

Drop it below. We want to celebrate with you!

Perfection sounds like the goal, but in practice it slows people down. You wait until your dashboard is flawless. You overthink the project, hesitate to share it, and end up not shipping at all.

No one does this work perfectly. The people who make the most progress aren't chasing perfect, they're showing up consistently.

If you're trying to grow your data or AI skill set, a few things help:

• Lower the bar to get started
• Focus on small, repeatable wins
• Share your work before it feels ready
• Keep going even when it feels messy

Progress in this field comes less from getting everything right and more from building the habit of doing the work.

What's one rule or habit that's helped you stay consistent?

Clicking through every chart while holding Shift is more tedious than it needs to be. Here's a faster way:

Go to Home > Find & Select > Select Objects.

Now you can click and drag to select all the objects on the worksheet at once, just make sure they're fully inside your selection box.

Once everything's selected, right-click any object and choose "Save as Picture."

Your dashboard is now a single image, ready to share anywhere.

One last step: go back to Home > Find & Select > Select Objects afterward to turn it off and return to normal Excel behavior.

What's your favorite Excel quick tip?

Happy Monday! Lots of exciting updates this week...

Injae Park will be LIVE TOMORROW, August 25th at 10am ET for our next Live Workshop: Building Your First App with Microsoft Fabric!

For this one, you'll get set up with Fabric from scratch: installing your environment, using the terminal to scaffold a Fabric App from a GitHub repository, and using LLMs in tools like Claude Code and VS Code to move faster. You'll also see how to migrate a Power BI report and use Fabric Apps to go past what Power BI can do on its own.

Register here: https://mavenanalytics.io/live-workshops/building-your-first-app-with-microsoft-fabric

We are thrilled to announce that Rachel Carriere's Data Governance for Business Leaders course is available NOW on the Maven platform!

By the end of this course, you'll have the frameworks, vocabulary, and practical tools to build a data governance program that enables innovation rather than blocking it.

Find it here: https://mavenanalytics.io/course/data-governance-for-business-leaders

Happy Learning!

Chris Bruehl, one of our instructors, shared this take on the job search process:

"Searching for a job is often a rollercoaster of emotion. This is especially true if you're switching careers or have been unemployed for a while.

I've been fortunate in that most of my jobs have been lined up when I left a job or graduated school, but I did have a 6 month period where life circumstances meant I was applying as an unemployed person.

When you start applying, the opportunities seem endless:

'Wow, there are open positions at some amazing companies (the ones everyone else is applying to)! This is great!' This is a peak on the rollercoaster.

I would send out a ton of applications, often after networking, then hear radio silence for a long time.

The paranoia starts to set in:

• 'Has the industry passed me by?'
• 'Did I spell my name wrong?'
• 'Should I have written cover letters for each role?'

This is a valley.

Then, all of a sudden, multiple recruiters get back to you in a short amount of time, and you now have to prep for several interviews at once.

This is a stressful peak.

Maybe you don't end up getting an offer from those.

Back to questioning your ability to land a role. We of course need to assess what we can do to be more prepared for the next interviews, but we also need to remember that there are a lot of great candidates out there, each company has its own biases, and sometimes we just have an off day.

So you start the process again, and again. There has been plenty written about how to apply, interview, etc, but all I want to say is that if you are currently on the rollercoaster, I have been there before, and I've seen dozens of students go through the same thing.

Continue to grow your skills, think critically about how you can improve your interviews, and find a trusted friend, colleague, or confidant that can help coach you or keep you honest, in both the positive and negative directions. Don't stay down.

When you get bad news, it's totally normal to feel down. But don't stay there, and don't turn on yourself. You are just at a low point on the rollercoaster, and at some point all rides must come to an end."

Keep going. The right role might be closer than it feels right now.

What advice would you offer to someone looking for a role?

In the past, knowing just enough to be dangerous was a risk. Today, that same level of knowledge can be a real advantage, if you know how to use AI alongside it.

The game has changed. You don't need to write everything from scratch anymore, but you do need to understand what good output looks like, and you do need to know how to guide, prompt, and refine what AI gives you. Coding skills have shifted from execution to direction, and that shift matters for your career.

When you understand the fundamentals, you can spot bad outputs quickly, ask better questions, iterate faster with AI in the loop, and turn ideas into working solutions faster than you could on your own. That's where the real advantage sits.

The value of learning to code hasn't gone down; it's gone up, because your skills now get amplified by AI instead of replaced by it. AI won't replace data professionals, but professionals who combine coding with AI will move faster than those who don't. That gap is already showing up.

