Job Spotlight: What’s New in Data Science?

Job Spotlight: What’s New in Data Science?

The pandemic brought to the fore a slew of sweeping changes that companies need to do for them to survive. With industries having no choice but to modernize, they needed an anchor where all the latest technological innovations were to take shape. Enter data science. 

You may have heard of terms such as “big data,” “data analytics,” “artificial intelligence,” and “data-driven” in most conversations in enterprise and introduced in everyday life through media. These terms spell out principles in data science – a field that stands front and center in today’s rapidly modernizing world. 


Data Science and Its Importance  

Data science seamlessly merges statistics, mathematics, computer programming, artificial intelligence, advanced analytics, and machine learning with a particular subject matter to draw actionable insights from an organization’s data. Data-driven entities use these insights for making vital decisions and strategic planning. 

With data scientists comprised mostly of experts obsessed with numbers and coding, the Harvard Business Review regards being a data scientist as the sexiest job in the 21st century. It is because many organizations have become more reliant on data scientists to make sense of their data and provide actionable insights to improve business outcomes. 

The next question is, as a data scientist, how do you make the most out of the sexiest job of the century? What data science trends should you watch out for in a continuously disrupted world of work? What future trends in data science have a lasting impact? 

Here are five trends that will give you a glimpse of where data science will be in the next few years.  


What’s New in Data Science?  

1. Small Data and Tiny Machine Learning

While you may have heard about Big Data and Machine Learning, these sweeping concepts do not necessarily cover all use cases in the field of data science.  

There is a shift in paradigm that tackles “small data,” evolving from the need to facilitate quick, cognitive analysis of vital data in instances where bandwidth, expenditure, and time, are of utmost importance. On the other hand, tiny ML refers to machine learning algorithms designed to consume as little space as possible for them to run on low-powered hardware.  

You can find direct use cases of small data and tiny machine learning in embedded systems such as self-driving cars, home appliances, agricultural machinery, and industrial equipment, among others. These technologies cannot utilize unlimited bandwidth all the time. Hence, small data and tiny ML would be much more useful for analyzing and running applications in these cases.  


2. Data-Driven Customer Experience

Everyone’s interactions with businesses are increasingly becoming more and more digitized. From convenience stores that no longer utilize human cashiers to e-commerce stores that deliver using robots, more innovative practices in customer experience are created regularly.  

This trend has created a need for an increasingly personalized customer experience. As a result, many companies are searching for fresher methods and more innovative strategies to leverage customer data. This is also why they hired more data scientists to help them use customer data to create worthwhile, valuable, and personal buying experiences.  

Today, even top brands need to employ cutting-edge data scientists to deliver outstanding customer experience. As a data scientist, you must understand that this is no longer an exception but a requirement for many familiar brands to stay above the fray.  


3. Generative Artificial Intelligence

You must have been shocked like the rest of the world when terrifyingly “deepfake” videos started circulating online. These deepfakes were so convincing that people who know very little about the advances in artificial intelligence were convinced of these videos’ seeming authenticity. Deepfakes are a common example of generative AI.  

Generative AI intends to create something that, in reality, does not exist. It is fairly common in the entertainment industry and is often used in movies and television. It also gives rise to another emerging trend in data science – the creation of synthetic data.  

Synthetic data uses machine learning algorithms to resemble “a real thing.” It enables data scientists to create faces of people who have never actually existed. The medical field is one industry where this may be utilized, specifically in image recognition systems.  


4. Merging of AI, Cloud Computing, Internet of Things (IoT), and 5G

For digital transformation to truly thrive, there needs to be a convergence of technologies that used to exist separately. When these technologies combine, they make each other stronger and more useful. AI has enabled IoT devices to work seamlessly and with the least human intervention possible. This combined power creates smart homes, organizations, and cities.  

On the other hand, data scientists will play a vital role in fulfilling the combined promise of 5G and IoT. The AI algorithms you will create will enable the optimal transfer of data at ultra-fast speeds and give birth to aggregated, real-time data processing in the cloud. Eventually, this combination will become a norm instead of a premium service. 


5. Automated Machine Learning

Have you ever dabbled in “Auto ML?” Automated Machine Learning or Auto ML is an emerging new trend that is paving the way toward the “democratization” of data science. This means that Auto ML can develop tools everyone can use to create more widespread and commonplace ML apps.   

If you are an experienced data scientist, dabbling in Auto ML can be a double-edged sword. Auto ML aims to empower non-data scientists such as doctors, economists, and entrepreneurs to utilize ML to improve their respective fields. It can empower individuals with no coding experience to create their innovations by utilizing machine learning.  

Simply put, automating the machine learning process makes it more user-friendly while promising to provide more accurate outputs than coded algorithms. Interestingly, by automating ML, what makes data science sexy will be demystified since it will be possible for more people to tap into data science’s various functions. 


Data Scientists’ Role in Digital Transformation 

You can be assured that data scientists will remain one of the most sought-after jobs in the digital world today and in the years to come. There is no turning back from the current trend wherein companies and organizations must create more value and insights with their data. 

As organizations continue to innovate and the market becomes more mature, your role as a data scientist evolves as more trends emerge. Arming yourself with relevant skills connected with the five trends identified in this blog allows you to consistently flow in your career as a data scientist as you continually drive innovation in your sphere of influence. 



Are you looking at maximizing your success as a data scientist? Consider partnering with Davis Companies and benefit from our team’s dedication, integrity, and willingness to go above and beyond what is typical to ensure your success. Being the preferred partner of many tech professionals like you, Davis Companies is committed to making your professional success our priority. Reach out to Davis Companies today. 

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