In the 1960s, data science rose as a discipline that refers to gathering, understanding, and interpreting large sums of data over time. Little did early practitioners know that the next 50 years would see a boom in data collection. Now, a career in data science is gaining attention and demand.
Industries realize how much data they can create, amass, and process for various uses. It’s projected that jobs within data science will increase by 36 percent between 2021 and 2031.1 This makes a data science career undeniably promising. However, for professionals to succeed in this field, they must be well-guided in this path. Your company must not only attract the best data science professionals out there, but also hone their skills and keep them for a long time. This is why you must develop a foolproof data scientist career for them.
Is there a data scientist in your company or anyone willing to upskill or reskill for the role? Or do you have employees who might enjoy being data analysts or data engineers? Here are some ideas you can explore to map a career path for them.
What Are the Prerequisites to Be a Professional in Data Science?
Before we discuss the elements of a data science path, let’s first look at the characteristics that a data scientist must have:
- Patience with problem-solving through numbers. Let’s start with a soft skill. While patience is a skill required in any profession, a data scientist’s focus is dealing with massive amounts of data. In this role, a lot of number crunching is expected. It mainly involves identifying the proper data, asking the right questions about datasets, developing an algorithm, and extracting information, among others. Data scientists see messages, connections, and solutions between numbers, so they must have the patience to make sense of them.
- Ability to visualize and communicate data. While it’s safe to say that trustworthy data will always back up their findings, how they deliver them is the next challenge. Data scientists must be able to present analyses and recommendations using proper data visualization and presentation tools.2 Is your data architect speaking at the next town hall in front of employees who are not as well-versed in analyzing data? Or does your data analyst have to explain critical company statistics to investors? This is where the choice of presentation media, from pie charts to 3D plots, matters. Data visualization is crucial because if others cannot understand presented data, all efforts to do data mining will go to waste.
- Basic knowledge of programming. Aside from data visualization, programming is one of the most important skills that data scientists should have.3 Apps and programs that collect and present data make them efficient and dependable. For example, when processing big data, it’s best for the person who created the algorithm for data processing will also present the findings and suggestions. That is why a data scientist must have basic Python4 knowledge or any other programming language, such as R Programming, Excel, or SQL.5
Related reading: What’s New in Tech: In-Demand Tech Skills Employers Are Looking For in 2023
What You Should Know About Mapping a Data Science Career Path
Now, how can you provide your data scientists with a career path with clear opportunities for growth, mentorship, and longevity? Here are some ideas to get you started:
Offer a career path to those with a solid background. Hard work and passion are some of the best traits of a promising professional, but let’s face it. Without a proper background and technical skills in a role, many will have to work twice as hard as others to succeed in data science. In fact, Albert Romero, an analyst at Cambrian AI Research, says that a data science job cannot be offered to anyone. A data science professional must possess intelligence, education, and knowledge.6
When looking through candidates or evaluating which employees deserve a data science upskill or reskill, assess their educational background, skill set, and passion for the craft. It’s also a good idea to conduct a thorough interview on where they see themselves with their career in a few years.
Make sure that the career progression is clear. A great way to ensure that a budding data analyst or software engineer will stay with the company for a long time is to show them their potential future. It also gives a sense of confidence in the employee that the company is truly invested in them, showing that you take career mapping seriously.
Part of this exercise is also goal setting. List down the training and mentorship in store for the data scientist. Laying down a training plan and its completion period gives all stakeholders a common reference of what to do and when. It also puts “positive pressure” on employees that goals must be met before a deadline. Their adherence to meeting deadlines is a good sign that they are serious about their career.
Leave some room for them to explore their career. One way to keep your data scientists engaged is to show them how they can shape their professional path. Encourage them to look for training that can give them business analyst skills. They can also take short courses about other ways to manipulate raw data and explore frontiers in data engineering. They can attend data science workshops and seminars, too, to expand or update one’s skills. Giving them a hand in “personalizing” their career allows them to build their passion for the profession. It will also warm their heart if your company is willing to fund any data science course they’d like to take, or you can give it as an annual incentive.
FIND THE BEST DATA SCIENCE PROFESSIONALS AND MORE THROUGH FOCUS PEOPLE.
Laying down a person’s career path means we are invested in their success. We at Focus People have always been committed to helping candidates and companies thrive. We tailor-fit our hiring process, so whether it’s business analytics or software engineering that you need, trust us that we’ll find the next person to find success in that field. This is our promise, for our promise is the focus we put toward success.
We are Focus People, and we focus on YOU. Contact us now.
References:
1 “6 In-Demand Data Scientist Jobs in 2023.” Coursera, https://www.coursera.org/articles/data-scientist-jobs.
2 Castañón, Jorge. “10 Visualizations Every Data Scientist Should Know.” Medium, Towards Data Science, 8 Nov. 2019, https://towardsdatascience.com/10-viz-every-ds-should-know-4e4118f26fc3.
3 “Importance of Software Skills in Data Science.” School of Data Science, https://datascience.virginia.edu/news/importance-software-skills-data-science.
4 “What Is Python? Executive Summary.” Python.org, https://www.python.org/doc/essays/blurb/.
5 Bradford, Laurence. “What Is SQL? A Beginner’s Guide to the SQL Language.” Learn to Code With Me, 13 Jan. 2023, https://learntocodewith.me/posts/sql-guide/.
6 Romero, Alberto. “The Hard Truth: Data Science Isn’t for Everyone.” Medium, Towards Data Science, 7 July 2021, https://towardsdatascience.com/the-hard-truth-data-science-isnt-for-everyone-1689b7c05e62.