Data has always been an important part of our decision making since ages, be it data coming from previous learning or data coming in from sophisticated softwares and artificial learning. Data Science is a process of using various complex algorithms and methods to excavate knowledge and trends and deeper insights from structured and unstructured data. Data Science uses advanced analytics and machine learning to help us make more extrapolations and predictions based on the data provided to us. This extrapolation helps us make more informed decisions and hence helps us achieve goals with higher accuracy and speed.
Data science teams are expected to answer many questions in today’s world. Innumerable businesses demand better prediction and optimization based on real-time insights backed by tools like these.
The life cycle of data Science starts with collecting data from trustworthy and relevant sources, the second step involves cleaning that raw data and putting it in formats that machines can understand. In the next phase, various statistical methods and other algorithms are used to deduce patterns and trends that come from the data. After this, models are programmed and built to predict and forecast; finally, results are interpreted.
Technological advances in artificial learning, machine learning and automation have set higher standards of data science tools for business. This results in the formation of data scientist teams – expert data scientists, citizen data scientists, programmers, engineers and business analysts – that extend across business units.
There are massive opportunities here. The automation of extremely tough data science tasks such as preparing the data, and the empowering the analysts without coding experience to construct models, this helps in keeping business agile and innovative. Creating and automation for the data science lifecycle makes the experts find free time to address more interesting and innovative areas of the field. When there is a mixture of human intelligence along with technology and automation, this combination helps businesses extract greater value from data.
Artificial Intelligence will be automating more than 50% of the data scientist activities by 2025 which will in turn ease the acute talent shortage. IBM provides Auto AI to automate data science and AI lifecycle management. Most of the time of the data scientist’s working time is spent in finding, cleaning and organising data which amounts to about 80% of their time. This job is also one of the most promising job profiles in 2019 and 2020 and for the years to come in the future.
The magnitude and diversity of social, mobile and device data, including new technologies and tools, data science today plays a broader role than ever before. Businesses consider data analytics and data science along with AI to be a technology-enabled strategy. For data science to be effective, its full lifecycle should support traditional analytics and also must work in concert with latest and modern applications. This implies that the data science practice must transform beyond routine, tedious tasks — as much 80% of a data scientist’s time is spent cleaning, shaping and moving data from place to place, often to feed machine learning. This leaves only a small percentage of their time to find patterns and trends, to build complex models, to predict and forecast, and to interpret results.
Fortunately, there is relief. The latest development in modern data science is an AutoAI capability that automates the data preparation and modeling stages of the data science lifecycle. Now, not only can more data scientists use their specialized skills the way they were intended; but more businesses can benefit from data science, from prediction to optimization.
Data Science has become a revolutionary technology that everyone seems to talk about. Hailed as the ‘sexiest job of the 21st century’, Data Science is a buzzword with very few people knowing about the technology in its true sense. While many people wish to become Data Scientists, it is essential to weigh the pros and cons of data science and give out a real picture. In this article, we will discuss these points in detail and provide you with the necessary insights about Data Science.
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