Marco is a Data Analyst based in Berlin. Since starting with DataCamp, he has learned new data science skills, become more efficient at his job, and is more confident when presenting his analyses to company executives.
Tell us a little about your background.
I studied Economics and Marketing in school, and now I am working as a Data Analyst in Berlin at Foodpanda, a start-up in the food delivery industry. I've worked there for the past 2 years but soon I will join GetYourGuide, a fast-growing start-up in the tourism industry. Previously I worked as a Research Analyst in a marketing consultancy in Amsterdam and as a Business Analyst in a financial consultancy in Budapest.
What was your experience with data science before you started with DataCamp?
During my MSc. in Marketing Management, I learned a lot of statistical tools, but mostly using SPSS, Excel, and SQL, with some base R.
I started more advanced data science (with R) when I joined foodpanda in Berlin 2 years ago. After Googling a million answers using blogs and Stack Overflow in my first month of work, I felt it was time to try out a more structured way of learning. That is when I discovered and joined DataCamp.
DataCamp provides an effective learning experience both for someone starting from scratch and advanced users.
What drew you to data science? Why is learning data science important to you?
Data science gave me the tools to find information in an honest fashion, without relying on subjective opinions. Often our brains suffer biases and our version of the truth can be easily distorted by the context around us, but data science gives us methods to assess the real truth and measure the confidence of our judgements. It is this ability to better assess reality that inspired me to learn and expand my data science skills. I would love one day to contribute to better decision making in a social context using my skills, and that is why continuing to learn is fundamental.
What do you like about DataCamp?
I like the clarity and shortness of the presentations and the ability to use the R console directly in the platform to solve the exercises. It provides an effective learning experience both for someone starting from scratch and for more advanced users. DataCamp immensely helped me in expanding my ggplot2 knowledge, which I consider the best tool out there for data exploration and visualization. Also, DataCamp taught me new packages which entirely changed the way I conduct my daily job. For instance, I use dplyr for data manipulation and caret for machine learning almost every day now. DataCamp additionally helped me better understand the statistical and mathematical aspects of the different data science techniques.
I am also learning Python from scratch in DataCamp, stretching my skill set to better interact with my colleagues who work in object-oriented languages. All in all, it has helped me tremendously in gaining confidence to build and defend my analyses in front of management, and ultimately to get my new job.
Thanks to DataCamp, the learning process is faster than ever and the results are measurable. Just putting in a little effort can lead to a huge payoff.
Can you give us some examples of how you've used skills you've learned from DataCamp?
I recently developed an algorithm to predict the restaurant cooking time for different products. I used dplyr heavily, and caret for building the final model that went in production—both packages that I discovered on DataCamp. Another instance where I directly applied DataCamp skills in my job was making word clouds and doing other text analysis using the Twitter API. Additionally, all reports that I produce contain several plots built with ggplot, directly using the code I learnt from DataCamp.
Why buy a DataCamp subscription?
Googling solutions to specific problems can be time consuming sometimes, because users describe similar problems in different terms and you end up with tons of unstructured info which you get lost in. Additionally, it often happens that code found online is not correct or not reproducible on your local machine due to difficult-to-spot technicalities. Another pain is reading through page after page only to discover that the info you are looking for is not there.
Instead, DataCamp provides a smooth path to structurally build your skillset, covering the core and details of arguments and ensuring reproducibility by offering exercises and the R/Python consoles on the platform. Also, the fact that the core developers of the packages are the teachers ensure the absolute quality of the information. All this at a fair and competitive monthly and yearly price.
Compared to other platforms, DataCamp is much more entertaining and interactive and has courses that are taught by the best Python and R minds out there.
How does DataCamp compare to other online learning platforms you've tried?
Compared to other platforms, DataCamp is much more entertaining and interactive and has courses that are taught by the best Python and R minds out there. The courses are developed thoughtfully, with 3-5 minute videos on few topics and related exercised after it. This allows you to stay engaged and immediately practice what you learnt, honestly testing your understanding. This teaching method is highly effective for me. Also, DataCamp describes the packages' code together with the theory and ideas behind it, while other online courses do not offer the right balance of theory and practice. DataCamp also frequently releases new courses, allowing you to expand your skillset systematically. Lastly, the quality of teaching for the price is unbeatable.
What is the greatest benefit of learning with DataCamp?
A DataCamp student can, in my opinion, get a well paying and enjoyable job as Data Analyst or Data Scientist after taking the key courses on the most successful packages in R and Python. Thanks to DataCamp, the learning process is faster than ever and the results are measurable. Just putting in a little effort can lead to a huge payoff. For people already working in Data Science like me, the platform allows me to expand my skillset in unfamiliar areas and keep up with the latest developments on R and Python packages.