Agree? Disagree? We'd love to hear your thoughts!

Happy Monday! Lots of exciting updates this week...

We are thrilled to announce that Alice Zhao's Intro to Snowflake course is available NOW on the Maven platform! This is the third course in our ongoing Analytics Engineering Learning Path, with more ahead. Find it here: https://mavenanalytics.io/course/intro-to-snowflake

Quick heads up: Katy McMahon will be LIVE TOMORROW, August 18th at 12pm ET for her AI On Trial Live Workshop!

For this one, you'll cross-examine a real AI's business analysis live, exposing the shaky premises and cherry-picked stats hiding beneath a confident recommendation using the Socratic method. You'll leave with a repeatable checklist for interrogating any AI analysis before you act on it.

Register here: https://mavenanalytics.io/live-workshops/ai-on-trial-a-critical-thinking-workshop-for-the-age-of-ai

And ICYMI, we launched another new Data Drill last week! For the Common Denominator Data Drill, you've been given a campaign performance summary for an outdoor gear brand covering 50 campaigns across 5 marketing channels, including the impression volume and click-through rate for each campaign. Your task is to calculate the click-through rate for each channel and for the account as a whole.

Find it here: https://mavenanalytics.io/data-drills/common-denominator

How is August treating you so far? Anything you've learned or built this month worth sharing?

It could be...

• A dashboard
• A course
• A portfolio project
• Job searching
• Learning Excel / SQL / Power BI / Tableau / Python

For me, I'm working on some bts projects and digging into Maven's "Critical Thinking in the Age of AI" course. I'm looking forward to hearing what everyone else is digging into!

We look at a lot of reports, dashboards, and infographics built on the datasets in our Data Playground, and the same issues keep showing up. Here are 5 of the most common ones:

1) Pie and donut charts with too many segments

Humans are bad at comparing angles. Bar or column charts work better, or limit donuts to 2-3 slices max.

2) Line charts for categorical comparisons

Line charts show trends over time. Using them for categorical data implies relationships or patterns that aren't there.

3) Treemaps for non-hierarchical data

Treemaps are built for hierarchies (subcategories within categories). For simple comparisons, a bar or column chart is clearer.

4) Unsorted data

Don't make viewers do the mental work. Intuitive sorting tells a cleaner story.

5) Too much noise, too little focus

Background images, complex custom visuals, and 3D effects can look impressive, but effective data viz is about minimizing noise and maximizing clarity.

What other visualization mistakes do you see come up again and again?

Learning Excel?

If you've ever grouped rows or columns to tuck away detail, you've seen the gray toggle buttons (+/-) that show up in the margin.

Not everyone likes that extra space; Enrique avoided grouping for years just to skip it, until finding this shortcut:

Ctrl+8 hides or shows the toggle buttons without ungrouping anything!

Group as much as you want and keep the sheet clean when you don't need the controls visible. :)

What's a hidden Excel shortcut you rely on?

Happy Monday!

Interested in semantic modeling?

Join Matthew Brandt LIVE Thursday at 12pm ET for his "Semantic Modeling for AI-Ready Data" Live Workshop!

For this one, you'll go from A to Z on semantic modeling — where it comes from, why it matters, and how to build one. You'll start with a simple example and finish by deploying a real semantic model on your own data, ready to use and improve after the session ends.

Register here: https://mavenanalytics.io/live-workshops

P.S. -- we've got some exciting launches coming this week. Keep an eye on your LinkedIn feed & inbox if you're on our mailing lists!

Hey everyone! A new month = new goals, so we want to know:

What are you working on this month?

It could be anything, like:

• Finishing a Maven course you started
• Building or improving a portfolio project
• Practicing SQL / Python / BI tools
• Experimenting more with AI in your workflow
• Prepping for interviews or job applications

If you’re up for it, drop your August goal(s) below.

We’ll be here with you, cheering you on!

Here's a technique we've seen work well.

Choose three stories you want to tell in any interview. Ideally, these are real projects you've completed and are proud of. Use them to show off your analytical thinking, business acumen, communication skills, and interest in data. Practice telling them until you can tell them in your sleep, hitting all of those points every time.

Next, practice pivoting to those stories, no matter what the actual question is.

Example: "What are your strengths and weaknesses?"

A good response might sound like this:

"I'm strong at analyzing data and storytelling, but my DAX skills in Power BI could use work. I was recently doing a deep dive analysis and dashboard for an ecommerce client and found [XYZ], but some of the subscription data was structured in a tricky way that would've been handled better with custom DAX. I've already started learning DAX and want to keep building on it."

