According to a report, data science was named the sexiest profession and the most popular STEM career. This has led to many wonders about who are data scientists and what exactly they do? Like every IT profession, data scientists have an aura of mystery surrounding their roles and responsibilities. They are at the forefront of technology and innovation. They use data to make better decisions, help solve problems and improve lives. They work with all kinds of data — from databases to social media to the latest machine learning models.
Data scientist spends a lot of time in front of their computer screen, but they don’t just stare at it all day long.
- They have to be able to work fast and efficiently, so they’re constantly looking for ways to optimize their workflow and make sure they’re getting the most out of their time.
- They also develop new algorithms or frameworks for analyzing data or create new models for analyzing different data types.
- They may work with other professionals such as software developers to create products and services based on their findings.
With so much change occurring in the field of data science and statistical analysis, there has never been a better time to pursue a career as a data scientist. Check out the data scientist certification course to discover more about data science tools and learn the in-demand skills data scientists use in the real workplace.
Now coming back to the core topic.
Let’s take a look at the life of a data scientist. I’ll explain the different stages of their career, the typical office environment, and what they’re likely to be doing on a day-to-day basis.
Who is a data scientist?
A data scientist is a person who uses data to solve problems and find solutions. They use their skills to look for patterns in large sets of data, and then they interpret those patterns to derive meaningful information that can help inform decisions about the future.
Generally speaking, data scientists work with various kinds of data, including
- structured and unstructured data
- Numerical and categorical data
- Metadata such as names, dates, and locations
Data Scientists, also responsible for curating datasets so that they’re easy for other people to use – for example, by adding descriptions or tags so that others can easily find what they’re looking for.
What role do data scientists play?
The daily tasks of a data scientist revolve around data, which is not surprising considering the job title. They spend a lot of time obtaining data, analyzing data, and manipulating data, but in a variety of methods for various purposes.
The key roles of a data scientist are depicted below:
1. Defining data science problems and collection of raw data
One of the most critical tasks that any data scientist takes along the data science workflow is identifying and describing the business problem by asking the right questions. This is the most crucial part of a data scientist’s task. It cannot deliver the proper insights for better business decision-making unless a data scientist asks the right questions. After all, it’s how the actual process starts.
A data scientist must understand stakeholders’ pain points and frame data science problems accordingly. To produce a data product, they gather domain knowledge from stakeholders and mix it with data and technical knowledge.
The next crucial task is to identify the data sources into which they must delve to obtain all necessary data and gather all relevant information in one place that may be required in the future. After collecting raw data, a data scientist cleans and organizes it to fix errors, remove missing values, and detect duplicate information.
2. Exploring datasets and looking for approaches
Having collected all data and understanding the problems, a data scientist investigates the best and most effective methods to answer questions. The best and most efficient solutions
might not always be the same, and thus it is the role of a data scientist to develop a trade-off solution. They have to choose the best and optimal method for every data science problem.
There are several algorithmic approaches that can be used to solve a problem, such as regression and clustering, and so on.
3. Perform an in-depth analysis
A data scientist’s job is to help businesses make better decisions by building automated machine learning pipelines and personalized data products that fit the needs of each client.
As soon as the data is analyzed, the data scientist can extract useful information. There are open-source tools and frameworks libraries in Python and R that can be used to analyze data, derive high-value insights and make smarter business decisions.
Before coming up with the optimal solution to a data science problem, a data scientist explores several models and methodologies.
4. Communicate with a variety of stakeholders
The life of a data scientist revolves around meetings with stakeholders and non-experts.
After fine-tuning and optimizing the model to get the best results, the next most important thing for a data scientist is to communicate the results clearly so that different stakeholders can understand them. This may be a trivial part of a data scientist’s day, but it is highly important because your ultimate goal is to solve problems, not build models. As they say, a picture is worth a million datasets.
They create presentations with a proper flow to communicate the story that the data can tell in an easily understood and compelling manner to the stakeholders.
As it turns out, the life of a data scientist is anything but boring. It is full of excitement and intrigue. It’s a job that requires you to be constantly on the lookout for new opportunities to learn, grow, and improve. Given their varied responsibilities and the complexity of their work, one day can be vastly different from another. This means that one of the biggest challenges for data scientists is simply staying focused—and finding ways to combat boredom!
You also noticed that there are no typical workdays. A large part of the reason is that a data scientist’s job is not confined to any specific activity. A data scientist may start the day by answering questions from team members, fixing bugs in software, writing code for a new tool, or developing advanced machine learning algorithms. It all depends on what the data scientists need to do their job. If you’re convinced that this job is for you and you’re flexible enough to take this on, consider taking Learnbay’s data science course in Bangalore, which is designed for working professionals, along with job placement opportunities.