Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from both structured and unstructured data and to identify actionable insights from data across a broad range of applications. 

Data science is related to data mining, machine learning and big data and is one of the most in-demand skills in the cyber security industry. Data science can be applied to multiple industries in a wide range of ways for different purposes. 

Key Trends In Data Science In 2022 Are Expected  To Include:

Customer Experience Powered by Data:   Companies are now starting to use data in order to understand their customers and create better customer experiences. Data is used to create personalised customer experiences, which will be tailored to the needs of the individual consumers.  What this means is that companies will be able to offer more personalised versions of their products or services, which in turn makes them more likely for retailers to buy them.

To provide the most pleasant shopping experience to their customers, businesses will use the predictive capabilities of data and improve customer delight. Artificial Intelligence (AI) chatbots combined with data can provide quality customer service without any workforce. Such data-driven practices are already happening in some industries, with many retail companies using data analysis techniques for improving customer experience.

Focus on Convergence:   An emerging trend in data science is that it has started to converge with other disciplines such as AI, business intelligence, and digital marketing as well as technologies like IoT, cloud technology and 5G in order to provide even faster and improved results.  This convergence is also called data science 2.0. Data scientists in the past were able to predict future trends with the help of data analytics and mathematical modeling alone. But now they can use machine learning and artificial intelligence in order to make better predictions.

Small Data:   The concept of “small data” is the idea that the focus should not be on collecting all the data, but instead on extracting insights from small data sets. The modern world is flooded with an ever-increasing amount of data. With AI, this large quantity of data can be effectively analyzed to find patterns and trends that could have been missed if only larger sets were being analyzed.

Some companies are analyzing satellite images to get real-time information on crop conditions, while others are analyzing traffic patterns in order to optimize transportation flows. As data usage comes to new fields like driverless vehicles, compressing that data for easy transmission also becomes essential. So creating efficient transmission of data for emergencies will also be a new trend. 

Demand for Data Scientists:   Data science has been the fastest growing job sector for years. And it’s expected to remain so therefore leading to an increasing demand for data scientists. Data scientists work with a variety of tools to extract information from raw datasets, analyse it, and then produce insights that can be used by other people to solve business problems or answer questions about the world around us.

 AutoML:   In the future, the AI will be smarter and more efficient. Automated Machine Learning, popularly known as AutoML, is the newest trend in ML. The whole idea is that given a dataset, an algorithm can learn how to create predictive models automatically. AutoML has already been implemented at Microsoft, Intel, and Salesforce. It is important to keep an eye on this because it could soon change the entire landscape of ML as we know it.

Source:  Cyber Security Intelligence