Dave on Data

The Dave on Data mission is to empower any professional (or team) with the DIY data science skills needed to unleash the power of AI technologies like Copilot in Excel.

What gets left out of the AI Hype Cycle is this:

AI will make you (or your team) wildly more productive.

**BUT**

You must have the requisite skills to make the most of AI.

**AND**

If these skills are lacking, you will not be able to recognize (and correct) the AI when it makes mistakes.

BTW - AI makes mistakes all the time.

Take Microsoft's Copilot in Excel AI as an example.

The "magical" scenarios where Copilot analyzes data using a few clicks and a couple of short prompts rely heavily on two things:

Python
Machine learning

Here's the thing, however. 

I've tested Copilot in Excel, which rarely generates the correct Python/machine learning code.

As the DIY data scientist, this is where you unleash Copilot's power.

I can teach you these DIY data science skills.

Regardless of role. Regardless of background.

Founder

TDWI

I develop and deliver live training experiences on data science topics such as data analysis and predictive analytics (e.g., machine learning). I focus on accelerating the data science journey for teams and organizations using a practical hands-on approach.

Training can be delivered virtually or in person with your team. The following offerings are certified by TDWI and can be taught using R or Python:

👉 Machine Learning Bootcamp
👉 Visual Data Analysis
👉 Linear/Logistic Regresion

💻 Learn more: https://daveondata.com/live-analytics-training

Data Science Consultant & Instructor

Schedulicity

VP of Analytics

Data Science Dojo

VP of Data Science

Microsoft

Sr. Director, BI and Analytics

Principal Program Manager

Sr. Director, Platform Architecture

Principal Architect

Ascentium Corp

Associate Partner, Solution Architect

Safeco Insurance

IT Architect

e-software, Inc.

Software Design Engineer

Programmer Analyst

I help ANY professional or team have more impact using data. Analytics demystified.

Want to differentiate yourself at work with Excel skills? Leaders aren't excited about this in 2025:

- Using PivotTables
- Knowing many Excel functions

You know what they're excited about: AI

And that means Copilot in Excel.

Here are the 4 steps to build the skills you need to master Copilot in Excel and impress your manager, business stakeholders, and executives:

1) Ignore "prompt engineering."
2) Learn Python.
3) Learn DIY data science.
4) Recognize hallucinations.

I want to help you own the future of Microsoft Excel.

I will help you get started today - for free.

Join 1,000s of professionals building skills for the future: 
https://daveondata.com/newsletter

David Langer

As an author, there's nothing quite like waking up to some good news from Amazon!

Back in May of 2023, I saw an early version of Python in Excel for the first time. I immediately knew I wanted to be a part of the most significant upgrade in Excel's analytics capabilities ever. 

And it didn't stop there.

When I first got access to Copilot in Excel's "advanced analysis" mode, I quickly saw Microsoft's vision for Excel's future. For professionals building Python analytics skills, the future of Excel lies in deep integration with Copilot AI to unleash the power of their data.

My book is the first step in building the skills for Excel's AI-powered future. It uses your existing knowledge of Excel to help you learn the Python foundation you need - fast.

If you're ready to make 2026 the year you embrace the power of Excel, AI, and data, you can pre-order my book here: https://lnkd.in/eQp4g5_d

#Excel #PythonInExcel #MicrosoftExcel

I help professionals and teams build better forecasts using machine learning with Python and Python in Excel.

Interesting behavior from Copilot in Excel. Just another example of how Python in Excel skills are part of Excel's AI-powered future.

I was working on the next issue of my LinkedIn newsletter, which will cover using AI to build better forecasts in Microsoft Excel, and asked Copilot to create a forecast for me.

I was surprised to see that Copilot didn't use Excel's FORECAST.ETS() function because that's the best way to build data-driven forecasts using native Excel features.

Instead, Copilot in Excel generated Python code in the background to build an ETS model similar to what would be produced from FORECAST.ETS() using Python's mighty statsmodels library.

BTW - I covered statsmodels in my last two LinkedIn newsletter articles.

I'm betting that the new Copilot in Excel Agent Mode will be able to manipulate worksheets (e.g., generating formulas) directly and will be able to generate forecasts using FORECAST.ETS().

However, I'm also betting the Agent Mode will eventually be smart enough to recognize when a better forecasting technique is available from Python and use that instead.

Which means that the AI-powered future of Microsoft Excel will belong to those professionals who develop Python skills.

