Navigating the Data Revolution: Trends in Data Science and Machine Learning

Harvard Business Review called Data Science the sexiest job of the 21st century 12 years ago, and there has been no looking back since. Not only is the industry booming, but it has also laid the groundwork for artificial intelligence as we know it today. Data Science careers are the second highest paying jobs in the US, next only to Artificial Intelligence jobs. This has paved the way for aspirants and professionals to discover lucrative and rewarding career opportunities in the domain. But before we delve into the career opportunities and how we can navigate them in the current landscape, an understanding of the latest trends in 2024 in the domain is essential. So here we go:

Latest Trends in Data Science and Machine Learning:

• Explainable AI: The two most important aspects that have emerged with the evolution of data science and machine learning are transparency and interpretability. Explainable AI techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) have taken the front stage. These enable data scientists and ML engineers to understand and communicate with complex ML models. According to Gartner, by the end of 2024, 75% of organizations will shift from piloting to operationalizing AI, and the demand for data science talent will surge proportionately.
• Quantum Computing for Data Science: Quantum Computing is likely to revolutionize data science with the power to solve complex optimizations and accelerate ML performance. A study by McKinsey estimates that quantum computing could create $80 billion to $700 billion in value across industries by 2035.
• Automated Machine Learning (AutoML):  AutoML platforms automate the process of model selection, hyperparameter tuning, and feature engineering, making data science more accessible to non-experts. Democratizing the entire process of data science development has gained considerable traction in recent years, accelerating the rate of development of data science technologies for laymen and non-technical workers. This is one of the primary causes of the surging data science hiring phenomenon.

There are several other fast-paced accelerators, such as cloud-based data engineering, robotics reinforcement learning and natural language processing, and rapid evolution in the domain has almost remained constant since its inception, and as the usage of AI gains more traction among the general population, careers in data science are only set to grow and become more rewarding soon, with certified professionals being at the top of the demand curve.

Some important statistics to consider:

If you are still considering if a career in data science will lead to professional growth and development, and whether your skills will still be in demand as a data science or data engineering professional, consider the following statistics:

• According to the World Economic Forum’s Future of Jobs Report 2020, data scientists and analysts are among the top emerging roles across industries, with a projected growth rate of 41% by 2025.
• The International Data Corporation (IDC) forecasts that the global big data and business analytics market will grow from $168.8 billion in 2019 to $274.3 billion by 2023, at a CAGR of 13.2%.
• A report by PwC estimates that AI could contribute up to $15.7 trillion to the global economy by 2030, potentially increasing GDP by 26% in China and 14.5% in North America.
• The McKinsey Global Institute predicts that by 2030, the demand for data science skills will grow by 39%, creating a talent shortage of 250,000 data scientists in the United States alone.

Navigating your way to ‘that’ perfect data science role

So, what are ways in which you can navigate your way to a rewarding and fast-paced career in this ever-growing industry? Firstly, it's not for everyone. Data scientists require a tremendous amount of problem-solving capabilities and the ability to learn and unlearn technology tools as old ones grow obsolete and new ones become mainstream. Here are some ways you can be at the top of your game in your data science career, even if you are just aspiring to enter the industry:

• Certifications: Entering at the top of the list of requirements by employers, certifications from prestigious institutions and renowned credentialing bodies remain at the top of the list of requirements for all levels of data science professionals. Showcasing skills in any domain is best done by certifications from world-renowned institutions, and data science is no exception.
• Programming Expertise:  Python, R and SQL remain at the top of the list of required programming languages that data science professionals must have. This has not changed for over a decade, and building a strong foundation and acquiring advanced skills in these languages will keep you ahead of the curve since most data science tools require basic to advanced knowledge in these languages.
• Statistical and Maths knowledge – These are the very foundations of a data science career and strong foundations in linear algebra, probability, and calculus are absolutely must-have skills for a successful data scientist
• Business and Domain Knowledge: Like almost every other profession, having the skills is not enough. Aspiring data scientists must have the necessary know-how of the business their employers are in, the functioning of their industries and a thorough understanding of business processes in which they can apply their skills.

Ready to dive to become a successful data science professional? Get ready to get ahead and certified. Embrace the data revolution, continuously upskill, and be part of shaping the future of technology and business. As the World Economic Forum emphasizes, the ability to harness the power of data and AI will be a critical driver of economic growth and social progress in the coming years.