Skip to main content
Arnaud Perigord Headshot

Arnaud Perigord

Data science is the future, and it is better to be on the cutting edge than left behind.

Arnaud studied statistics and economics (ENSAE ParisTech 2007 & M2 at Paris School of Economics, 2007). Today, he works in the applied statistics field for the French government’s Ministry of Social Affairs. Even though Arnaud isn’t a data scientist, he uses techniques he learned on DataCamp every day.

What is so exciting about data science?

I think data science is the future of data. Data science is the future of applied econometrics, I would definitely say. I took econometrics and statistics, but my goal when I worked after that was always something applied. It was not something very theoretical. When I worked for the French Minister of Social Affairs, we did a lot of public evaluation but it was not formal. Data science, in my opinion, is the future of this kind of work. It is the only way to do it efficiently, replicably, and get great results. You have a lot more tools and a lot more flexibility in the way you can collect data, the way you can exploit multiple databases all at once, and produce something really usable as well. Data science is the future, and it is better to be on the cutting edge than left behind.

What was your experience with data science before you started your education with DataCamp?

I’m not really a data scientist, that’s for sure. But as a statistician and an economist, I use a lot of tools that data science provides. As a statistician, I graduated in 2007. In economics, I was particularly interested in applied economics. It is the kind of stuff where you have to take data from various sources, anywhere in the world. You had to do it, and back then, you had to do it by hand, then you had to interpret it. It was data science, but it was not like data science is now; I could not automate a lot of things. I could not use the full power of relational databases. All that was kind of missing in my world of data. But I used a lot of little tools of data science. I wasn’t trained in R, but I started learning R after school. In economics at the time, a lot of it was SAS format, so it was pretty much SAS over everything else.

What prompted you to start learning with DataCamp?

I went to DataCamp because of two things. First, I wanted to refresh some of my knowledge of R. I knew I needed R for what I had to do at work, so it was a good way to enter a little bit more into the data science world, refresh my memory of R, and learn more about what I could really do. That’s the first thing. The second thing is that I wanted to learn a little bit more Python and data science. I knew a little bit about how to code in Python, but not to use it efficiently for anything else, or for anything in my field. Those were the two main drivers that took me to DataCamp.

I learned some new skills in R that I didn’t have a year ago, and I got to use them quite immediately in my work.

What do you like about DataCamp?

What I like about DataCamp is that it is quite simple. It is quite easy to watch a video, read some slides, learn what to do, then try it. I think it is really efficient. I like the fact that the courses are very well organized and clear. All the courses I’ve taken on DataCamp were quite well organized with an easy number of chapters, a real path of progression which was well thought-out so it was pleasant to go through.

At the beginning, I was a little bit afraid that I might be bored or a little less interested by the courses because I didn’t intend to take all the courses at once, but now I get it: DataCamp can have a real impact. It is a journey through data science. It was a fun way to progress and learn new things. I was very pleased with that.

Another thing I like is that when you learn something, it is really fun, and it is really important to redo, re-read, re-learn stuff from all of the courses, which is something that you can’t really do when you are at university. With practice, I think you can really progress. I also like that the intro classes are not too hard, which is a good thing when you start. But I think DataCamp is quite well positioned to be a good start in the data science world for any level of expertise. For beginners, you can take a good step and you’ll know if you want to go farther or not. I think it is a nice entryway into the world of data science.

Can you give us some examples of how you’ve used skills you’ve learned from DataCamp?

At the beginning for cost reasons, and now for passion, I use R on a day-to-day basis for statistic analysis—market analysis, time series, and linear regressions mostly. I learned some R basics in 2007, but I used of lot of other softwares in between (SAS, Stata, SPSS). I felt I wasn’t using R to its full capacity.

So I refreshed my memory on DataCamp and learned some new skills in R that I didn’t have a year ago, and I got to use them quite immediately in my work. When I took cleaning data, I used the R code I learned on DataCamp the next morning, quite literally. I learned how to use dplyr. The next morning, I rewrote my program and I used dplyr a lot more efficiently. And I learned how to do graphics: after taking the comprehensive classes on ggplot2, I used it the next morning on the job. I didn’t have to search for commands all the time. I don’t have an example for ways I’ve used all of the data science I’ve learned, but I think it will come in due time. Even though I don’t work in a data science job directly, things I learn on DataCamp I can use at work the next day. Even if they are things I already knew a little bit about before, now I can write code more efficiently.

How does DataCamp compare to other online learning platforms you’ve tried?

DataCamp is quite good. I have tried online courses for a variety of subjects. Some simple platforms just have videos and written text exercises. It is sometimes useful, but lacks the real trial-and-error exercises that DataCamp allows.

Often on other platforms, the exercise part isn’t so present. So I still don’t know how to code after those courses. At DataCamp, the coding part is very well structured, it is very well integrated, it works quite well anywhere, so I feel I am really learning. There are other platforms that do this type of thing, but DataCamp is already at the top.

DataCamp courses are quite clear. I like the fact that there are also some case studies. I think case studies are very important for when you learn. The content of the classes is very good. You are clearly the top of my mind for online learning platforms.

All in all, DataCamp is easy to use and gives you a real sense of using R and Python for a data oriented use. Lessons are well written and simple. It is a good step to see what is behind the curtain and get a feel of the power of data science in the years to come.

Read more stories

Every month I learn something new on DataCamp, and they are all things that are very useful in my job.

Godefroy Clair is a Data Scientist at Flylab, where he helps create software for autonomous drones.

Read Godefroy Clair's Story

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon was able to transition to an analytics role at her bank, and is using DataCamp to hone her skills.

Read Devon Edwards Joseph's Story

DataCamp taught me new packages which entirely changed the way I conduct my daily job.

Marco Venè is a Data Analyst in Berlin where he uses DataCamp to help him do his job more efficiently.

Read Marco Venè's Story