The Biggest Data Science Trends In the year 2022 - NullClass

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The Biggest Data Science Trends In the year 2022

The Biggest Data Science Trends In the year 2022


The 5 Biggest Data Science Trends In 2022


  • The emergence and initialization of data science as a field of study and practical application over the last century has led to the development of the technologies like deep learning, natural language processing, and computer vision. Speaking on a broader perspective, it has enabled the emergence of machine learning (ML) as a way of working towards what we refer to as artificial intelligence (AI), a field of technology that’s quickly transforming the way we work and live.
  • Data science encompasses the theoretical and practical application of the ideas, inculcating Big Data, predictive analytics, and artificial intelligence. If the data is the oil of the information age and ML is the engine, then the data science is the digital domain’s equivalent of the laws of physics that cause the combustion to occur and pistons to move.
  • A key point to recall is that as the significance of comprehending and understanding how to work with data grows, the science behind it is becoming more accessible. A few years ago, it was considered a niche crossover subject straddling statistics, mathematics and computing, which is taught at a handful of universities. In today’s day, its importance and significance to the world of business and commerce is well established, and there are many ways and routes, including and inculcating online courses and on-the-job training, that can equip us in order to apply these principles. This has led to the much-discussed “democratization” of the data science, which we will undoubtedly see the impact and the effect many of the trends mentioned below, in the year 2022 and beyond.

Small Data and TinyML


Tiny Machine Learning: The Next AI Revolution | by Matthew Stewart, PhD Researcher | Towards Data Science


  • The rapid growth in the amount of digital data that we are generating and enamating, collecting, and analyzing is more often than not referred to as Big Data. It isn’t just the data that’s huge, though – the Machine Learning algorithms we use to process it can be quite big, too. GPT-3, the largest and the most complicated system capable of modeling the human language, is made up of around 175 billion parameters.
  • This is the fine if you’re working on cloud-based systems with unlimited bandwidth, but that does not by any means cover all of the use cases where Machine Learning is capable of adding value. This is why the concept of “small data” has emerged as a paradigm to facilitate and enable fast, cognitive analysis of the most vital data in situations where time, bandwidth, or the energy expenditure are of the essence. It’s very closely linked to the concept of the edge computing. Self-driving cars, for instance, cannot rely on actually being able to send and receive data from a centralized cloud server when you are trying to avoid a traffic collision in a state of emergency situation. TinyML refers to the machine learning algorithms designed to take up as little space as possible so they can easily run on low-powered hardware, close to where the action is. In the year 2022 we will see it appearing in an increasing number of the embedded systems – everything from wearables to the home appliances, cars, industrial equipment, and agricultural machinery, making them all smarter and way more useful.


Data-driven Customer Experience


Data driven customer experience: Delivering value at the right time


  • This is about how the businesses take our data and make use of it to provide us with increasingly worthwhile, valuable, or enjoyable experiences. This could actually mean cutting down friction and hassle in an e-commerce, more user-friendly interfaces and front-ends in the software we make use of, or spending less time on hold and being transferred between the different departments when we make a customer service contact.
  • Our interplay and interactions with businesses are becoming increasingly digital – from AI chatbots to Amazon’s cashier-less convenience stores – this means that often every aspect of our engagement can be measured and analyzed and think for insights into how processes can be smoothed out or made more enjoyable. This has also led to a sort of drive to create greater levels of personalization in goods and services being offered to us by the businesses. The pandemic sparked a wave of investment and innovation and creation in online retail technology, for example, as businesses looked to replace and interchange the hands-on, tactile experiences of bricks ‘n’ mortar shopping trips. Finding new types of methods and strategies for leveraging this customer data into better customer service and new customer experiences will be a point of focus for many of the people working in the field of data science during the year 2022.


Deepfakes, generative AI, and synthetic data


AI Generated Synthetic Media, aka deepfakes | by Ashish Jaiman | Towards Data Science


  • During this year many of us were tricked into believing Tom Cruise had started posting on TikTok when scarily realistic “deepfake” videos went viral. The technology behind this is called as generative AI, as it aims to generate or to create something – in this type of case, Tom Cruise regaling us with tales of meeting Mikhail Gorbachev – that doesn’t exist in reality. Generative AI has very quickly become embedded in the arts, and entertainment industry, where we have actually seen Martin Scorsese de-age Robert DeNiro in The Irishman and (spoiler alert) a young Mark Hamill appear in The Mandalorian web series.
  • In the year 2022 I expect that we will see it bursting and rushing into many other industries and use cases. For instance, it’s actually considered to have very huge potential when it comes to creating synthetic data for the training of other machine learning about algorithms. Synthetic faces of people who have never actually existed can be created and built to train facial recognition algorithms while you are avoiding the privacy concerns involved with using real people’s faces. It can be created and developed to train image recognition systems to spot signs of very rare and infrequently photographed cancers in the medical images. It can also be used to create and develop language-to-image capabilities, allowing and enabling, for instance, an architect to produce the concept images of a building simply by describing how it will look in words.


January 6, 2022

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