Stay healthy and happy data sleuthing!

#Excel #PythonInExcel #MicrosoftExcel

Voxel51

• Explore, tinker with, and push the boundaries of the latest AI and machine learning technologies to showcase what's possible with open-source tools and frameworks

• Build inspiring prototypes, proof-of-concepts, and example applications that demonstrate creative and impactful uses of AI to the developer community

• Contribute meaningfully to the open source ecosystem by creating and maintaining libraries, tools, datasets, tutorials and guides that enable developers to more easily leverage AI capabilities

• Curate, clean, annotate and publish unique multimodal datasets spanning text, images, audio, video, 3D and other emerging data types to facilitate novel machine learning research and applications

• Create engaging technical blog posts, social media content, videos, and presentations that educate and inspire developers and data scientists to build with AI

•  Collaborate openly with our internal teams and the broader AI/ML community to cross-pollinate ideas, share knowledge, and collectively advance the state-of-the-art

• Evangelize our technology and vision by representing the company at hackathons, conferences, meetups, universities, and other developer-focused events

•  Influence the product roadmap by providing input on the developer experience, uncovering opportunities for new features and improvements based on community feedback and your work

• Continuously learn, experiment with, and provide feedback on beta versions of our latest models and capabilities to help shape the future of our platform

Hacker-in-Residence

LinkedIn

1) Introductory computer vision course for LinkedIn Learning

2) Prompt Engineering with LangChain (releasing March 2024)

3) RetrievalAugemnted Generation with LlamaIndex (releasing Q2 2024)

4) Fine-tuning LLMs with Hugging Face TRL (est release Q3 2024)

5) Text Generation with Hugging Face Transformers (est release Q3 2024)

LinkedIn Learning Instructor

The Artists of Data Science

The ONLY personal growth and development podcast for Data Scientists

Host

Deci AI

Developer Relations Manager

Pachyderm Inc.

Senior Developer Advocate

Comet

ML Developer Advocate

Data Science Dream Job

Principal Data Science Educator

Price Industries

Lead Data Scientist

Bold Commerce

Senior Data Scientist

Emergent BioSolutions

Biostatistician

I create content about deep learning | The DevRel and Deep Learning Dude

Hacking on LLMs, curating multimodal datasets, and writing a book about RAG.

Harpreet Sahota 🥑

sam3 is seriously a step change improvement over sam2

the model is seriously good, glad they brought back text prompts. the automatic segmentation isn't as good as i hoped it to be...but it still works quite well. 

i also got it to output image embeddings and that was cool to visualize in the app

learn more here: https://lnkd.in/gzqJzj6G

🤖 Hacker-in-Residence @ Voxel51| 👨🏽‍💻 AI/ML Engineer | 👷🏽‍♀️ Technical Developer Advocate | Learn. Do. Write. Teach. Repeat.

qwen3vl is literally the best model i've used for video usecases. blows my mind

sony ai released a pretty cool dataset called the fairness human centric image benchmark, super high quality labels

instructions for downloading and parsing the dataset into fiftyone format can be found here: https://lnkd.in/g_VwxJx4

physical AI is top of mind as the next frontier for deep learning. but it'll only succeed if you have good datasets

language models scaled with web-scale text. 

vision models got ImageNet. 

now Voxel51 and NVIDIA are trying to do the same thing for physical AI 

autonomous vehicles, humanoid robots, anything that moves around atoms.

building these systems means millions of hours of testing in high-fidelity sims before you can actually deploy anything. 

petabytes of multimodal sensor data from various modalities

 LiDAR, cameras, radar, IMUs. 

but over 50% of simulations end up completely unusable because the input data is bad.

even small calibration or timing errors between sensors cascade through your neural reconstructions. 

you waste weeks and burn through millions in compute costs before you realize what went wrong.

the new FiftyOne Physical AI Workbench. it integrates with NVIDIA Omniverse and NVIDIA Cosmos and basically sits at the start of your sim pipeline to validate, clean up, and prep your multimodal data before it hits neural reconstruction and synthetic data generation.

what it does:

- automatically audits physical AI inputs across 75+ critical checkpoints

- transforms raw sensor streams into structured, searchable datasets

- triggers accurate neural reconstructions and generates synthetic scene variations

this means fewer wasted compute hours. 

iteration loops go from weeks to hours. 

you build safer, more reliable systems on data you can actually trust.

read more about what we've been building: https://lnkd.in/gC6_zn-X

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