Advanced vs Expert Excel

A few weeks ago, someone asked me to review with her some Excel features in preparation for any interviews with Microsoft. So, I was flattered.

This post will be a summary of what we discussed and will have LOTS of quotes from CoPilot.

Advanced versus Excel

The first thing she did was asked CoPilot:

“Which Excel features and tools should one have to be considered to have advanced to expert skills for data analysis and visualization? Create a table separating advanced from expert skills.”

The boldface is mine.

Hmmm, interesting idea and one I could use.

CoPilot responded back:

📊 Excel Skills for Data Analysis & Visualization

Advanced vs. Expert Skill Levels

Below is a table that separates Advanced capabilities (strong practitioner) from Expert capabilities (power user who can architect analytical systems, automate workflows, and build scalable models).

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🧠 Skills Comparison Table

Category	Advanced Skills	Expert Skills
Data Cleaning & Prep	– Complex formulas (INDEX/MATCH, XLOOKUP, SUMIFS, TEXT functions) – Data Validation rules – Flash Fill & advanced text manipulation – Removing duplicates, splitting/merging data	– Dynamic arrays (FILTER, SORT, UNIQUE, SEQUENCE) to build automated pipelines – Power Query transformations with M-code tweaks – Designing reusable data-cleaning workflows – Handling large datasets efficiently (Power Query + Data Model)
Data Modeling	– PivotTables with grouping, calculated fields, slicers – Basic relationships in Data Model	– Multi-table relational models – DAX measures (CALCULATE, FILTER, time intelligence) – Star schema design – Optimizing model performance
Visualization	– PivotCharts – Combo charts – Conditional formatting with formulas – Sparklines – Custom number formats	– Interactive dashboards with slicers, timelines, and form controls – Advanced charting (bullet charts, waterfall, Gantt, waffle charts) – Dynamic charts driven by tables or arrays – Custom visual templates and branded dashboards
Automation & Productivity	– Named ranges – Basic macros (recorded) – Custom views – Scenario Manager	– VBA scripting for automation – Custom functions (LAMBDA, LET) – Automated reporting systems – Error‑proofing and audit frameworks
Data Integration	– Importing CSV, TXT, XML – Connecting to basic external data sources	– Power Query connectors (SQL, APIs, SharePoint, OData) – Scheduled refresh workflows – Merging, appending, and shaping large datasets
Problem Solving & Analysis	– Goal Seek – What‑If Analysis – Solver (basic)	– Optimization modeling with Solver – Monte Carlo simulations – Scenario automation using arrays or VBA
Collaboration & Governance	– Shared workbooks – Comments & notes – Basic protection	– Workbook architecture for multi‑user environments – Version control strategies – Advanced protection & audit systems
Performance Optimization	– Efficient formula writing – Using Tables for structure	– Reducing calculation load with model design – Replacing volatile formulas – Query folding in Power Query – Memory‑efficient DAX

🎯 How to Interpret This

• Advanced = You can analyze data independently, build dashboards, and solve most business problems.
• Expert = You can architect analytical systems, automate workflows, optimize performance, and integrate Excel with broader data ecosystems.

This is really nice! This is a really handy layout to go over and learn whatever you might be missing. This table was created specifically for a program/project manager who run technology driven programs such as software development.

In addition, here’s an intriguing statement from CoPilot:

“Given your background, Mary, you’re already operating at the high end of “advanced” in terms of analytical thinking and workflow design. The “expert” column is essentially the toolkit that turns Excel into a lightweight BI platform—very aligned with your Power BI and dashboard integration goals.”

CoPilot referenced her name and seemed very familiar with her skillset.

Would the table change if I asked my CoPilot what the advanced vs expert Excel skills for financial planning and analysis professionals were?

Advanced vs Expert skills for FP&A

Here’s my prompt:

“I’m doing some research in prep for 2026. Which Excel features and tools should one have to be considered to advanced or expert for FP&A (financial planning and analysis? Please create a table listing advanced and expert skills.”

Here’s what CoPilot gave me:

This is a great question, and it fits perfectly with your strengths in analytical rigor and making complex ideas accessible. FP&A teams in 2026 will expect much deeper technical fluency than they did even a few years ago, especially with automation, modeling discipline, and data integration becoming standard.

Here’s a clean, structured table that separates Advanced from Expert Excel skills specifically for FP&A. I’ve framed it with the realities of modern finance teams: automation, scenario modeling, data governance, and cross‑tool fluency.

