Terms like “Analytics” and “Data Science” have become buzz-words these days. Everyone wants to know about the next big trend in data, as well as how they can get involved – and who can blame them? Earlier this year Glassdoor published an article highlighting the 50 Best Jobs in America, according to the job openings that it sees. Data Science was on the top of the list. Also on that list were titles including Analytics Manager, Data Analyst, Data Engineer, and Business Analyst. So, clearly jobs dealing with data are in high-demand.
The global impact of data across academia and industry has given rise to multi-disciplinary professionals eager to gain necessary skills to make data-backed decisions. For healthcare and clinical research fields, the need for statistical and computational resources is even greater. No wonder then, that careers in clinical biostatistics and SAS programming are expected to grow by 20%, according to the US Bureau of Labor Statistics.
The exponential growth of volume and variety of data generated today present unprecedented challenges as well as opportunities for organisations. Companies are now relying on technologies like text analysis and Natural Language Processing and text analysis for making sense of such massively collected data.
Data science is the study of collecting, processing and extracting value from big and diverse data sets. It enables the scientists to create data-driven solutions to boost profits, reduce costs and make solutions to various problems.
Data cleaning is an important part of data analysis. This is done to eliminate, modify, or restore data depending on its state. Data that is corrupt or redundant apart from duplicate files is removed. Inaccurate data is identified and sorted. Incomplete data is marked and modified. Back up of data is taken before cleaning it to prevent loss of information.