Data. One four-letter word that carries so much weight; 2.5 quintillion bytes each day, to be precise. This insane amount of information generated every day is the fuel for organizations worldwide – running sales and marketing, financial accounting, human resource management, executive decision making, social policy planning, and so much more. If you’ve done something as simple as using the internet today and browsed for a mobile phone, you’ve generated data. How is it that organizations filter through these humongous piles of data with the smallest of actions gathering information? It’s the data scientist at work. And life as a data scientist is anything but typical.
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Not a usual day at work
A data scientist is a data-hungry and data-loving professional who can gather, sort, and analyze valuable information for their organization. He or she needs to be an expert in several fields – statistics, Big Data, R programming, SAS, and Python – other than data science.
When your field involves solving unusual problems for clients and businesses, your working day would be anything but usual. Data scientists deal with different problems that need flexibility, creative thinking, and adaptability as essential qualities. So then, precisely what does a data scientist do?
Working with Data
Unsurprisingly, a significant chunk of time is devoted to understanding data problems and finding solutions.
Identify the Data Science Problems
The first step that a data scientist takes is to identify a business problem or data science problem. To do this, they need to look through various perspectives and ask multiple questions to arrive at the right set of questions that will bring unique insights. What does a data analyst do? Use these unique insights to plan data models and analysis to address the pain point. The business or data problem is framed from the business or stakeholder perspective and not that of the data scientist.
Gathering Raw Data
The next step is to find sources of data from where they can acquire all the relevant information. They may need to dig through data pipelines, look through several subjects and topics, and assimilate all the information in a single place. If the information they’re seeking is readily available with the organization, they may not need to gather additional data.
Data scientists may also conduct interviews and feedback surveys to get firsthand information and create new data sets. The function of gathering, cleaning, and categorizing data takes up the maximum amount of time, sometimes up to seventy percent of their day.
Choose the Approach to solve the problem
If you’re wondering what does a data manager do, here’s your answer. Once the data has been gathered and organized, the data manager selects a suitable solution approach to the problem. Several algorithmic, mathematical, and statistical approaches are at their disposal – two-class classification, multi-class classification, regression, clustering, reinforced learning algorithm, and more.
Conduct in-depth analysis
The above functions may seem tedious, but a data scientist builds computer models and programs to perform all of them. A critical responsibility of a data scientist is to design customized products and automated machine learning models to gather and organize information relevant to the problem. Digitization and machine learning helps the data scientist to resolve business problems through high-quality insights and stimulate better decision-making.
Working with People
It’s important to understand that a data scientist works in complete isolation at no point in time. Data science in real life involves business problems being solved by teams of experts. Everything about a data scientist’s job is data-related, and so are the meetings with various teams – internal and external.
While a significant portion of the job entails working with data, the end goal is resolving a business problem. And to that end, the data scientist works together with the strategy team. Often, these stakeholders aren’t data experts. Therefore, a data scientist needs to have moderately good communication skills to explain his or her findings in a more straightforward, non-technical language. Presentations and flow charts work as visual demonstrators, and so, the data scientist is generally good with creating these.
Working with the Industry
Wondering if there’s more to what data scientists do? Yes. The world’s systems are in a constant state of flux. And as such, the data also gathered varies in number and nature. Data scientists have to be flexible and willing to work with change. New information is constantly being collected, and sometimes, new data models need to be created to sort through data and get relevant inputs. A data scientist keeps up with newsletters, industry blogs, government policies, discussion forums, conferences, networking sessions, and peer groups to find out and gauge the extent of change.
Working with Growth
With several technology-driven companies setting up shop in the Middle East, the demand for data scientists has risen considerably. The Covid-19 pandemic also sent several businesses into a tailspin. But data science has helped keep up with the changes, constantly sharing information on ways to tackle problems and develop new solutions. The industry is so popular; it’s expected to be worth USD 64 billion in 2021 and more than USD 100 billion in 2027. (Statista)
Here’s how you can grow as a data scientist:
- Junior Data Scientist: They work to develop core technical skills such as SQL and Python, use models for data visualization and work with specific data problems instead of ambiguous ones. Junior data scientists are given a task to complete and aren’t equipped to find a new one.
- Associate Data Scientist: At the mid-level, they become improved contributors dealing with more significant projects and a better understanding of business problems. Instead of running queries, they’d be planning and designing new models. An associate data scientist has more autonomy with choosing tasks.
- Senior Data Scientist: With years of experience, a senior data scientist is an ultimate step in this career path. They’re expected to lead teams, be highly accurate with data and models, and often develop solutions from start to end. They’re generally the ones who participate in strategic meetings and fully understand the business problem.
Data Scientist, an exciting career option
Being a Data Scientist is one of the most exciting, knowledgeable, and glamorous jobs in the business world today. Organizations realize the importance of data in decision-making. Don’t know which markets to reach? Data will help. Don’t understand your target audience’s buying behavior? Data will provide insight. Don’t know how to change a car tire? Data will save your day. From the smallest to the most significant decisions, data will ensure you have your solution. And providing those answers is the most fulfilling aspect of this role.
Most organizations look for data scientists with advanced degrees. You can become a data scientist, too, and you don’t need previous experience in coding. The Professional Certificate Program in Data Science for Business Decision Making by upGrad is a foundation course that teaches you to start from scratch and become a next-gen data science professional. With a cutting-edge curriculum delivered by distinguished faculties, insightful industry projects, and knowledge-testing case studies, this course will equip you with everything you need to reach the top of the data science career path.
The ability to gather data, understand it, take it, and process it for more straightforward understanding and solving huge business problems will be an even more critical skill in the post-pandemic world. Find out what do data scientists do and make your mark by becoming a data science professional.