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7 Important Trends Impacting The Future Of Data Science
7 Important Trends Impacting The Future Of Data Science

November 8, 2022

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Overview

The future of data science is looking bright!

As the world becomes more digital and technology continues to develop at a breakneck pace, it's important to watch what's coming next. It's not hard to see why the world has become increasingly data-driven. More people are turning to it for answers on how to make smarter decisions about everything from health and medicine to business, education, and more.

 

While no one can predict the future of data science for certain, looking at trends gaining traction is the best way to invest in the right direction. Let's look at some of the major data trends and think about their impact on the present and the future of data science.

Career Growth in Data Science

For a long time, Data Science has been the most in-demand occupational skill set and was associated with lucrative salaries. Tomorrow's data scientists need to be knowledgeable about automated machine learning, advanced analytics, big data, and cloud computing.

Key Trends In Data Science

  1. Python over R programming

Python is the go-to programming language for all data science processes since it has many free libraries like Pandas and NumPy. Python provides the ability to automatically match data types, which can be quite useful for simplifying data analysis. Python's flexibility also ensures that all data analysis can be built in the same programming language, from machine learning models to blockchain applications. 

 

Soon, Python will continue to dominate R in data science and machine learning fields. While Python facilitates easy integration with current software, R is more of a closed ecosystem.

  1. Increased demand for end-to-end AI solution

 

AI is becoming more human-like and natural.

The future of data science will be shaped by the increasing prominence of artificial intelligence (AI). AI is already being used to automate tasks that were once done manually. Still, as AI becomes more sophisticated, it will also be able to perform tasks that are currently only possible for humans. 

  1. Data-Driven customer experience

As our interactions with businesses are becoming increasingly digital – from AI chatbots to Amazon's cashier-less convenience stores – practically every part of our engagement is recorded and analyzed to discover ways to optimize or improve processes. This has also led to a push for greater levels of personalization in the products and services offered by enterprises. As a result, many data scientists in the future will place a high value on finding innovative ways and approaches to using consumer data to improve overall customer service. 

  1. Convergence (IoT and edge computing): 

Artificial Intelligence (AI), the Internet of Things (IoT), cloud computing, and high-speed networks like 5G are the foundations of today's digital transformation. Data is the fuel they all use to produce insights. All of these technologies exist independently, but they can accomplish much more when coupled. 

 

Artificial intelligence (AI) allows IoT devices to interact with each other without the need for human intervention, resulting in the creation of smart homes, factories, and even smart cities. 5G and other networks will enable new types of data transfer in addition to speedier data transmission. For example, data scientists have developed AI algorithms to route traffic to ensure the best possible transfer speeds and automate environmental controls in cloud data centers.

  1. Small data and TinyML

The significant increase in the amount of digital data we generate, collect, and analyze is commonly referred to as Big Data. Not only is the data huge, but the machine learning (ML) algorithms we use to process it can also be pretty large. Approximately 175 billion parameters makeup GPT-3, the largest and most complex system capable of modeling human language. This is acceptable if you are working on cloud-based systems with unlimited bandwidth, but it doesn't cover all ML use cases. This is why "Small data" has arisen to facilitate quick, cognitive analysis of the most vital data when limited time, bandwidth, or energy. 

 

This is related to the concept of "edge computing." For example, self-driving cars can't rely on the ability to send and receive data from a central cloud server when trying to avoid traffic in an emergency. Another alternative is what is known as TinyML, which refers to machine learning algorithms built to carry up as little space as needed to run on low-powered devices.

 

  1. AutoML :

Automated machine learning (AutoML) will automate various data science operations, including data cleaning, model training, predicting insights, evaluating the results, and more. These tasks are usually performed by data science professionals. Often, a data scientist's time is consumed by tedious and repetitive data cleaning and preparation processes that require data expertise. Hence, AutoML is mostly about automating these kinds of tasks, but it is also becoming more about building models and making algorithms and neural networks.

  1. Increased demand for data science professionals

The adoption of Artificial Intelligence (AI) and Machine learning will undoubtedly give rise to many new roles in the IT industries. Two of which have high scope and businesses will be more likely to hire are data analysts and data science security professionals. Although the business market already has access to a large number of experts in ML, and data science, there is still a need for more professional data security professionals who can analyze and process consumer data securely.

 

Final Words

To conclude, data science will continue to be at the forefront of emerging technologies, which means the demand for data scientists will only continue to grow. For people looking to break into the data science field, these data science trends can also serve as indicators of corners of the industry that might be worth exploring.

 



 


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