Photo taken by Anna Niekraszewicz
With the development of data technology in recent years, we have seen an escalate in the number of companies implementing data analytics. Many companies are now trying to recruit the best talent for their data projects to gain a competitive advantage. One such talent is a data analyst.
Data scientists have proven that they can provide enormous value to companies. But what makes data analyst skills different from others? This question is not straightforward to answer because data scientists are a enormous group and the job responsibilities and required skills vary from company to company. Nevertheless, there are skills that data scientists will need if they want to stand out from the rest.
In this article, we will discuss five key skills for data scientists in 2024. I won’t discuss A programming language Or Machine learning because these are always necessary skills. I’m also not talking about generative AI skills because those are skills that are gaining popularity, but data science is more than that. I would only discuss further emerging skills necessary for the 2024 landscape.
What are these skills? Let’s get on with it.
Cloud computing is an internet-based service (“cloud”) that may include servers, analytics software, networking, security, and more. It is designed to scale according to user preferences and deliver resources as required.
In the current data science trend, many companies have started implementing cloud solutions to scale their operations or minimize infrastructure costs. From miniature startups to enormous companies, the utilize of cloud computing has become evident. Therefore, you may start to see that your current data science job posting will require you to have experience in cloud computing.
There are many cloud computing services out there, but you don’t have to learn them all because mastering one means you’ll find it easier to navigate other platforms. If you’re having trouble deciding what to learn first, you might want to start with a larger one like AWS, GCP, or Azure.
For more information on cloud computing, read “A Beginner’s Guide to Cloud Computing” by Aryan Garg.
Machine Learning Operations (MLOps) is a set of techniques and tools for deploying machine learning models in a production environment. MLOps is designed to avoid technical debt from our Machine Learning application by streamlining the deployment of ML models in production, improving model quality and performance while implementing CI/CD best practices, with continuous monitoring of machine learning models.
MLOps has become one of the most sought-after skills among data scientists, and job postings are seeing an escalate in MLOps requirements. Previously, MLOps work could be delegated to a Machine Learning Engineer. However, the demands on data scientists to understand MLOps have become greater than ever. This is because data scientists need to ensure that their machine learning model is ready for integration into a production environment, which only the model creator knows best.
Therefore, learning about MLOps in 2024 is beneficial if you want to advance your career in the field of data analytics. To learn more about MLOps, check out KDnuggets’ first tech brief which covers everything about MLOps.
Huge Data can be described as three Vs, which include: Volume, which refers to the huge amounts of data generated; Speed, which explains how quickly data is created and processed; AND Diversity, which refers to different types of data (structured and unstructured).
Huge Data technologies have become significant in many companies because many insights and products are based on what can be done with the Huge Data they have. Having huge data is one thing, but only by processing it can companies get value from it. That’s why many companies are now trying to recruit data scientists with skills in huge data technologies.
When we talk about Huge Data technologies, these terms cover many technologies. However, they can be divided into four types: data storage, data mining, data analysis and data visualization.
Here are some popular tools that were often mentioned as essentials in job ads:
-Apache Hadoop
-Apache Spark
-MongoDB
-Rich image
-Rapidminer
You don’t have to master every tool available, but understanding a few of them will certainly kickstart your career for the better. To learn more about Huge Data technology, check out the introductory article titled Working with Huge Data: Tools and Techniques by Nate Rosidi that can accelerate your Huge Data journey.
Data scientists need technical skills and mighty domain knowledge to advance their careers. A junior data scientist may want to model machine learning to achieve the highest technical metrics, but a senior one understands that our model should deliver business value first and foremost.
Experience in a given field means we understand the industry we are working on. By understanding the business, we could better adapt to the business user, choose better metrics for the model, and design in a way that will impact the business. This will become especially significant in 2024 as companies begin to understand how data analytics can add significant value.
The problem with acquiring domain expertise is that it can only be effectively learned if we are already working as data scientists in that industry. So how can you acquire this skill if you don’t work in the industry you want? There are several ways, including:
– Taking online courses and certification in related industries
– Dynamic networking on social media
– Participation in an open source project
– Having a side project related to the industry
– Finding a mentor
– Take part in an internship
These are suggested ways to gain expertise in a given field, but you can get more imaginative to find that experience. The article “Is domain knowledge an obstacle to starting a career in the data industry?” by Vaishali Lambe can also assist you gain domain expertise.
Some may view data as numbers or words in a database without caring about the person the data describes. However, most of this data is private information that, if mishandled, could harm users and the company. This topic becomes even more significant today as collecting and processing data becomes easier.
Ethics in data science refers to the moral principles that guide how data scientists should work. This domain covers the potential impact of our data analysis project on individuals and society, which should make the best moral decision we can make. The topic typically involves bias, fairness, explainability, and consent.
On the other hand, data privacy is an area related to the legality of the way we collect, process, manage and share data. Its purpose is to protect personal data from an individual and prevent their misuse. Each area may have a different data protection framework; for example, the General Data Protection Regulation (GDPR) in Europe usually only applies to personal data in Europe.
Knowledge of data ethics and privacy has become an necessary skill for data scientists because the consequences of data breaches are severe. Nisha Arya’s article on data ethics and privacy can be a starting point for a deeper understanding of these topics.
This article discusses five necessary skills that every data analyst needs in 2024. These skills include:
- Cloud computing
- MLOps
- Huge data technology
- Domain expertise
- Ethics and data privacy
I hope it will assist! Share your thoughts on the skills listed here and add a comment below.
Cornelius Yudha Vijaya is an assistant data analytics manager and data writer. Working full time at Allianz Indonesia, he loves sharing Python tips and data through social media and writing media. Cornellius writes on a variety of topics related to artificial intelligence and machine learning.