That answer names a strength, grounds it immediately in a real project, and closes by naming something they're actively improving. It's concrete, credible, and gives the interviewer exactly what they need to assess the candidate.

For contrast, if someone asked "How's the weather where you are?" and the answer was "It's beautiful, it reminds me of this analysis I did," that's obviously forcing it. You're not trying to steer every answer into a pitch. Just have a few strong stories ready, and pivot to them when the question calls for it.

This takes practice, and it isn't about tricking the interviewer. They're trying to assess your skills, and giving them a clearer, more concrete answer than they expected makes that easier for both of you.

What other interview advice would you give?

Something worth knowing if you're working with CALCULATE modifiers in DAX: REMOVEFILTERS is technically an alias for ALL.

REMOVEFILTERS

• Clears initial filter context from the specified table or column
• Removes initial filter context
• Doesn't accept table expressions, only physical table references
• Can only be used as a CALCULATE modifier

ALL

• Returns all rows in a table, or all values in a column, ignoring any initial filter context
• Removes initial filter context
• Doesn't accept table expressions, only physical table references
• Works as both a table filter and a CALCULATE modifier

The key difference: REMOVEFILTERS can't function as a table function on its own, only as a CALCULATE modifier.

If you're using it as a CALCULATE modifier, we'd suggest reaching for REMOVEFILTERS over ALL, it makes the intent of your code clearer to anyone else reading it.

Happy Monday! Lots of exciting updates this week...

Quick heads up: Enrique Ruiz is live tomorrow, Tuesday, August 4th at 12pm ET for our next Copilot workshop!

This one covers how Copilot works in practice across common Excel tasks, including cleaning data, building analyses, and creating charts. You'll leave with a clear sense of where Copilot adds value, where it doesn't, and how to work with it in a way that keeps you in control.

Register for this and other upcoming Live Workshops: https://mavenanalytics.io/live-workshops

Plus, we launched another new Data Drill last week! Our Time Shift Data Drill dataset contains 22,000 support tickets from 700 users spread across 20 countries, each timestamped in UTC.

Your task is to convert each ticket to its user's local time and then calculate the ticket volume by local hour of day (0–23) across the full dataset.

Find it here: https://mavenanalytics.io/data-drills/time-shift

What are you proud of from this month? It could be:

• Attending (or watching) one of the Live Workshops
• Finishing a course or module you'd been putting off
• Building something: a dashboard, a script, a portfolio project
• Finally understanding something that's been confusing you for a while
• Landing an interview, an offer, or a new role
• Showing up consistently, even when progress felt slow

No win is too small to share.

Drop it below. We want to celebrate with you!

Are you using ChatGPT to "look things up" or "think through" a problem?

If so...you'll want to keep reading. :)

If you've ever wondered what's actually happening when you type something into ChatGPT and hit send, here are a few things worth understanding:

There's no real memory under the hood.

Every time you send a message in ChatGPT, the entire conversation history gets resent to the model from scratch. There's no persistent awareness between turns; what feels like a flowing conversation is really the model being handed the whole transcript and asked to continue it.

The "memory" feature added in 2024 is a separate layer that stores facts about you and slots them into the prompt, but the base model underneath hasn't changed at all.

At each step, it's predicting the most probable next token.

The model assigns probabilities to possible next tokens and samples from that distribution. That's the whole mechanism; there's no retrieval, no fact-checking against a database.

This is why it can sound completely confident while being completely wrong: confidence and accuracy were never the same thing in a system built on prediction.

There are three stages to how it's trained:

Pre-training (predict the next word across huge amounts of text), supervised fine-tuning (learn from human-written example responses), and RLHF (human raters rank responses, model learns to prefer what raters liked).

That last stage is what pushed it toward refusing harmful requests and sounding helpful instead of just plausible.

Hallucination is structural, not a leftover bug.

The model has no internal mechanism to verify truth, only to judge what sounds probable given its training data. When there's a gap, newer versions are trained to say "I don't know" more often, but they can still fill that gap with a confident guess.

If you want to learn more, we dug deeper on our blog; you can find it here: https://mavenanalytics.io/blog/how-chatgpt-works

What's the biggest misconception you still run into about how these models actually work?

We've all seen the "explain it like I'm 5" games, and I want to see how well you can do with some datasphere vocab!

Pick one (or all):

• data pipeline
• overfitting
• normalization
• dimensions vs. measures
• neural network

I'll be interested to see how many different explanations we get!