iiinova — where your digital self keeps evolving

Manuel Chevalier @manuelchevalier.bsky.social · 29/09/2026

1/3 Writing an MSCA Doctoral Network proposal? Build data training in from the start, not after data collection. Once the data exist, some important decisions cannot be undone.

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Manuel Chevalier @manuelchevalier.bsky.social · 23/09/2026

Today I received this on a mailing list: "We are expanding our team at the Department of XXX in XXX and are opening a semi-permanent full-time Research Scientist position starting in 2027" I must be getting dumb, but could someone explain the concept of a semi-permanent job?

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Manuel Chevalier @manuelchevalier.bsky.social · 21/09/2026

In a few weeks, I need to be teaching some AI to master's students for 6 hours Easy on paper, but I find myself struggling to find a good starting point. What can I teach people who, in all likelihood, are already familiar with the tool?

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Manuel Chevalier @manuelchevalier.bsky.social · 18/09/2026

This week, I was told that I made statistics fun. Job done. 🫳 🎤

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Manuel Chevalier @manuelchevalier.bsky.social · 16/09/2026

In stats, I've always considered that most things are fair game. The good, the bad, and the ugly... ... as long as the assumptions and uncertainties are properly discussed. It is okay to make predictions with a few data points. But results should be discussed accordingly.

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The five miles I never ran

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Manuel Chevalier @manuelchevalier.bsky.social · 15/09/2026

First day jitters. Tomorrow I start teaching a new stats class. I know I know my s** t, and yet I am stressed. For this class, I decided to go with a completely different approach. Skipping all the maths and equations to focus only on what things mean. And how to use them.

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Manuel Chevalier @manuelchevalier.bsky.social · 01/09/2026

Common sense before technique I had a classification problem for my running data. As I thought about which supervised-learning algorithm to use, I took a U-turn and tried the simplest, non-fancy solution first. --- My latest notebook entry: manuelchevalier.com/running-extr... 📓

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Extracting the signal from the background

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Manuel Chevalier @manuelchevalier.bsky.social · 26/08/2026

Finding the right variable that addresses your question is difficult. And quite often, what you actually want isn't even there. At least not directly. I often notice how many students tend to stick to what they have. They forget they can engineer data columns. manuelchevalier.com/running-find...

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Finding the right variable

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Manuel Chevalier @manuelchevalier.bsky.social · 25/08/2026

Over the past few weeks, I’ve been working on a course my younger self would have loved: 12 hours focused on the concepts behind statistical techniques and on deciding whether they make sense for your data. Not “hey, look at the equation; it says it all”.

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Manuel Chevalier @manuelchevalier.bsky.social · 18/08/2026

My running notebook already has six chapters! As small and slightly irrelevant as it may be, I’m enjoying writing it. Just a little summer project, growing one chapter at a time ☀️📓 Chapters 7-10 are already brewing. I just need to run more to collect the data. buff.ly/IIObNCm

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Running

Sixteen weeks of rebuilding a running routine while documenting every step of a complete data project, from data collection, interpretation, and race time predictions.

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Manuel Chevalier @manuelchevalier.bsky.social · 12/08/2026

Writing R code is rarely the hardest part of data analysis. Structure is. That's what makes things click ... or crash. The world needs to know. That's why I adapted this chapter into the latest entry of my running notebook: 📓 A place for everything manuelchevalier.com/running-sett...

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A place for everything

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Manuel Chevalier @manuelchevalier.bsky.social · 10/08/2026

Last week was the 5th online edition of my 𝗥 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 workshop. And I experienced such a proud teacher moment when one participant showed me this plot at the end of the fourth day. It might not look like much, but it truly is. I was really excited for them. Let me explain why.

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Manuel Chevalier @manuelchevalier.bsky.social · 04/08/2026

In the past months, I've thought a lot about data science, its current ecosystem, and how much more efficient learning has become in recent years. (Without even talking about AI doing your mundane tasks.) manuelchevalier.com

manuelchevalier.com

Enter the Mind of a Data Scientist

My vision of Data Science. Follow complete investigations from idea to conclusion, where questions matter more than tools and thinking matters more than technique.

