Key Trends Impacting the Future of Data Science

eWEEK DATA POINTS: Data science also helps businesses use advanced tools and technologies to automate complicated business processes linked with extracting, analyzing and presenting raw data. With rapid advancements in technology, and data being generated at warp speed, it is crucial to stay current and be able to anticipate upcoming trends.

Artificial intelligence (AI) and machine learning (ML) are experiencing massive growth as companies increasingly look for fast, cost-efficient and innovative ways to use the big data at their disposal. But in order to effectively deploy these technologies, companies’ teams must stay up to date on the latest trends in data science.

Today, the term “data science” covers AI, ML, the internet of things, deep learning and others. In simple terms, it’s a combination of data inference, algorithm computation, analysis and technology that helps in solving complex business problems.

Data science also helps businesses use advanced tools and technologies to automate complicated business processes linked with extracting, analyzing and presenting raw data. With rapid advancements in technology, and data being generated at warp speed, it is crucial to stay current and be able to anticipate upcoming trends.

Here’s a list of the top five data science trends that your company should be preparing for and will push your business to new heights in 2020 and beyond. Industry information for this eWEEK Data Points article was compiled by CEO Martijn Theuwissen of data science learning platform developer DataCamp, whose clients include 3M, Credit Suisse, Deloitte, Ikea, Intel and Uber.

Data Point No. 1: Acceleration of AI in Business

During the past few years, AI has gradually been adopted as mainstream technology for both small and large businesses, and there’s every indication that will continue over the next few years. Today, we are in the beginning stages of using AI, but by the end of 2020, it’s likely we will see even more advanced applications of AI across scientific fields and business industries. What’s driving this rapid growth is the fact that AI allows enterprise-level companies to dramatically improve the effectiveness and efficiency of their business processes and operations. AI also delivers huge advances in managing customer and client data.

Deploying AI technology for customer service will continue to be challenging for some businesses with limited financial and people resources, but for those willing to make the investments, the return will most noticeably pay off in advanced apps developed with AI—and machine learning and other technologies that will profoundly change the way we work.

Automated machine learning is another trend that will make significant inroads in the months ahead as it helps to transform data science with improved data management. This will drive a need for more specialized training for aspiring data scientists to help them understand and work to execute deep learning.

Data Point No. 2: Rapid Growth in the IoT

Investments in IoT technology are expected to reach $1 trillion by the end of this year according to a report by IDC—a clear indication of the anticipated growth in smart and connected devices. Many people are already using apps and devices to control their home appliances like furnaces, refrigerators, air conditioners and TVs. These are all examples of mainstream IoT technology—even if users aren’t aware of the technology behind them. Smart devices such as Google Assistant, Amazon Alexa and Microsoft Cortana allow us to easily automate everyday tasks in our homes. It’s only a matter of time before businesses use these devices and their business applications and start investing more in this technology. The most likely advancements will be seen in manufacturing, such as applying IoT to optimize a factory floor.

Data Point No. 3: Evolution of Big Data Analytics

Effective big data analysis undeniably helps businesses gain a significant competitive advantage and helps them achieve their primary objectives. Today, enterprises use different tools and technologies such as Python to analyze their big data. Taking it a step further, we see more businesses focusing on identifying the reasons behind certain events that take place at present. That’s where predictive analytics play a major role by helping companies identify trends and predict what can happen in the future. Examples include using predictive analysis to help identify customer interests based on their purchasing and/or browsing history. Sales and marketing professionals can analyze those patterns to create more targeted strategies to attract new customers and increase the retention rates for current ones. Companies such as Amazon also use predictive models to stock warehouses given demand across neighborhoods

Data Point No. 4: The Rise of Edge Computing

Today, sensors are largely responsible for pushing edge computing to the forefront. This advancement will continue due in large part to the growth of the IoT as it takes over mainstream computing systems. This technology offers businesses the opportunity to store streaming data near the sources and analyze it with real-time capabilities. 

Edge computing also offers an effective alternative to big data analytics that demand high-end storage devices and much bigger network bandwidth space. With the number of devices and sensors collecting data increasing exponentially, more and more companies are adopting edge computing due to its capabilities in terms of resolving issues related to bandwidth, latency and connectivity. Further, combining edge computing with cloud technology provides a synchronized infrastructure that can minimize and mitigate risks connected to data analysis and management.

Data Point No. 5: Increasing Demand for Data Science Security Professionals

AI and machine learning adoption will undoubtedly give rise to many new roles in the IT and high-tech industries. One that will be in high demand as a result is data science security professionals. The business market already has access to many experts who are proficient in AI, ML, data science and computer science, but there is still a need for more professional data security professionals who can analyze and process data to customers securely. In order to perform those functions, data security scientists must be well versed with the latest technologies like Python and the other most commonly used languages in data science and data analysis. Having a clear understanding of Python concepts can help you tackle the problems related to data science security.

Data Point No. 6: Conclusion/Takeaways

Data science is one of the fastest growing fields in all industries. That’s why it’s critical for businesses adopting these technologies to remain fully up-to-date with the latest trends.The five data science trends outlined above will undoubtedly be at the forefront throughout 2020. Staying on top of them will help you to analyze where you need to improve your business processes in order to achieve maximum growth and ROI when deploying these technologies. 

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