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What it is like to be a Data Analyst today – Adesola Akindeji Have you ever wondered what it’s like to be a data analyst, and what exactly it is we do as a data analyst, well, I am about to take you through the details using my role as a case study. We are going to go over a range of topics in this article; like why data analytics, how your background could help you in a data analyst role, and the tools we use on a daily basis. But without further ado, let’s get at it! 1.	A background in data science 2.	A day in the life of a data analyst 3.	A career in data analytics and the future 1. A background in data science What drew me 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 Facebook, Alibaba, Instagram, and Google to “enhance the user experience” by targeting adverts based on browsing habits. My opinions changed once I started working for my current NGO, which uses data analysis for a good cause. My organization analyzes (students' experience with lecturers/teachers, staff performance, in our network of schools, some construction projects, easing of road traffic, service attendance related data to predict growth pattern, service impact on worshippers, new converts establishment, the impact of branches on their host nations worldwide), and many more, JUST WHAT I DO. This really changed my perspective on data analysis and made me feel like I’m making an actual difference in the world. How has my background in computer science helped me? To work as a data analyst, you need to master at least one of the main programming languages. The analysis for our attendance data and the rest as stated above are done by 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 ground in the languages on which I’ve built.

what do data analysts do? I like to think of a data analyst as an ‘interpreter’. Someone who is capable of interpreting what numbers means into plain English in order for a company to improve their business by taking credible decision. Personally, my role as a data analyst involves collecting, processing, and performing statistical data analysis of over 200,000 people that gathered in the service every Sunday. What do I like about being a data analyst? What I like most about my current job is working with high-end AI software that is capable of telling me what happened with about 200,000 people that gathered for a service. 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. 2. A day in the life of a data analyst A typical day at work? Usually, my day doesn’t start until I’ve finished my 30 minutes morning devotion. It’s more like a ritual of getting into “the zone” which is likened to the feeling of taking black coffee… before I start working with massive amounts of attendance data. A typical day usually includes, but is not limited to, meetings with the CEO, 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 simplest part of the process, because the service databases I work with are easily accessible—and 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, in fact, it is usually 70% of the project time, but understanding the data is crucial in order for me to start processing the data, which makes the data cleaning part more important. 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 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 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. My experiences with Excel? While Excel is a powerful tool in data analysis, it still has a lot of serious limitations. Excel cannot 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 R and Python. However, if you are a newbie, you can start with excel.

Taking you through the use of 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 able to do you good. Allow me to take it deeper, R is one of the most frequently used tools in data science and machine learning. Over the last few years, R has become a very important data science tool. It’s used frequently to unlock patterns in large blocks of data and was designed to make our work easier. It is 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. Steps to take when beginning a new analytics project and how I know if I am asking the right questions and performing the right analysis? 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, University precisely and that really helps when it comes to connecting the dots and making educated conclusions regarding the data that’s being processed. How important it is to be familiar with the organization 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 NGO sector if I didn’t have the relevant background experience. Thankfully, in addition to my education in computer science and graphic design, I am also a member of the organization for 8 years before I started working for them which definitely help when it comes to analyzing the attendance.

3. Having a career in data analytics and what the future holds Where can I see data analytics heading in the future? 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. I don’t want to get political, but the more ‘data’ you have on someone, the more you can control their lives.

Where I see myself in 5 years. Honestly, I don’t know. 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 are not limited to: •	Data Management Professional •	Data Engineer •	Business Analyst •	Machine Learning Researcher/Practitioner •	Data-oriented Professional But of course, each one of these categories can branch out to subcategories which can open up more career opportunities. The impact I want my skill to have on the world. If I wasn’t convinced that my current work is important for improving how people see things, then I would have kept my old job as a database administrator. I believe that what I do helps save lives and helps gives certain people hope to keep living and live well, and the thought itself makes me feel like I’m helping in creating a better world. What You Should Do Now if you are trying to follow a data analyst career path 1.	Get a hands-on introduction to data analytics courses. 2.	Take part in live online data analytics events with industry experts. 3.	Talk to a program advisor to discuss career change and find out if data analytics is right for you. 4.	Discover how to become a qualified data analyst in just 4-7 months—complete with a job guarantee. 5.	Call me back to thank me on LinkedIn. https://www.linkedin.com/in/adesola-akindeji-184038135