Data Science

Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.

Data Science is the field of applying advanced analytics techniques and scientific principles to extract important information from data for business decision-making, strategic planning, and other uses.

Data Science is an integrative mix of data inference, algorithm development, and technology to solve analytically complex problems.

Components Of Data Science

Data Science consists of 3 parts namely:

1. Machine Learning: Machine Learning involves algorithms and mathematical models, chiefly employed to make machines learn and prepare them to adapt to everyday advancements. For example, these days, time series forecasting is very much in use in trading and financial systems. In this, based on historical data patterns, the machine can predict the outcomes for the future months or years. This is an application of machine learning.
2. Big Data: Everyday, humans are producing so much of data in the form of clicks, orders, videos, images, comments, articles, RSS Feeds etc. These data are generally unstructured and is often called as Big Data. Big Data tools and techniques mainly help in converting this unstructured data into a structured form. For example, suppose someone wants to track the prices of different products on e-commerce sites. He/she can access the data of the same products from different websites using Web APIs and RSS Feeds. Then convert them into structured form.
3. Business Intelligence: Each business has and produces too much data every day. This data when analysed carefully and then presented in visual reports involving graphs, can bring good decision making to life. This can help the management in taking the best decision after carefully delving into patterns and details the reports bring to life.