In the rapidly evolving world of data technology, companies are increasingly leveraging data science to gain a competitive edge. At the forefront of this field stands the data scientist, capable of generating immense value if equipped with the right skills. The following article presents five key skills that data scientists should acquire to stand out in 2024 that extend beyond basic knowledge of programming languages or machine learning.
1. Cloud Computing: Many companies are migrating to cloud-based solutions in an effort to scale their businesses and cut infrastructure costs. A strong grasp over cloud computing services like AWS, GCP, or Azure, is therefore being sought after in job postings. To navigate this area, start with a leading platform and use resources like the beginner’s Guide to Cloud Computing.
2. Machine Learning Operations (MLOps): MLOps is an important collection of techniques that streamline the deployment of ML models in production, improving model quality and performance, and ensuring continuous monitoring of the models. Data scientists need to have a strong foothold in MLOps as this skill has gained paramount importance in job descriptions. The KDnuggets’ first Tech Brief offers comprehensive insights into MLOps.
3. Big Data Technologies: Given the rise of technology capable of handling large volumes of data, businesses are increasingly seeking data scientists familiar with big data. A robust understanding of data storage, data mining, data analytics, and data visualization techniques, and tools such as Apache Hadoop, Apache Spark, MongoDB, Tableau, and Rapidminer will provide a solid foundation in big data technologies.
4. Domain Expertise: All too often, the data scientist’s in-depth understanding of the industry to be served is overlooked. Grasping different industries’ business models enables data scientists to create better metrics, frame projects in impactful ways, and align better with business users. Several methods can be utilized to gain domain knowledge, ranging from online courses, social media networking, open-source project contribution to internships.
5. Data Ethics and Privacy: As data scientists work with sensitive personal information, maintaining individual privacy is paramount. It’s crucial to understand the moral principles that govern data science, such as bias, fairness, explainability, and consent, as well as the legality of data collection, processing, and sharing. Given the severe consequences of breaching ethical guidelines and privacy laws, this area has become a key focus for data scientists.
In conclusion, the listed skills of Cloud Computing, MLOps, Big Data Technologies, Domain Expertise, and Data Ethics and Privacy are predicted to be essential for data scientists in 2024. Realizing these skills can encourage a flourishing career in data science in the coming years.
