This is a follow-up to a previous post that I’ve made about various educational avenues to advance your data science career, specifically about the DataCamp portion. I found myself hitting a learning rut using LinkedIn Learning, where even though there were thousands of courses, I felt like I was running out of valuable skills to learn. As there was a discount on DataCamp for the start of the year and I felt I was getting rusty in some areas, I got back on the train to check it out.
What’s New in DataCamp
Since I last was on DataCamp there have been a lot of changes. At a high level I’ve noticed these things:
A Larger Emphasis on Data Engineering
This reflects the current job market, as there’s a much higher need for people who can ETL data as opposed to build a model off of it. I noticed DataCamp had many more courses in areas such as: * SQL Server * Spark * Bash & Shell Scripting * AWS (though the selection here is still fairly slim)
I recently went through the Data Engineer with Python path to get a better understanding of the modern tools used. The Python courses in the track were a good refresher ways to code efficiently, but there was also exposure to robust automation methods like Airflow I hadn’t used before.
My workplace recently shifted towards using Linux for its data science Virtual Machines, so the courses on Bash and Shell scripting in the track were very timely. They helped me navigate the command line to set up various programs on the server and automate current data science workflows on there via cron jobs.
Some of the courses weren’t directly applicable to my current role, but the majority either taught me a useful skill to help build more reliable pipelines for my data science work or gave me a better understanding of the tasks the data engineers at my company perform on a regular basis.
A Larger Emphasis on Business Intelligence Tools
Another reflection of the job market is an investment in courses focused on dashboarding technologies, including: * Power BI * Tableau * Shiny * Dash (though there’s only one course on this)
In my past experiences using Tableau I’ve found its capabilities in a data scientist capacity somewhat limiting. As a collaboration tool its way of saving workbooks makes it so you can’t compare differences between versions on Git easily. Because of that I’m a bit surprised DataCamp has so many courses now on the tool. However, drag-and-drop tools that purport to make things simpler have a much lower barrier to entry than traditional software development practices, so I can understand why DataCamp is capitalizing on the demand.
I rely on R Shiny for dashboarding in R and have not found a preferred tool for dashboarding in Python. In the past, I have used Streamlit, but have found it to be laggy for end users to experiment with. I will experiment with Dash in the future to see if it’s a more effective tool for Python dashboarding needs.
Assessment of Skill Levels
Since I last used DataCamp they have added a tool in order to evaluate your skills called DataCamp Signal. After taking a timed quiz that adapts to your current skill level based on whether you get the question right or wrong, you get a number between 0 and 200 that tells you whether your expertise is either Beginner, Intermediate, or Advanced.
It’s a neat tool. I like messing around with it to see what functions are “expected” to be known, and it can be a good reminder when I can’t think of the proper function to solve within 60 seconds (yeah, you don’t get time to Google the solution). However, some of the questions can be repetitive and awfully specific (one quiz asked me to make a ggplot scatterplot 3 times) so I wouldn’t call it a perfect replacement for vetting a candidate’s skills.
Certifications vs Statements of Accomplishment
When I previously used DataCamp they only had statements of accomplishment for their Data Analyst and Data Scientist in R paths. However, they now offer a Data Science Professional certification specializing in either R or Python. In order to obtain the certification there are three assessments, a case study (with presentation component to technical and non-technical audiences), and a coding challenge.
Given where I currently am my career I don’t have a need for a certification. However, if you’re already paying to access their premium content, it doesn’t seem to cost you any extra money to get certified. Further, DataCamp says they have a career services team that will work to get you a job in data science once you’re certified.
I’m generally dubious about the value of all of these different certifications that are appearing for data science. Having been on the other side of the hiring table it’s hard to validate the effectiveness of any one particular certification, so it ends up being reliant on how well you interview. However, relevant certifications show you’re a “go-getter” and DataCamp has decent quality associated with them, so you could certainly do worse if you were seeking out a certification for a career change.
Do I Recommend DataCamp?
DataCamp is a relatively affordable means of upskilling your career in data science. It won’t teach you the underlying fundamentals needed to excel (which I had gotten from my educational background in Mathematics and Statistics) but if you need to brush up on a certain methodology or a programming language, DataCamp is a decent option. The content on the courses could be found online for free with enough digging, but the added structure of the course and exercises can help concepts stick for certain people (like me).