Welcome to The DIY Data Scientist!
Greetings and welcome to The DIY Data Scientist - Learn real-world, do-it-yourself (DIY) analytics skills that make you stand out at work with weekly hands-on tutorials designed for ANY professional, including Excel workbooks, data, and code.
If you work with data in any capacity, even if it’s using Microsoft Excel, one thing is becoming very clear:
The professionals who know how to go beyond spreadsheets, dashboards, and basic reporting will have a huge advantage - even in the age of AI.
Not because everyone needs to become a full-time data analyst or data scientist.
But because the people who can ask better questions, analyze data more deeply, build better workflows, and use modern analytics tools with confidence are going to be the ones who create more value, solve bigger problems, and stand out inside their organizations.
Oh, and they’ll be able to partner deeply with AI technologies like ChatGPT, Copilot, and Claude to have more impact at work.
That’s what this newsletter is all about.
The DIY Data Scientist exists to help regular professionals build practical analytics skills they can actually use at work — without needing a computer science degree, a formal analytics title, or a math background.
So if you’ve been thinking:
“I know data matters, but I want to do more with it.”
“I want to learn analytics in a practical, hands-on way.”
“I want skills that make me more valuable at work.”
“I want to use tools like SQL, Python, machine learning, and AI without feeling overwhelmed.”
Then you’re in the right place.
🔬 Who Is The DIY Data Scientist For?
The DIY Data Scientist is for professionals who want to build real-world analytics skills to have more impact at work.
More specifically, it’s for people like:
📊 Excel Power Users and Spreadsheet Analysts: You’re already the “data person” on your team. You know formulas, PivotTables, charts, and reporting. But now you want to go further — into SQL, Python, machine learning, automation, and modern analytics workflows that help you solve bigger problems than Excel alone can handle.
💼 Business Professionals Who Want To Stand Out: Maybe you work in marketing, finance, operations, product, sales, HR, or another business function. You may not have “analyst” in your title, but you know that being data-savvy will make you more effective, more credible, and more valuable in your role.
🚀 Aspiring Data Analysts & Data Scientists: Your career goal is to become a Data Analyst or Data Scientist, and you know you need skills to clean data, analyze trends, build models, interpret results, and use data to drive smarter decisions. Your time is limited, so you want to learn what’s really used day in, day out.
No matter which group you fall into, the goal is the same:
Build practical analytics skills you can use in the real world to make yourself stand out at work.
⁉️ The Biggest Problems In Real-World Analytics
If you’re here, chances are you’re trying to solve one or more of these problems:
Problem #1: You’re stuck at the spreadsheet ceiling.
Excel is useful, but at some point, formulas and PivotTables stop being enough for the kinds of questions you want to answer.Problem #2: You know analytics skills matter, but you don’t know where to start.
SQL, Python, machine learning, statistics — it can feel like too many things, all at once.Problem #3: Most training is either too basic or way too technical.
A lot of content is either beginner fluff or advanced theory. There’s not enough practical instruction for professionals who want useful, job-relevant skills.Problem #4: You want hands-on learning, not vague advice.
You don’t just want to hear what analytics is. You want to actually do it.Problem #5: You don’t want to become a programmer just to work better with data.
You want enough technical skill to solve real business problems — not a four-year detour into software engineering.Problem #6: You’re unsure how tools like Python, SQL, machine learning, and AI fit together.
It’s hard to know what each tool is for, when to use it, and how they connect in a practical workflow.Problem #7: You don’t trust black-box outputs.
Whether it’s a dashboard, a model, or an AI-generated answer, you want to understand what’s happening well enough to validate the results.Problem #8: You want skills that translate into credibility and career impact.
You’re not learning this stuff just for fun. You want to solve better problems, contribute more, and become more valuable at work.Problem #9: You need examples grounded in real business use cases.
You want tutorials that feel like work you might actually do — not toy examples that never show up in the real world.Problem #10: You want a repeatable path to growth.
You don’t need random tips. You need a steady stream of practical tutorials that help you build skills over time.
That’s exactly what this newsletter is designed to provide.
📬 The DIY Data Scientist: Weekly Tutorials to Build Real-World Skills
At The DIY Data Scientist, the goal is simple:
Every week, you get a hands-on analytics tutorial designed to help you build practical, real-world skills.
This is not a newsletter built around hot takes or abstract theory.
It’s built around tangible, repeatable value.
Weekly Hands-On Tutorials
Each issue is designed to help you learn by doing.
That means you can expect tutorials that walk through topics like:
Python for data analysis
Data cleaning and transformation
Visual data analysis
Machine learning in practical business settings
Forecasting, segmentation, and other common analytics use cases
AI-assisted analytics workflows
Python in Excel and modern analytics tools for business professionals
The focus is always on helping you build skills you can actually use.
Real Code and Data
This newsletter is designed to be practical.
So when appropriate, tutorials include:
code you can run
data you can inspect
examples you can follow
workflows you can adapt to your own work
The goal is not just to explain concepts.
The goal is to help you practice concepts.
Analytics Skills That Make You Stand Out
A lot of people want to “learn data science.”
But what they really want is to become more capable, more credible, and more valuable at work.
That’s why this newsletter focuses on skills that help you:
go beyond basic reporting
solve more interesting business problems
communicate more effectively with analysts and technical teams
use modern tools with confidence
build the kind of skill set that gets noticed
In other words:
This newsletter helps you become the person on the team who can do more with data.
A DIY Approach
You do not need to wait for permission.
You do not need to go back to school.
And you do not need to become a full-time data scientist to start building these skills.
You can learn, practice, and apply them yourself.
That’s the spirit behind The DIY Data Scientist.
Ready to learn data analysis right now?
Here are three hands-on tutorial series to get you started learning real-world data analysis skills:
Machine Learning with Python in Excel (yes, you read that correctly!)
👋 Who Am I?
I’m Dave Langer - Author, Microsoft Excel MVP, and LinkedIn Top Voice. I’ve been in technology for 28+ years, with the last 14+ years doing hands-on analytics.
Before becoming an independent analytics consultant and trainer, I held data leadership positions at Schedulicity, Data Science Dojo, and Microsoft.
My passion is demystifying analytics and making it accessible to ANY professional. Regardless of your role. Regardless of your background.
I believe this so much that I wrote Python in Excel Step-by-Step to help professionals like you use your Excel skills as a springboard into the world of analytics.
I created The DIY Data Scientist as part of my mission to help professionals build practical analytics skills in an approachable, useful, and grounded way.
