Essential Skills Every Data Scientist Needs in 2024
With the advancement of data technology in recent years, there has been an increase in the number of businesses implementing data science. Many businesses are now attempting to attract the best talent for their data projects in order to gain a competitive advantage. Data scientists are one example of such talent.
Data scientists have proven their ability to provide enormous value to businesses. What distinguishes data scientists’ skills from those of others? It’s a difficult question to answer because data scientists are a broad category, and the job responsibilities and skills required vary by company. Nonetheless, if data scientists want to stand out from the crowd, they will need certain skills.
This article will discuss five essential skills for data scientists in 2024. I would not discuss Programming Language or Machine Learning as they are always necessary skills. I also don’t talk about Generative AI skills as those are trending skills, but data science is bigger than that. I would only discuss further emerging skills essential for the 2024 landscape.
What are these skills? Let’s get into it.
1. Cloud Computing
Cloud computing is a service over the internet (“Cloud”) that could include servers, analytical software, networking, security, and many more. It’s designed to scale to the user’s preferences and deliver resources as required.
In the current data science trend, many companies have started implementing cloud computing to scale their business or to minimize infrastructure costs. From small startups to big companies, the usage of cloud computing has become apparent. That’s why you can start to see that the current data science job posting would require you to have cloud computing experience.
There are many cloud computing services, but you don’t need to learn everything, as mastering one means navigating to the other platforms more easily. If you have difficulty deciding which to learn initially, you could start with a bigger one, such as AWS, GCP, or Azure platform.
2. MLOps
Machine Learning Operations, or MLOps, is a collection of techniques and tools for deploying ML models in production. MLOps aims to avoid the technical debt from our Machine Learning application by streamlining the deployment of ML models in production, improving model quality and performance while implementing best practices in CI/CD, with continuous monitoring of machine learning models.
MLOps has become one of the most sought-after skills for data scientists, and you can see the surge of MLOps requirements in job postings. Previously, the MLOps works could be delegated to a Machine Learning Engineer. However, the requirements for Data Scientists to understand MLOps have become bigger than ever. This is because Data Scientists must ensure that their machine learning model is ready to be integrated with the production environment, which only the model creator knows the best.
That’s why learning about MLOps in 2024 is beneficial if you want to advance your data science career.
3. Big Data Technologies
Big Data can be described as the Three V’s, which comprise Volume, which refers to the massive quantities of the generated data; Velocity, which explains how fast the data is produced and processed; and Variety, which refers to various data types (structured to unstructured).
Big Data technologies have become important in many companies, as many of the insights and products rely on how they can do something with the Big Data they have. It’s one thing to have big data, but only by processing it can companies get value from it. This is why many companies are now trying to recruit data scientists who possess big data technology skills.
Many technologies are included in these terms when we talk about Big Data Technologies. However, it could be categorized into four types: data storage, data mining, data analytics, and data visualization.
Here are some popular tools that job postings often listed them as necessary:
- Apache Hadoop
- Apache Spark
- MongoDB
- Tableau
- Rapidminer
You don’t need to master every tool available, but understanding a few of them would certainly launch your career for the better.
4. Domain Expertise
Data scientists need technical skills and strong domain expertise to advance their careers. A junior data scientist might want to model machine learning to achieve the highest technical metrics, but the senior one understands that our model should bring business values above everything else.
Domain expertise means we understand the industry’s business we are working on. By understanding the business, we could better align with the business user, select better metrics for the model, and frame the projects in a way that impacts the business. In 2024, it’s especially become more important as businesses start to understand how data science could bring significant value.
The problem with acquiring domain expertise knowledge is that it can only be effectively learned if we are already working as data scientists in that industry. So, how could one acquire this skill if we are not working in the industry we want? There are a few ways, including:
- Taking online courses and certification in related industries
- Active networking in social media
- Contributing to the open-source project
- Having a side project related to the industry
- Finding a mentor
- Take an internship
In 2024, data scientists must have a diverse skill set that includes more than just programming and machine learning. Cloud computing, MLOps, big data technologies, and domain expertise are all necessary skills for success in the rapidly evolving field of data science. By honing these skills, data scientists can position themselves as valuable assets to businesses looking to leverage data for strategic advantage.