Keen to find out what it’s actually like to work in data? Ever wondered what a day in the life of a data analyst is really like?
We touched upon a range of topics; from why he got into data analytics, to how his background helped him in his current work. We also got a fascinating insight into his particular role within his company, and the tools he uses on a daily basis.
If hearing about Radi’s life as a healthcare data analyst has made you think “I could do this job!”, then why not get a taste by actually trying it out? CareerFoundry’s free data short course is great way to dip your toe in the field.
But without further ado, let’s get into a day in Radi’s life as a data analyst.
- A day in the life of a data analyst
- Getting into data analytics
- A career in data analytics and the future
1. A day in the life of a data analyst
Hey Radi! Can you walk us through a typical day at work?
Usually, my day doesn’t start until I’ve finished my first cup of coffee. It’s not about the caffeine as much as it is a ritual of getting into “the zone” before I start working with massive amounts of medical data.
A typical day usually includes, but is not limited to:
I’ll typically meet with the analytics team to discuss the tasks of the day and brainstorm for possible solutions. When everything is clear, I start working on the data. Analyzing data consists of three main tasks: gathering the data, cleaning the data, and finally processing the data.
Depending on the problem I’m working on, gathering data is usually the most simple part of the process.
This is because the medical databases I work with are easily accessible—so I don’t have to worry about searching for it.
Cleaning the data, which is the next step, simply means going through the data and trying to understand it, making corrections where needed such as moving outliers or data that should not be included in the analysis.
This step can take a lot of time, but understanding the data is crucial in order for me to start processing the data.
The data processing part of the process is where I get to use my programming skills, which I use alongside several different data tools. I use these skills and tools to analyze the work and come up with solutions for the problem at hand.
What are a data analyst’s day-to-day responsibilities?
My role involves:
- Gathering data
- Cleaning data
- Processing data
- Producing reports
- Spotting patterns
- Collaborating with others and setting up infrastructure
How much of a role does data cleansing play in your processes?
Cleaning data is a very important process because you need to recognize which data should stay and which should not.
Including incorrect data while processing it might give you the wrong results, which in turn can lead to coming up with the wrong solutions. You then have to repeat your work, which is a waste of your time.
How often do you meet with stakeholders to discuss business needs and new things to analyze?
Personally, I don’t have to meet with any stakeholders—that’s the job of my team members. The only people I collaborate with are the analytics team because we need to keep each other updated on how things are going.
What are your experiences with Microsoft Excel?
While Excel is a powerful tool in data analysis, it still has a lot of serious limitations. Excel can’t handle datasets above a certain size, and does not easily allow for reproducing previously conducted analyses on new datasets.
The main weakness of such programs is that it was developed for very specific uses, and do not have a large community of contributors constantly adding new tools. This is why I prefer using and R and Python.
Tell us more about R and Python!
R and Python are the two most popular programming languages used by data analysts and data scientists.
Both are free and open source. R is used for statistical analysis, and Python is a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are both godsends.
To go into a bit more detail, R is one of the most frequently used tools in data science and machine learning. Over the last few years R has become the golden child of data science.
It’s used frequently to unlock patterns in large blocks of data and was designed by people like me, statisticians, to make our work easier. It’s one of the most must-know programming languages in the field of data analytics and data science.
Python is also one of the most popular languages in data analysis. Since my job deals with machine learning, artificial intelligence (AI) and predictive analytics, Python is an ideal language because it’s widely used in scientific computing, data mining, and others.
What steps do you take when beginning a new analytics project?
Usually, for my work, there are certain logical steps that I follow to reach the desired outcome.
Sometimes it’s not very straightforward, and that’s when meetings with the analytics team come in handy. As long as everything seems to run smoothly, then you’re most likely on the right path.
Logical thinking is the main process involved. I learned mathematical logic in school and that really helps when it comes to connecting the dots and making educated conclusions regarding the data that’s being processed.
How important do you think having a familiarity with the industry you’re working in is?
Understanding of the inner workings, processes, procedures and other key aspects of a company is a very important thing when it comes to data analytics.
It would have been really hard for me to do my job in the medical sector if I didn’t have the relevant background experience. Thankfully, in addition to my education in computer science and graphic design, I have a background in chemistry and biology which definitely help when it comes to analyzing DNA sequences.
If you’d like to hear another professional talk about a day in the life of a data analyst, check out this video as we follow Senior Data Scientist Tom Gadsby, as he goes through his day:
2. Getting into data analytics
What drew you to the world of data analytics?
Before I started working in the field, my understanding was that data analysis is used by companies to target specific consumers, or as a way for companies like Meta and Google to “enhance the user experience” by targeting adverts based on browsing habits.
My opinions changed once I started working for my current company, which uses data analysis for a good cause. My company analyzes DNA related data in young people to predict future diseases such as Alzheimer’s, Diabetes, Crohn’s, and many more. This really changed my perspective on data analysis and made me feel like I’m making an actual difference in the world.
How did you become a data analyst?
To work as a data analyst, you need to master at least one of the main programming languages.
The analysis for our medical data is done by an AI software that we built and continue to improve using two programming languages: R and Python. These are powerful statistical programming languages used to perform advanced analyses and predictive analytics on big data sets.
They’re both standard languages in data analytics, and my computer science studies in university certainly gave me a good grounding in the languages from which I’ve built on.
In your words, what do data analysts do?
I like to think of a data analyst as a ‘translator’. It’s someone who is capable of translating numbers into plain English in order for a company to improve their business. Personally, my role as a data analyst involves collecting, processing, and performing statistical data analysis to help my company improve their product.
What do you like about being a data analyst?
What I like most about my current job is working with high-end AI software that analyzes DNA sequencing. It’s such a complex task and I’ve always liked puzzles. It takes a lot of creativity and problem-solving skills to be able to think outside of the box and find new solutions.
I like being challenged, and I love the thrill of finding a solution to a problem that we spent months trying to solve. It’s that sense of accomplishment that makes me love my job.
3. Having a career in data analytics and what the future holds
Where can you see data analytics heading in the future?
You’ve heard it a lot, but I’ll repeat it: data analytics is the future, and the future is now!
All the actions you do on your computer, smartphone or tablet are recorded and collected by a data analyst somewhere who is trying to make their business flourish. That’s right—every mouse click, keyboard button press, swipe or tap is used to shape business decisions.
Everything is about data these days. Data is information and information is power.
Where do you see yourself in 5 years?
Honestly, I don’t know. A year ago, I couldn’t have imagined living and working in Berlin, but here I am, and it’s the same regarding my work.
As long as the effort I put into my work is worth the reward then I will keep doing it until it’s not. And fortunately, my education in computer science and graphic design will always be in demand, so I try not to worry about the future and just enjoy the moment for now.
What does career progression in data analytics look like?
There are various professional possibilities that people in data analytics can aim for.
Some of these possibilities are, but not limited to:
- Data Management professional
- Data Engineer
- Business Analyst
- Machine Learning Engineer
- Data-oriented Professional
But of course, each one of these categories can branch out to subcategories which can open up more career opportunities.
What impact do you want your skill to have on the world?
If I wasn’t convinced that my current work is important for improving peoples’ lives, then I would have kept my old job as a graphic designer.
I believe that what we do will someday eliminate or find a cure for many of the diseases that are incurable at the moment, and the thought itself makes me feel like I’m helping in creating a better world.
Am I a good fit for a career as a data analyst?
You can also get a feel for what it’s like to work as a data analyst by taking CareerFoundry’s free introductory short course.
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