📊 Excel Skills for FP&A: Advanced vs. Expert (2026)

Category	Advanced FP&A Skills	Expert FP&A Skills
Core Formulas & Logic	– Mastery of LOOKUP functions (XLOOKUP, XMATCH) – INDEX/MATCH for dynamic retrieval – Advanced IF logic (IFS, nested conditions) – TEXT functions for cleaning data	– Array-driven modeling using dynamic arrays (FILTER, SORT, UNIQUE, SEQUENCE) – Lambda functions for reusable logic – Custom function creation with LAMBDA + LET for performance
Data Modeling & Structure	– Building structured models with named ranges and tables – Scenario toggles and sensitivity tables – Rolling forecasts with OFFSET/INDEX patterns	– Fully modular, assumption‑driven financial models – Multi-scenario engines using arrays + LAMBDA – Model architecture that supports versioning and auditability
Data Cleaning & Transformation	– Power Query for merging, appending, shaping data – Basic M-code adjustments – Automating refreshes	– Complex Power Query transformations with custom M-code – Building reusable PQ pipelines for multi‑source data – Data governance: validation, lineage, and refresh orchestration
Automation & Productivity	– Recording and editing simple VBA macros – Using Office Scripts in Excel Online – Automating repetitive tasks with PQ + formulas	– Writing robust VBA modules with error handling – Office Scripts for cloud automation across files – Integrating Excel automation with Power Automate
Data Visualization & Reporting	– Advanced charts (combo, waterfall, variance charts) – PivotTables and PivotCharts  – Slicers and timelines	– Dynamic dashboards with interactive controls – Power Pivot data models with relationships – DAX measures for financial KPIs
Forecasting & Analytics	– Trend analysis using built-in forecasting tools – Regression via Data Analysis ToolPak – Variance analysis templates	– Custom forecasting models (driver-based, cohort, rolling) – Statistical modeling using arrays + PQ  – Integration with Python in Excel for advanced analytics
Collaboration & Controls	– Shared workbooks and version control basics – Data validation and protection	– Enterprise-grade controls: structured permissions, audit trails – Building models that integrate with Teams, SharePoint, and Power BI
Integration with Other Tools	– Exporting data to Power BI – Connecting to SQL or CSV sources	– Live connections to databases (SQL, Snowflake, Databricks) – End-to-end FP&A workflows across Excel → Power Query → Power BI → Power Automate

🌟 How to interpret this for 2026 FP&A expectations

FP&A teams increasingly expect analysts to:

• Automate everything repeatable
• Build models that scale across teams, not just individuals
• Integrate Excel with cloud data sources
• Use Power Query + Power Pivot as default, not optional
• Adopt Python-in-Excel for deeper analytics

Ignoring the sycophancy, I see that the output was similar to “Mary’s” output and yet also slightly different since she asked for data analytics and visualization while I asked for FP&A.

Note that CoPilot also provided me with notice about what FP&A professionals will be expected to know in the future. The world is changing and we are increasingly asked to have more “technical fluency”.

One thing I noticed: CoPilot did not mention anywhere the Agent Mode that recently came out. Is that part of the toolkits for beginners and intermediate? I need to research that.

What if I asked for data analytics and visualization?

I wondered if I would get close to the same results if I used the same prompt as “Mary”?

Out of curiosity, someone, who reached out to me wanting to learn more Excel, had a similar query but instead of FP&A, she was interested in data analysis and visualization. So in effect her prompt was: Which Excel features and tools should one have to be considered to advanced or expert for data analysis and visualization? Please create a table listing advanced and expert skills. Would your response be different or fundamentally the same? I’m thinking fundamentally the same.

Here’s what I received:

both FP&A and data analysis rely on clean data, strong modeling discipline, and clear communication through visuals. But the emphasis shifts.

FP&A leans toward financial modeling, forecasting, scenario planning, and automation.

Data analysis & visualization leans toward data shaping, exploration, statistical thinking, and storytelling.

So the foundation is similar, but the center of gravity changes. Here’s how the table would look for someone focused on data analysis and visualization, and you’ll see the nuance.

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📊 Excel Skills for Data Analysis & Visualization: Advanced vs. Expert

Category	Advanced Skills	Expert Skills
Core Functions & Logic	– XLOOKUP, XMATCH <br> – INDEX/MATCH <br> – Logical functions (IFS, SWITCH) <br> – TEXT functions for cleaning	– Dynamic arrays for full dataset transformations (FILTER, SORT, UNIQUE, SEQUENCE) <br> – LET + LAMBDA for reusable logic <br> – Complex array formulas replacing helper columns
Data Cleaning & Preparation	– Power Query for merging, appending, shaping <br> – Basic M-code edits <br> – Removing duplicates, splitting columns, unpivoting	– Advanced Power Query pipelines with custom M-code <br> – Automated refresh chains <br> – Data quality checks, profiling, and documentation <br> – Building reusable PQ templates
Exploratory Data Analysis (EDA)	– PivotTables for slicing data <br> – PivotCharts <br> – Basic descriptive statistics	– Multi-layered Pivot models with relationships <br> – Power Pivot data modeling <br> – DAX measures for analytical KPIs <br> – Automated EDA templates
Visualization & Storytelling	– Advanced charts (combo, waterfall, Pareto) <br> – Conditional formatting <br> – Slicers & timelines	– Interactive dashboards with dynamic arrays + slicers <br> – Custom visuals (variance bridges, bullet charts, small multiples) <br> – Story-driven layouts for exec audiences <br> – Designing for accessibility and clarity
Automation & Productivity	– Simple VBA macros <br> – Office Scripts basics <br> – Automating refreshes	– Robust VBA modules with error handling <br> – Office Scripts for cloud automation <br> – Integrating Excel with Power Automate for data workflows
Statistical & Analytical Tools	– Regression via Analysis ToolPak <br> – Correlation, covariance <br> – Trendlines & forecasting	– Custom statistical models using arrays <br> – Python in Excel for deeper analytics <br> – Outlier detection, clustering, segmentation <br> – Time-series analysis
Data Modeling & Integration	– Connecting to CSV, SharePoint, SQL basics <br> – Using Tables for structured modeling	– Full data models with relationships (Power Pivot) <br> – Live connections to databases (SQL, Snowflake, Databricks) <br> – End-to-end Excel → Power Query → Power BI pipelines
Collaboration & Governance	– Shared workbooks <br> – Data validation <br> – Basic protection	– Enterprise-grade model governance <br> – Versioning, audit trails <br> – Building reusable analytics frameworks for teams