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Manuel Chevalier @manuelchevalier.bsky.social · 30/07/2026

🚨 Last days before the workshop. 50% discount if you contact me here or by email. Take control of your data journey. Today. 📆 August 3-7, 2026 📍 Live & interactive, online 🎟️ datasharpacademy.com/workshop-rfu...

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Manuel Chevalier @manuelchevalier.bsky.social · 29/07/2026

I’ve politely ignored the dbplyr R package for years. I had never had issues with databasing from R or Python. What could it bring me? manuelchevalier.com/running-extr...

manuelchevalier.com

From SQLite to R

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Manuel Chevalier @manuelchevalier.bsky.social · 28/07/2026

R is much more than a language for statistics. It's a language for cleaning, transforming, analysing, and turning your data into publication-ready figures. My next week workshop start with the fundamentals. And we build up together. 📆 August 3-7, 2026 🎟️ datasharpacademy.com/shop/r-funda...

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Manuel Chevalier @manuelchevalier.bsky.social · 22/07/2026

I've often been lured by the word "database". For me, it means clean, structured data, homogenised fields, formal relationships between tables, no (or limited) duplicate rows, etc. When someone tells me they have a database, I instinctively picture a relational database.

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Manuel Chevalier @manuelchevalier.bsky.social · 21/07/2026

Properly loading real-life data in R is hard. And a skill to learn before anything else. Because being able to run sophisticated models won't matter if you can't load your data. 📆 August 3-7, 2026 📍 Live & interactive, online 🎟️ datasharpacademy.com/shop/r-funda...

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Master R Fundamentals for Data Science – DataSharp Academy

Develop powerful statistical analyses in R without getting lost in complex code. This course is designed for resourceful beginners who want to take control of their analysis and workflow. You’ll…

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Manuel Chevalier @manuelchevalier.bsky.social · 15/07/2026

I've started writing my first data science notebook, using the running program I recently started as the excuse to discuss many of the key stages of a typical data science project. Especially the stages that precede the shiny results. Far from the hype, but no less important. 📓 buff.ly/pqOapGq

manuelchevalier.com

Running

Sixteen weeks of rebuilding a running routine while documenting every step of a complete data project, from data collection, interpretation, and race time predictions.

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Manuel Chevalier @manuelchevalier.bsky.social · 13/07/2026

Have you ever wondered what these strange-looking symbols |> or %>% AI keeps spitting really mean? They are actually beautiful things that make your data processing sooo much easier once you get the logic. These pipe operators ( |> or %>% ) can be interpreted as follows: "take the result of the tr

datasharpacademy.com

R Fundamentals for Data Science – DataSharp Academy

Learn foundational R skills and discover how to use AI as a supportive tool in your data journey. Whether you’re starting from scratch or looking to

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Manuel Chevalier @manuelchevalier.bsky.social · 09/07/2026

Your code somehow works, but you know it won't survive a crash from RStudio? You'd be surprised how a few careful decisions could prevent this dread. Learn to organise your workflow so that your scripts and file structure work for you, not against you. buff.ly/cW8rA76

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Manuel Chevalier @manuelchevalier.bsky.social · 08/07/2026

Recently, I got really fed up with the constant AI noise. Every week seems to bring another tool that promises to revolutionise data science. Another model. Another workflow. Another miracle. 😮‍💨 Yet most of the data science I actually do still looks remarkably ordinary. manuelchevalier.com

manuelchevalier.com

Enter the Mind of a Data Scientist

Following data wherever they lead.

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Manuel Chevalier @manuelchevalier.bsky.social · 06/07/2026

The summer is the perfect time to learn to code. Fewer distractions. Fewer commitments. Invest 5 hours of your time each day for a week and harvest the benefits for many years. datasharpacademy.com/workshop-rfu...

datasharpacademy.com

R Fundamentals for Data Science – DataSharp Academy

Learn foundational R skills and discover how to use AI as a supportive tool in your data journey. Whether you’re starting from scratch or looking to

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Manuel Chevalier @manuelchevalier.bsky.social · 01/07/2026

After the last successful 𝗥 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 workshop a month ago, I decided to revisit all my slides, exercises, and even my overall outline. As I keep teaching this class, I understand more and more what the participants expect and where they come from.

