5 Essential Skills Every Data Scientist Needs in 2024
Photo by Anna Nekrashevich 

 

With the advancement of data technology in recent years, we have seen a surge in businesses implementing data science. Many companies now try to recruit the best talents for their data project to gain a competitive advantage. One such talent is the data scientist.

Data scientists have proven themselves able to provide massive value to companies. However, what makes data scientist skills different from the others? It’s not an easy question to answer as data scientists are a big umbrella, and the job responsibilities and the skills required differ for each company. Nevertheless, there are skills that data scientists will need if they want to stand out from the others.

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.

 

 

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.

You can learn more about Cloud Computing with this Beginner’s Guide to Cloud Computing article by Aryan Garg.

 

 

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. To learn more about the MLOps topic, refer to KDnuggets’ first Tech Brief, which discusses everything about MLOps.

 

 

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. To learn more about Big Data Technologies, here is an introductory article called Working with Big Data: Tools and Techniques by Nate Rosidi that could kickstart your Big Data journey.

 

 

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

These are suggested ways to acquire domain expertise, but you can be more creative to find the experience. The article “Is Domain Knowledge a Hurdle to Start a Career in Data?” by Vaishali Lambe can also help you get domain expertise.

 

 

Some might see data as numbers or words in the database without concern for the individual that these data describe. However, much of this data was private information that could harm the users and the business if we mishandled it. The topic is becoming even more important in this modern era as data collection and processing become easier.

Ethics in data science is concerned with the moral principles that guide how data scientists should work. The field covers the potential impact of our data science project on individuals and society, which should follow the best moral decision we could take. The topic usually concerns bias, fairness, explainability, and consent. 

On the other hand, Data Privacy is a field concerned with the legality of how we collect, process, manage, and share data. It aims to protect the personal information coming from the individual and avoid misuse. Each area might have a different data privacy framework; for example, the General Data Protection Regulation (GDPR) in Europe usually applies only to personal data in Europe.

Ethics and Data Privacy knowledge have become essential skills for data scientists, as the consequences of breaking them are severe. The article from Nisha Arya on Ethics and Data Privacy could become your starting point for understanding these topics further.

 

 

This article discusses five essential skills that every data scientist needs in 2024. The skills include:

  1. Cloud Computing
  2. MLOps
  3. Big Data Technology
  4. Domain Expertise
  5. Ethics and Data Privacy

I hope it helps! Share your thoughts on the skills listed here, and add your comment below.
 
 

Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and Data tips via social media and writing media.

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