Know-How of Data Science

From browsing online to buying online our data is continuously generated and has been used by different companies to cater to our needs and to make our life a little easier. But how is this possible? How is our data continuously provided to companies and are needs being understood by them? Well it's through data science.


So what is data science? Well according to Wikipedia, “Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data.” In simple words, data science belongs to the field of study that helps us study the data. It consists of extracting, recording, storing ,and analysing the data to put it into proper use. The objective of this field is to gain an understanding, Insight ,and knowledge from data whether it is structured or unstructured. Data science is practised by Data Analysts, Data Engineers, and Data Scientists. For understanding data science, one requires to have the knowledge of various programming languages like python, R, TensorFlow, Pytorch and many more.



Our future will consist of use of immense technology including artificial intelligence and the future of Artificial Intelligence depends on data science. Therefore, it is very important to understand what is Data Science and how it can add value to your business. Big data is a very important tool for businesses to grow and to add value to their business. The availability and interpretation of big data has altered the business models of old industries and enabled the creation of new ones. Data science is the field which breaks down big data into useful information and thereby creating softwares and algorithms which help businesses to increase their market share and profit margins.


Now there are various techniques and technologies which are used to interpret data in data science. According to wikipedia, some of them are:


Techniques-

  • Clustering is a technique used to group data together.

  • Dimensionality reduction is used to reduce the complexity of data computation so that it can be performed more quickly.

  • Machine learning is a technique used to perform tasks by inferring patterns from data.

Technologies-

  • Python is a programming language with simple syntax that is commonly used for data science.There are a number of python libraries that are used in data science including numpy, pandas, and scipy.

  • R is a programming language that was designed for statisticians and data mining and is optimized for computation.

  • TensorFlow is a framework for creating machine learning models developed by Google.

  • Pytorch is another framework for machine learning developed by Facebook.

  • Jupyter Notebook is an interactive web interface for Python that allows faster experimentation.

  • Tableau makes a variety of software that is used for data visualization. Apache Hadoop is a software framework that is used to process data over large distributed systems.


What knowledge is useful if you can’t implement it right? So the careers in data science include machine learning scientist, data analyst, data consultant, data architect,and application architect.


So this was some of the useful information on data science and how it has become the need of an hour.


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