Data Scientist's Lair: The Future of Data Science

While the world is marveling at the wonders of AI and, shortly, AGI, they tend to overlook the fact that Data Science laid the foundation for the models to be built on. Even today, data that goes into an LLM or AI Agent is thoroughly checked for bias and inaccuracies, and data engineering is crucial to feed data into an AI model (although that is soon changing with APIs). This, here, is just a fraction of an example of what Data Science has helped the world of technology achieve. While AI players fight tooth and nail for supremacy in the new world, Data Science is not far behind, shaping the data science future.

First, the definition

"Data Science is the study of the generalizable extraction of knowledge from data".

This seemingly semi-garbled definition, from the paper dating back almost to the turn of the century, comes from a book titled "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics" by William S Cleveland. While this remains the textbook definition, several other definitions have also been created over the years by various authors and researchers. But, Cleveland's definition is still considered the original and official definition.

So, where's Data Science in 2024?

Behind all the YouTube and news channels hoopla about artificial intelligence, Data Science has also been rapidly evolving with an exciting but uncertain future. It would have been a travesty to call Data Science having an uncertain future a couple of years ago, but with AI and data science taking mainstage in the popular media, what will the future of Data Science be? Will it be just another Apple III or will it see evolution in leaps and bounds to keep up with the rest of the technological world? Here's a look at the state of data science wearing lens facing the future:

Data Explosion and Data Visualization

The rate of generation of data is such that by next year, the global data sphere is predicted to reach 175 zettabytes. This will largely be driven by accelerated digital transformation and IoT sensor data. Data Scientists need to adapt to this ever-increasing velocity and volume of data, employing advanced Data Visualization techniques to make sense of this vast information. Innovative storage solutions, such as quantum storage and DNA data storage is largely being looked at as the second coming of data storage, and data engineers need to be ready to handle them when they go mainstream.

Living on the Edge

While edge computing hasn't taken the popular world by storm yet, it is expected to become more prominent as the volume of data blows up soon. Data Scientists are being expected to learn these cutting "edge" technologies (pardon the pun) to be ready for massive data proliferation. They also need to devise the right methodologies to ingest this data and churn out the right output for data-driven decision-making. Edge computing, literally, will become a mainstay of data engineering, if not data science altogether, according to futurists.

The Rising Prominence of Adversarial Machine Learning

While Data Scientists have been using Machine Learning algorithms for quite some time now, AML can be called the white knight in shining armor for vulnerable AI systems. By simulating White-box attacks, Black-box attacks, Targeted attacks and untargeted attacks, and much more, these are inputs that are fed into a machine learning model by Data Engineers to Machine Learning models that have been intentionally designed for the model to cause a mistake, generally improving its performance, learning from that mistake. Talk about biting the bullet to get better and more accurate ML models.

The Grand Old NLP and Deep Learning

Semantic Search and Natural Language Processing were born around the same time, but while semantic search grew as subtle as a fireworks display, NLP stayed in the sidelines, gathering steam slowly until it burst into the scene with AI. Data Scientists need to prepare for the coming data explosion and advanced NLP is a valuable tool in their arsenal. In fact, tokenization of LLMs is itself a function of NLP. Advanced NLP in Data Science will include more sophisticated language models, enhanced multilingual capabilities, Few Shot and Zero Shot Learning, integration with blockchain and decentralized systems, and even Quantum NLP. This is sure to give a shot in the arm that NLP currently needs, while the world marvels at AI, a product of whom NLP is probably the most vital component today. Data Science will gain tremendously from the rapid advancements in NLP and Deep Learning as time goes by.

Low Code, No Code

Largely a closed arena till now, the democratization of Data Science with several low code and no code platforms, the likes of DataRobot, Mendix, Appian, Microsoft Power Platform and even Google Cloud Auto ML is putting the gift of data science knowledge and mechanisms in the hands of ordinary employees and the general public. This democratization of Data Science is expected to grow manifold in the next 2-3 years as more data science processes are automated and human involvement slowly reduces.

Going Quantum

What Data Scientists call a "quirky quagmire" might just save data science from the proliferation of expansive amounts of data. In fact, the industry joke goes "Quantum Computing, where the bits are in a superposition of being both zero and one, much like a data scientist's confidence level when explaining their model to non-technical stakeholders." Quantum Computing includes several abstract yet practical concepts like Superposition, Qubit entanglement, Quantum Algorithms. Futurists are already talking about Quantum Supremacy over computers. This will be a tremendous gain for data engineers, especially those handling preprocessing before ingestion of data.

XAI!

Ever had someone trying to explain the innards of an AI model to you? Or a Data science process model? Or ETL? Have you tried explaining to someone, how it all comes together and functions so well? Well, the future of Data Science will involve XAI (Xplainable Artificial Intelligence) in the hands of the layman, giving them an understanding of the foundational basis of AI – with explainable Data Science.

These are just a few advancements in Data Science that seem less obvious than continuous learning, deep learning and ethical AI. What is explained for the reader are the actual functions and predictions of Data Science in 2024 and beyond.

A word about Professional Certifications and Career in Data Science

A professional certificate in data science is almost like a modern amulet against the capricious gods of the job market. In a "datafying" world, and that's where you need professional certifications as a data scientist. A professional certificate in data science is your symbolic representation of your preparedness for the new world order of technology.

In the end, the certificate itself is less important than what it represents: a commitment to understanding the language of our time. Whether that understanding comes through formal certification or other means is, perhaps, a data point for future generations to analyze as they consider a Career in Data Science.