What I learned after working for 3 years as an entry level Data Analyst

(Hopefully) Some helpful lessons for fresh graduates and entry level data analysts

Data analyst — it seems like every company needs at least one or two in their team nowadays. I graduated with a Masters in Data Analytics in May 2020 (yes, in the height of the pandemic) and have been working in this area as an intern in 2019 until now, as a full time Data Analyst in 2022. I have learned a lot in these 3 years and I’m going to tell you what they are!

Lesson 1 — Interviews are scary but it doesn’t have to be

I know it is nerve racking, I have done quite a few myself and the nervousness is sometimes the worst thing in the world. HOWEVER, if you have done your research, prepare some answers for possible questions and know your life inside out (which you do because uhh it’s your life?) then take a *deep breath* and ~relax~. Remember, the interviewers are also human! It is okay to take your time thinking for an answer. Try to make it into a conversation as much as possible.

I interviewed candidates for a data analyst position recently and I could not tell you how important it is to be comfortable. They are testing your technical skills but they are also trying to see if your personality matches their group. Be yourself. TAKE A DEEP BREATH AND RELAX.

Insider tip: Your introduction does not have to encompass your entire resume and it is fine to ask for clarifications on questions.

Lesson 2 — Yes skills are great but what’s better is understanding the system your company uses and the databases first

You might think you have to know all the ways to clean a dataset or write a stored procedure on your first day but fear not, once you get the job, you will code every second of every hour that you are working. So don’t worry, those skills will come and improve with the million hours of practice. What is more important during the first few weeks is to ask questions on the data, the structure of the databases and how they are used. Write down as many notes as you need to and pay attention during the meetings.

Lesson 3 — Is the “great attention to detail” requirement REALLY necessary? The short answer is yes.

The long answer is also yes. The longer answer is, attention to detail is necessary when you are dealing with a multitude of data. Data is inconsistent. There will always be something unusual that pops up even when you use a particular data source all the time because you can’t possibly check each row in a dataset with one million rows, for example. I learned that triple and quadruple checks are good practice.

For example, I was joining two datasets from two different sources to one main dataset to complete the missing information. The size of the main dataset increases after the join. This can definitely happen if you are expecting many-to-many joins so I had to check if this is the case. It turns out that one of the external data sources had some rows with wrong information. If I hadn’t checked the size of the data frame after joining, this instance will cause problems later on. So, checking and testing the details are necessary!

Lesson 4 — Document, document, document EVERYTHING

This is THE MOST important and helpful thing I did while working. It started with me trying to jot down everything I learn so I don’t forget them but it turned into a running document of my daily work. I often work with multiple projects at a time, therefore, writing down what I did at the end of the day helped a lot when picking up my work the next day, the next week or even 2 months after. I use Google Docs and put the date as the header and project name as sub headers but there are a lot of apps you can use and style it to your liking.

Commenting what my code does WHILE WORKING ON IT also helps when you document your work at the completion of the project. I sometimes don’t even wait until I finish the project to document. I use almost every Friday to organize my code and write down the process so I can start the next week with ease. Documentation is also key when someone else has to read and use your code for future work.

Lesson 5 – 80% of your work is the prep work with the data.

I might underestimate the percentage of this but truly, after 3 years of doing data analysis work, I am confident in saying that the prep work takes the most time. Understanding the data from sources, reading the data dictionary, making sure the data is accessible, checking if we have enough data to use, cleaning them, understanding why certain columns are empty and then finally, after 5000 tests, checking if we can join them to our dataset to be used in our products. This grunt work is necessary to make sure your final result is up to par so expect these types of tasks in your daily work!

Lesson 6 — Projects not getting made are normal and expected.

This is sad but true. Sometimes you spend months conceptualizing a project, coming up with the work needed and researching methods to use to make it work, but in the end, the result is not good enough for publishing or for next steps. It is okay if your work doesn’t make it to the end, there is always more to work on :)

Conclusion

Data analysis can be messy, tedious and overwhelming at times but it is a work that can possibly solve new problems and open new opportunities with technology. So learn as much as you can and keep an open mind. Good luck!