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Manuel Chevalier @manuelchevalier.bsky.social · 13/06/2026

One thing many people underestimate in data analysis is the friction in working with files. Broken paths. Lost files. Scripts that only work on one machine. Good workflows are more than organisation. They give back mental energy for the parts of your code that actually add value. buff.ly/NdnYxT1

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Newsletter – DataSharp Academy

A newsletter that brings structure and confidence to the chaos.

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Manuel Chevalier @manuelchevalier.bsky.social · 10/06/2026

For years, I treated paths as regular strings. Paste this. Add a slash. Don’t forget the extension. A lot of workflow friction comes from manually handling things that should already have structure.

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Manuel Chevalier @manuelchevalier.bsky.social · 09/06/2026

Some workflows are so fragile that people become scared of their own folders. “Don’t move that.” “Don’t rename this.” “Don’t touch the old version.” And definitely: “Don’t close RStudio.” If everything breaks the moment something (anything!) moves, your workflow needs more love.

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Manuel Chevalier @manuelchevalier.bsky.social · 06/06/2026

One of the biggest shocks when moving from tutorials to real-world data analysis is realising how messy real datasets are. Extra friction at every step. Never forget: there is no learning data analysis without learning data cleaning. datasharpacademy.com

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Manuel Chevalier @manuelchevalier.bsky.social · 04/06/2026

Stop blaming your tools. Start by checking your data. I’ve seen people spend hours rewriting code, changing packages, or asking AI for help… for problems that were quietly sitting inside the dataset from the start. Messy data create fake complexity. => Fix the data, not the pipeline.

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Manuel Chevalier @manuelchevalier.bsky.social · 02/06/2026

The vast majority of coding problems come from: * inconsistent formatting * hidden missing values * broken variable types * messy real-world data You probably know how to code. But are you equipped to deal with the real complexity of data analysis?

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Manuel Chevalier @manuelchevalier.bsky.social · 01/06/2026

One of the biggest shocks in data analysis: Real datasets are NOTHING like tutorial datasets. Inconsistent categories. Broken dates. Missing metadata. Mixed formats. Unexpected NAs. Many people think they are bad at coding. In reality, nobody prepared them for messy data.

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Manuel Chevalier @manuelchevalier.bsky.social · 28/05/2026

Messy data create fake complexity. If adding 1 and 1 gives you 11, don't blame your tool. Your data probably need some love. A dataset with inconsistent categories, mixed formats, incomplete metadata, or duplicated labels can completely distort your understanding of what is happening.

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Manuel Chevalier @manuelchevalier.bsky.social · 26/05/2026

Most “coding problems” are actually data problems. Not the algorithm. Not the package. Not R/Python. Your data! Wrong types. Inconsistent strings. Hidden spaces. Duplicate categories. Unexpected NAs. "1" ≠ 1 "Blue" ≠ "blue" The code is often just reacting to the chaos underneath.

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Manuel Chevalier @manuelchevalier.bsky.social · 23/05/2026

AI can already help you write code. The real challenge is learning how to look at your data: - spotting suspicious patterns - questioning assumptions - understanding what the dataset can actually support That skill is built through experience, not magic.

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Manuel Chevalier @manuelchevalier.bsky.social · 21/05/2026

Two people can look at the same dataset and draw completely different conclusions. Not because one is smarter. And not because one knows more advanced methods. Often, it simply comes down to what they notice.

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Manuel Chevalier @manuelchevalier.bsky.social · 19/05/2026

1/ One thing that took me years to accept: Good data analysis is often surprisingly slow at the beginning. Not because strong analysts are inefficient. But because they spend time understanding the dataset before deciding what to do with it.

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Manuel Chevalier @manuelchevalier.bsky.social · 16/05/2026

Most people approach data analysis backwards. They first ask: “What method should I use?” But strong analysis starts earlier: - understanding the dataset, - spotting patterns, - questioning assumptions, EDA is the foundation of the analysis itself. Don't skip it.

