DATA SCIENCE

2023-04-13

DATA SCIENCE

Data science combines multiple disciplines that enables businesses to process huge amounts of structured and unstructured data in order to detect patterns and apply the information learned in real world applications. Data science uses statistics, data analysis, and machine learning to analyse data and to extract knowledge and insights from it. This allows businesses to efficiently manage costs, identify new market opportunities, and boost their market advantage.

Data science combines multiple disciplines that enables businesses to process huge amounts of structured and unstructured data in order to detect patterns and apply the information learned in real world applications. Data science uses statistics, data analysis, and machine learning to analyse data and to extract knowledge and insights from it. This allows businesses to efficiently manage costs, identify new market opportunities, and boost their market advantage. It involves mining large data sets of raw data to identify patterns and extract useful insight from them. The first stage in the data science pipeline workflow involves gathering data and entering it into a database system. After which one needs to maintain the data in an easily accessible way which includes data warehousing, data cleansing, data processing, data staging, and data architecture. During data exploration and processing data scientists stand apart from data engineers. This involves data mining, data classification and clustering, data modelling, and encapsulating the insights learned from the data. A Data Scientist requires expertise in several backgrounds such as Machine Learning(ML), statistics, programming, mathematics and databases. To work as a data scientist one needs to ask the right questions(understand the business problem), explore and collect data, extract the data, analyse data, find patterns and make future predictions. And finally we need to Represent the result with useful insights in a way the business users can understand. Data Science is used in many industries in the world today, e.g. banking, consultancy, healthcare, and manufacturing. Some examples include operating a self-driving car, foresee delays for flight/ship/train, providing useful results in search engines, forecasting for a company’s revenue, talking to a chatbot for customer service. These are all real-life applications for data science. Data Science can be applied in nearly every part of a business where data is available. In 2020, there was around 40 zettabytes of data, that is 40 trillion gigabytes! This data is expected to grow exponentially. Internet users alone generate about 2.5 quintillion bytes of data every day. This means there is a huge amount of work in data science. As such, data science tools are the key to understanding big data in a meaningful way.