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

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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🚨 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...

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...

From SQLite to R

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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...

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.

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...

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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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

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.

manuelchevalier.com

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

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

datasharpacademy.com

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

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

Enter the Mind of a Data Scientist

Following data wherever they lead.

manuelchevalier.com

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...

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

datasharpacademy.com

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.

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

Newsletter – DataSharp Academy

A newsletter that brings structure and confidence to the chaos.

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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.

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.

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

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.

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?

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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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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.

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?