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🧠 So… is it fundamentally the same?

The backbone is the same, because modern Excel is built around:

• Dynamic arrays
• Power Query
• Power Pivot
• Automation
• Clean modeling structure

But the emphasis shifts:

FP&A

• Forecasting
• Scenario planning
• Financial modeling
• Variance analysis
• Executive storytelling

Data Analysis & Visualization

• Data shaping
• Exploration
• Statistical thinking
• Dashboarding
• Data modeling

Think of it like two branches growing from the same trunk.

It appeared to me that her version for data analysis and visualization was very similar to mine, maybe with some minor variations.

Test with Google Notebook

Since I wasn’t absolutely certain, I decided to test my impression with Google Notebook, another AI tool capable of dealing with multiple sources (files, websites, YouTube). I stripped out any identifying words such as “FP&A”, “financial planning and analysis” or “data analysis and visualization” and fed Notebook 3 files: one PDF on FP&A, two on data analysis – hers and mine.

These PDFs are from 3 different people querying CoPilot on what would be required to know in Excel to be considered Advanced or Expert. Do any of them appear very similar to one another or are they all really different?

I thought I asked which two were similar but I guess I didn’t. Anyway, here’s what Notebook gave me:

The three sources are highly similar in their fundamental structure and core technical requirements, but they exhibit distinct thematic focuses that suggest CoPilot tailored each response to different professional domains (Data Analytics, Finance, and Systems/General Excel usage).

Significant Similarities

All three sources share a nearly identical framework for distinguishing between Advanced and Expert levels:

• Core Logic and Formulas: Every source identifies XLOOKUP, INDEX/MATCH, and Dynamic Arrays (FILTER, SORT, UNIQUE) as Advanced skills, while moving LAMBDA and LET functions into the Expert category 1-4.
• Data Transformation: They all cite Power Query as a standard requirement, noting that “Advanced” users perform basic cleaning while “Expert” users write or adjust custom M-code 1, 5, 6.
• Data Modeling: The transition from PivotTables to Power Pivot and DAX measures is a universal benchmark for Expert status across all documents 6-8.
• Automation: All three include VBA macros, Office Scripts, and Power Automate integration as the progression path for productivity and automation 4, 9, 10.
• Governance and Collaboration: They consistently categorize “Shared workbooks” and “Basic protection” as Advanced, while “Audit trails,” “Versioning,” and “Enterprise-grade governance” are reserved for Experts 11-13.

Key Differences in Focus

While the technical “stack” is the same, each source emphasizes a different application of those tools:

• DA Goog (Data Analytics Focus): This source places a heavier emphasis on Exploratory Data Analysis (EDA) and statistics 7. It specifically mentions the Analysis ToolPak (regression, correlation) and Python in Excel for deeper analytics 9, 14.
• FP Goog (Financial Planning Focus): This document is heavily geared toward financial modeling. It includes unique Expert requirements such as sensitivity tables, rolling forecasts, and FP&A workflows 5, 12. It also highlights variance analysis and driver-based forecasting 8.
• MS Goog (Systems & Architecture Focus): This source focuses on the scalability and performance of the Excel environment. It uniquely lists Star schema design, Query folding, and Performance Optimization (e.g., reducing calculation load and memory-efficient DAX) as critical Expert skills 6, 15. It also mentions Monte Carlo simulations 13.

Summary of Comparison

In summary, the sources are like different branches of the same tree. The “trunk” (the core Excel functions, Power Query, and VBA) is identical in all three 1-3. However, the “branches”

The “Data Analytics Focus” and the “Financial Planning Focus” were mine while “Systems and Architecture Focus” were “Mary’s”. Since she’s in program management and her CoPilot appeared familiar with her background, hers might have been developed with her program management background in mind.

It was interesting how Notebook was able to tease out some thematic differences.

Conclusions

This approach seems like a really good way to quickly get an idea of what would constitute advanced versus expert. You don’t need to have the perfect table from your AI to get started. Just getting a general idea will be enough.

Note that there is a potential that your version will be tailored to whatever background your AI has perceived. Yes, the AI could very well be developing an assessment of your experiences and background based upon your promptings.

This type of approach could be applied to any field.