When you consider a new career, you often do a lot of research about that career path and what it takes to get into it and succeed in it. In life, there are career paths that are clear and direct. You know what you should learn, what you need to practice, and what you will do once you get a job. For example, if you want to be a math teacher, you must work on your math and communication skills.
You have a clear roadmap of what you need to do to get to your end goal. This clarity can make a lot of difference in the amount of time and effort you must put in to have a successful career. That said, it’s not always easy to get a clear, step-by-step roadmap of a specific career path. This is especially accurate when the career path you’re considering is in tech.
In general, the tech field — and data science — is vast, with many divisions and subfields that can vary quite a lot. That’s why navigating the tech field is not often as simple as navigating some other fields. But today, let’s focus on data science as a career path. Data science is an umbrella term often used to describe various career paths that have something to do with data.
But, within data science, there are different fields and specialties that you can pursue. Because of this variety, it is often confusing to try and have a clear vision of what being a data scientist actually entails. Prove of that can be found by how many times I, and I am sure other data scientists, get asked, “so, what do you actually do as a data scientist?”.
In this article, I will explain some of its means to be a data scientist. I will go over some of the tasks that most data scientists do as part of their daily job.
№1: COLLECT DATA
At the beginning of every data science project, scientists need to gather and collect data, often from different resources — from the web or a database — to analyze and apply algorithms. Sometimes, the data scientist doesn’t collect the data themselves but will prepare it for analysis from their client or company. This process is a basic and essential skill you must master as a data scientist’s first step, just as foundational as A brief overview of how Git works for aspiring developers.
№2: CLEANING THE DATA
Once we have the data, we will need to perform some preprocessing on it before we move on to analyzing it, namely, cleaning the data. When the data is collected from different resources, it often contains incorrect, corrupted, or duplicated entries. These values can cause some significant errors in the results. That’s why removing them leads to better, more accurate results. There are various steps in the data cleaning process, such as fix structural errors, removing duplicates, and filling in missing data.
№3: ANALYZING THE DATA
After we get our data in a clean, structured form, we can move on to the next step, data analysis and pattern finding. This step often contains different forms of visualizations and applying statistical or/ and logical techniques to determine if the data contains any patterns or anomalies that may help us determine the best algorithm or model to apply next to obtain accurate results.
№4: APPLY A MODEL TO THE DATA
Now, we are finally getting to the interesting part of every data science project and the step where you feel the particular specialty your project belongs to. You will need to use the information you obtained from the previous step to decide on a good model to apply, and your model can vary depending on your application and your desired results.
№5: INTERPRETING THE RESULTS
This step goes hand-in-hand with the previous step; you will have some potential model candidates, then apply them to your data and check the results. Choosing the correct model will have a great effect on the results you’re going to obtain. But, once you obtain these results, you will need to make sense of them and use them to predict future data or make important business decisions.
№6: COMMUNICATE THE FINDINGS
As you may be thinking now, the steps applied for any data science project may appear straightforward, but they actually need a lot of thinking, analyzing, and practicing. And after all that effort, you must deliver and communicate your results to your supervisors or clients. So, another essential skill a data scientist needs to work on is science communication and visualization. This step is crucial because it can make or break your project. If your communication skills are not good, you won’t show your hard work or share your results effectively.
FINAL THOUGHTS
Some career paths have a clear job description. If you ask someone what they do and say they are a math teacher or a surgeon, there often needs to be follow-ups on what they actually mean. But, if you ever say you’re a data scientist, then 90% of the time, you will get asked what that means or what you do as a data scientist.
The reason behind this vagueness is the generality of the term data scientist, which is used to describe different roles and job descriptions. A data scientist, in simple terms, is a person who deals with data and tries to use it to make better business decisions. And despite the different available roles within data science, from being an analyst to machine learning expert, or big data expert, almost all data scientists use data to make better sense of the world.
In this article, I tried to simply put to words what data scientists actually do daily and what being a successful data scientist requires. So, next time someone asks you, “what do you do as a data scientist” you will have a good way of answering them.
