8 Emerging Data Science Trends for 2024 The field of data science is constantly expanding as data is found in almost every sector. The data science market is estimated to grow at 27.7% of CAGR, reaching a value of US $322.9 billion by 2026, according to Markets and Markets. Companies need to determine the latest trends in data science and make effective business decisions. This post will let you delve into top data science trends for 2024 and beyond that will impact the work of data scientists and other professionals. 1. Cloud Migration A cloud will be a more cost-effective, flexible, and scalable solution for data storage than anything else. Around 44% of traditional small businesses adopt hosting services or cloud infrastructure (source: TechRepublic). This usage is higher among small tech companies, with 66% using these services. Approximately 74% of enterprises show the highest usage of these services and even the numbers are projected to rise. According to Mordor Intelligence, the cloud migration market is currently worth US $232.51 billion and is estimated to reach US $806.41 billion by 2029 with a growth of 28.24% CAGR. 2. Deepfake Technology The number of deepfake technology rises by 900% per year, as reported by the World Economic Forum. This technology uses artificial intelligence to change or create fake images, audio recordings, or videos to represent someone or something else. Deepfakes can be tooled up to suspect politicians and business personalities. Governments are also starting to safeguard against this with social media regulation and legislation. 3. Companies Recruit More Data Analysts According to the U.S. Bureau of Labor Statistics, the job market of data analysts is expected to grow by 23% by 2032. It is projected that the amount of digital data produced will be 175 ZB by 2025 (source: Forbes). It is a clear indication that the need to hire professionals to analyze all this data will increase. There are numerous data analytics programs that can sort through it all. Big data is often highly complicated and doesn’t have a proper structure. So, data analysts are required to manually straighten training data before it is used by machine learning algorithms. Since AI-created outcomes are not always correct, machine learning firms often use humans to tidy up the final data. 4. Data Regulation In 2024 and 2025, there will be a huge amount of data online, and guarding data privacy becomes the main objective for every business. This is significantly important for data-sensitive industries like finance, insurance, and healthcare. New legislative acts will encourage businesses to examine their present procedures according to the new legislation. 5. Python Becomes the Go-To Programming Language Python is on track to become the main programming language for data analytics by 2024 and the coming years. The reason is that Python plays a crucial role in the data science industry. It has numerous free data science libraries like Pandas and machine learning libraries. Moreover, Python can be used to build blockchain applications.
An industry analyst firm RedMonk has ranked Python as the third most popular programming language in general. It is widely used in different fields alongside traditional applications like bioinformatics and 3D game development. So, if you aim to get an engineering job in data science in 2024, then you must focus on this language. 6. Kaggle’s Increasing Growth Kaggle has quickly become the largest online community of data scientists and machine learning practitioners across the world. It is also a leading data science competition platform with more than 15 million users from 194 nations as of October 2023 (source: Wikipedia). Many growing data scientists are now starting with Kaggle to kickstart their machine-learning journey. It also provides the chance to share data sets and join competitions to solve data science complexities with neural networks. Users can even work with other data scientists to develop models in the webbased data science workbench of Kaggle. Not just that but academic papers have also been published according to the competition findings of Kaggle. Leading projects from several competitions of Kaggle tend to keep expanding boundaries in the data science industry for better growth. 7. Defend Against Adversarial Machine Learning Adversarial machine learning or AML is a new machine learning technique that is primarily used for fraud detection and algorithmic trading. It is the process of gaining information about the features of a machine-learning system and identifying ways to change inputs into the system to get the required result. Data scientists will require protection against adversarial inputs. Understanding this technique helps in studying attacks on machine learning algorithms or models and defenses against those threats. 8. End-To-End AI Solutions According to TechCrunch, Dataiku – an enterprise AI company is worth $4.6 billion. AI startups assist enterprise consumers in tidying their massive data sets and developing machine learning models. It provides companies with deep-learning insights from huge amounts of data and automates significant data management processes. More and more businesses nowadays want end-to-end data science solutions. Wrapping Up Data science, just like any other form of science, is changing rapidly with time. It is ready for some huge innovations and adoptions. So, stay informed about the latest and futuristic data science technology trends mentioned in this post. Note that allowing people to manage information will always remain at the core of data science new trends. The industry will need professionals to create innovative solutions and adopt trends to perfect their strategies and business operations.
