Why Data science trends in 2020

Data science is not a single term. It covers a wide range of topics and networks. B. Internet of Things, deep learning, AI, etc. Simply put, data science counts as an overall combination of data inference, computational algorithms, analytics, and technologies that help solve a variety of business problems. It also provides enterprises with advanced tools and technologies that can automate complex business processes related to raw data extraction, analysis, and presentation. Engineering and data generation are taking place at a very fast pace, so it is important to be aware of the latest and future trends in data science.

Kaggle CEO Anthony Goldbloom predicts that the data center will be replaced by a departmental or business-specific team. Meanwhile, Professor Thomas H. Davenport of Babson College claims that artificial intelligence (AI) will improve in 2020. When asked about data trends, AI remained at the top in 2020. We've created a list of data science trends to keep you up to date on the evolution of data science that will make your business a huge success.

Artificial intelligence and smart apps

AI is becoming the mainstream technology for both small and medium-sized businesses and will thrive in the coming years. Currently, the adoption of artificial intelligence is still in its infancy, but the full implementation of more advanced AI is expected in 2020. The reason AI is growing rapidly is to help businesses improve their overall business processes and better process data from customers and consumers.

The use of AI remains a challenge for many, but researching the development of this technology is not so easy.

In 2020, we will introduce innovative apps that can improve the way we work, based on AI, machine learning, and other innovations. Another phenomenon set to take over the industry is automated machine learning. Help transform data science through better data management. Therefore, special training may be required to perform deep learning.

IoT Growth

IoT technology investment is expected to reach $ 1 trillion by the end of 2020, which clearly illustrates the development of smart and connected devices. In 2019, we also used apps and devices that could control home appliances such as air conditioners and TVs. At this time, IoT alone may not be able to do this. If you come across a smart device that can automate common things such as B. Google Assistant or Microsoft Cortana, you know that the Internet of Things is always in the spotlight of users. Therefore, companies can invest in the production of this technology, especially smartphones that take full advantage of the IoT.

Evolution of Big Data Analysis Big data research cannot be ignored when it comes to data science, as it helps companies gain a competitive advantage in their data and reach their goals. Today's enterprises use a variety of tools and technologies to analyze big data, especially Python. Organizations are also focused on identifying the root cause of the particular incident that is occurring. And this is where Predictive & Business Analytics comes in. This helps businesses predict what will happen in the future

For example, predictive analytics can help identify customer preferences based on purchase or browsing history. Based on this, you can find smarter approaches to attracting new customers and retaining existing ones.

Edge computing is expected to increase

Sensors are currently driving edge computing

However, with the advent of the IoT, edge computing will be taken over from traditional cloud systems. Edge computing helps businesses store streaming data close to data sources for real-time analytics. It also provides an excellent alternative to big data analytics that require high-end storage devices and higher network capacity. As the number of data acquisition devices and sensors grows rapidly, companies are turning to edge computing because they can solve bandwidth, latency, and security issues. The integration of edge computing and cloud technology can provide a structured system that helps mitigate the risks associated with data analytics and management.

Demand for Data Science Security Professionals

Artificial intelligence and machine learning implementations have led to many new industry positions. One of the most challenging positions is the Data Science Security Professional position. Both artificial intelligence and ML are completely data-dependent, and data scientists need to be proficient in data science and computer science to process this information efficiently. While the enterprise market already has access to many data management and IT professionals, there is still a need for experienced data security professionals who can safely process customer data. For this reason, data security scientists need to be familiar with the latest data science or big data analytics techniques. First of all, Python is one of the most widely used languages ​​in data science and data analysis, so a clear understanding of Python's concepts can help you address data science security issues.

Final Word

Data science has become one of the emerging sectors of all industries, especially the IT industry. Therefore, companies implementing data science methods and innovations need to stay on top of the latest trends.