Innovation in Future of Data Science

In Today’s Engineering Industry, Data Science has become one of the most important area to learn and master in. The amount of Data which is acquired is in the number quadrillions and also the rate at which the Data is Captured is increasing exponentially.

Data Science is a broad spectrum covering so many areas, a Data Scientist should be expert or well versed in all these area. The Venn diagram of Data Science is shown below.

Data Science and Big Data, from simple statistics have evolved or paved way for Artificial Intelligence (AI) and Machine Learning (ML).

By 2012, the role of “data scientist” had become the “most impressive job of the century”, Between 2011 and 2012, there was a 1500% rise in the data scientist job listings, indicating the growing need of such specialists and the increasing popularity of data science as a theme — a stream of technology that has become incredibly useful and developed.

Data Science practice into four different strategic areas:

• Datasets typically covers data governance, strategic data sources and infrastructures.
• Skillsets are about measuring analytics readiness, managing talent, spreading an evidence-based culture (e.g. creating a shared language), applying Data Science processes, and designing the organizational framework of Data Science teams.
• Toolsets cover the selection of the proper Data Science tools and the application of the best practices throughout the organization.
• A Mindset that assembles the animating principles that support the ethos of a Data Science function to deliver value and innovate at the source of a digital transformation.

Mindset for Innovation in Data Science —

Data Science encompasses a diverse set of disciplines and does not work in isolation. In order to collaborate with others, specialists need to learn beyond their domain of expertise. In fact, the practice requests a developed sense of curiosity to understand the language of other disciplines and a strong appetite for collaborative learning. These characteristics of a ‘polymath’ or ‘generalized specialist’ prepare scientific teams to engage with people with different roles in an organization from product management, design, marketing, legal, communication, engineering, finance, and more.

A generalized specialist brings the advantage of interdisciplinary knowledge, which fosters creativity and a firmer understanding of what society, an organization or a business needs. A team of generalized specialists brings better overall perspective for deep, complex and unconventional areas than a team of experts can.

Data Science Trend for the Future -

AI and Data Science-

The advancement AI 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 Science and 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. All these require and use Data Science for accurate results and execution.

Cybersecurity -

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 science is one of the fastest growing fields in all industries. That’s why it’s critical for businesses adopting the technologies to remain fully up-to-date with the latest trends. The data science trends outlined will undoubtedly be at the forefront for some considerable amount of time. Staying on top of them will help us to analyze where weneed to improve our business processes in order to achieve maximum growth and ROI when deploying these technologies.