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Manuel Chevalier @manuelchevalier.bsky.social · 14/05/2026

1/ One of the most common mistakes in data analysis is jumping to complex solutions too early. AI made this even easier. Sophisticated tools are now only one prompt away.

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Manuel Chevalier @manuelchevalier.bsky.social · 12/05/2026

Most people think exploratory data analysis means loading a dataset and making a couple of plots. But EDA is about learning how your data behave: - what looks suspicious - what moves together - what is missing Otherwise, you risk solving the wrong problem with the wrong method.

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Manuel Chevalier @manuelchevalier.bsky.social · 09/05/2026

You load a dataset. Sometimes you don’t know where to start. Other times you think you do… until nothing makes sense. Your problem is a lack of structure. Start simple to make things click • What are you trying to answer? • Can your data support it? • What are the key steps? datasharpacademy.com

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Confident Data Analysis with DataSharp Academy

We offer practical, hands-on data analysis foundational training to help you structure, analyse, and communicate data with clarity and confidence.

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Manuel Chevalier @manuelchevalier.bsky.social · 07/05/2026

Thinking “I’ve seen this before, I know how to handle this” is often where the problem starts. You stop looking at the data, and start fitting it into what your expectations. And just like that, the analysis is already biased. Never forget each project demands specific tools and care.

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Manuel Chevalier @manuelchevalier.bsky.social · 05/05/2026

Have you ever found yourself just "doing stuff" with your data? You were asked to analyse them, so here we are. RStudio is open. Some fancy graphs. It looks like work. But what are you _really_ trying to do? How should you analyse them? And most importantly, why?

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Reposted by Manuel Chevalier

Dr. Suzette Flantua @suzetteflantua.bsky.social · 04/05/2026

@ac-lima.bsky.social will today be presenting at #EGU26 ! In the session "GM9.2 Mountain Glaciations in a Changing World", Augusto will be talking about "Validation Practices in Mountain Palaeoglacier Modelling", based on an extensive global literature review.📚 #ProudSupervisor #RisingStar 🤩

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Reposted by Manuel Chevalier

Manuel Chevalier @manuelchevalier.bsky.social · 02/05/2026

When you mix everything together, nothing makes sense. R, RStudio, packages, scripts… Different layers. Different roles. Without a mental map: * your work feels random * hard to reproduce * hard to trust We fix that at DataSharp Academy. Next newsletter breaks it down ↓ buff.ly/EBkTcRY

datasharpacademy.com

Newsletter – DataSharp Academy

A newsletter that brings structure and confidence to the chaos.

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Manuel Chevalier @manuelchevalier.bsky.social · 02/05/2026

datasharpacademy.com

Newsletter – DataSharp Academy

A newsletter that brings structure and confidence to the chaos.

012

Manuel Chevalier @manuelchevalier.bsky.social · 30/04/2026

“What’s the right way to do this in R?” There isn’t one! If you understand your solution, it’s valid. Whether it takes 3 lines or 20. The real skill isn’t memorising code. It’s knowing how tools work — and choosing what fits.

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Manuel Chevalier @manuelchevalier.bsky.social · 28/04/2026

Most beginners don’t struggle with code. They struggle with naming things correctly. R ≠ RStudio Functions ≠ packages .csv ≠ .xlsx You can still “make it work”. Like hammering with a screwdriver. But it feels random. Hard to trust. Get the basics right. Everything else follows.

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Manuel Chevalier @manuelchevalier.bsky.social · 26/04/2026

At some point, you need to be able to defend what you did. Your choices. Your steps. Your logic. AI doesn’t change that. That’s the difference between: running scripts and actually understanding them. I can help you with that.

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Manuel Chevalier @manuelchevalier.bsky.social · 23/04/2026

AI is powerful. I use it every day. But I have one rule: I don’t use it for things I can’t explain. Because sooner or later, you’ll need to defend what you did. And if you can’t ... AI isn’t helping you anymore. It’s exposing you.

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