Five things I wish I knew before getting into data
My first week after graduating from the immersion Data Analytics course is almost over and while I am getting adjusted to a new learning routine, I have been spending some time reflecting on some things that would have probably contributed to smooth my transition into data.
I also realized that this might be helpful for other people who are about to start their journey into data analytics, so here are the top 5 things I wish I knew before getting into Data Analytics
I WISH I KNEW…THAT THE BASICS MATTER
During my first Master’s year, I had classes in general statistics and economics statistics. These subjects required me to pass two exams which were compulsory to move to the next year.
The challenging thing about those courses was that I was thrown into the subjects without having attended any fundamental courses. I remember the frustration of feeling lost because I did not understand the basics. While studying and trying to memorize as many concepts as possible, I realized that my chances of passing the exam (written + oral) were low. To avoid the overwhelmingness I decided to change strategy and focus on some key topics that would have been tested in the written part of the exam and registered myself to all the available exam dates. I took the written exam three times (at any available chance!) and attended the public sessions of the oral exams to note down every single question asked by the professor.
Once back home, I looked out for the definition and broke down the problems or topics to make it easy for me to understand the process. By going through this strategy, I realized that some of the topics were not so complex as they seemed but quite recurring connected, I just needed the time to learn and understand the fundamentals, before moving to more complex topics.
Today, while studying data, the experience is similar. To succeed, I need to understand the basics first.
While conducting descriptive statistics, I sometimes wished I had access to more theories and contexts behind the topics.
My learning is that I become confident about a topic or a subject the moment I’m able to understand the logic behind it and explain it to an audience with no prior knowledge. Communication is key and once simple words are used to describe a more complex topic, this topic becomes more accessible.
I WISH I KNEW… HOW POWERFUL EXCEL FUNCTIONALITIES ARE!
During high school, I attended an ECDL basic course. One module was about Spreadsheets. At the time I underestimated the power of this tool and could not get the best out of the course.
I have been using Excel almost daily at work, since my first internship. Mostly to keep track of progress, inventories or expenses. But except for some tricks and shortcuts, I do not have the feeling that my Excel proficiency improved during this time.
I got introduced to the world of data by using Excel in the very first exercises of the data analytics program. I suddenly realized how helpful it would have been to progress ahead with the ECDL course and gain more expertise in creating graphs, applying functions, and creating pivot tables.
But I’m happy to have brushed up my knowledge and curious to see how this tool will develop further with additional useful functionalities.
I WISH I KNEW… WHERE TO FIND GOOD DATASETS FOR MY PROJECTS
Not sure if there is a secret path to finding the perfect dataset while learning data in a Bootcamp, but I wish I knew where to spot free, interesting, and reliable datasets.
Through most of the data analytics program, most of the datasets were made available through given project briefs, except for the last achievement for which I had to come up with my project (and dataset) from scratch. It took my ages to find one, and even when I found one, it did not fulfill the required criteria for the analysis I was supposed to conduct,
Despite U.S. government public available datasets, E.U. datasets, Kaggle, Google Analytics, the most interesting datasets I found are available on paid platforms (which make sense, as probably there is a lot of work behind those sets).
Here, I might have underestimated the fact that working on a project or case study I’m passionate about probably implies creating an ad-hoc dataset — For example: if I liked to see whether health-related activities have increased during the pandemic, I could start working by downloading my FitBit activities files or asking Nike Run Club to send me the data of my last year of performance and organize them in a way that I’m able to measure the progress and eventually compare it to one of the previous years.
The key learning here was that looking for a suitable dataset is part of the responsibilities of a Data Analyst and I have very much concretely experienced this part of the job while working on my project idea
I WISH I KNEW…HOW AND WHERE TO KEEP TRACK OF THE RESOURCES I ENCOUNTERED ALONG WITH MY STUDYING PHASE
During my journey, I talked about shifting to data analytics with family, friends, and colleagues. Some of them advised me on resources, recommended (free) online courses, readings, newsletters, challenges, and communities to join.
Sometimes I noted it down on my phone, in a notebook, forward the information as an Email, sent an instant message to someone else, wrote it down on my agenda.
At the beginning of this journey, everything was so new and I didn’t think about creating a system to track all this useful information.
I quickly realized that I was losing track of some information and soI started transcribing it in the same place (Excel). Sadly, I felt overwhelmed by the number of resources I had to recall and collect, especially the ones I might have lost or missed out.
At the beginning of my journey, I wish someone had told me how precious shared information is and informed me about tools to pin all these links and names.
I WISH I KNEW (EARLIER)… I’LL NEVER STOP LEARNING!
DATA IS A NEW EVOLVING SCIENCE, THERE ARE AND WILL BE ALWAYS NEW THINGS TO LEARN. EVERYTHING IS ABOUT THE LEARNING PROCESS.
One reason why I signed up for a data analytics Bootcamp instead of enrolling back to university was the realization that there will be no right time and amount of knowledge to make a career shift easier.
Like many technology fields, data is an evolving science and there will always be a new tool or programming language to learn or new trends to follow.
At this point in my life, the best combination for getting started is to keep up with learning new things, trying to understand all the relevant systems, simulate and learn them, to be able to and apply them in real contexts. And then starting over. Some topics will be easier to understand and memorize, others will need more time and effort.
Reaching the end of the last achievement felt like a race against the deadline, but once the program was over I quickly realized that there isn’t any race we can win when we run against time.
We should stop rushing through and take the learning path more holistically, prioritizing the importance of learning new things, make mistakes, relearn and improve what we know.
Becoming aware of these things helped me get more clarity on my path ahead: I will focus on learning and consolidate the fundamentals and start a few side courses to deep dive into statistics and SQL skills (The Art of Statistics and Serious SQL).
