Data Science In Manufacturing Industry
We know that data is everywhere and every organisation uses various data for creating application programs ,in projects , for identifying their staff etc.
Data Science is a way to process raw data to make it meaningful and get every aspect and insight of that data. It is processed by Data Scientists by various methods and algorithms.
In IT Industry and Business Industry both deal with numbers of data whether it is structured data or unstructured data(i.e without any specific format) so in such cases Data Science comes into the frame where data is analysed and gives useful information from it.
Some years ago when there was not an abundance of data it was easy to handle and manage the data but now in any company there is large amount of data to process and to handle so such a scenario is configured by “data science”. It helps to make better decisions for business goals and for development of the IT industry. This technology is widely used in startups as well as in MNCs for better performance and growth. Also it helps customers to have better experiences. It is used in self-driving cars, surveys,dealing with big datasets etc. Some of the techniques utilised in Data Science hold within machine learning, visualisation, pattern recognition,probability model, signal processing, etc.
There are some technical skills required to for so that we can learn Data Science :-
1) Machine Learning
2)Basic Maths
3)Computer Programming
4)Database
5)Statistics
There are some other skills required to understand it better:-
1)Curiosity
2)Critical Thinking
Following are some tools required for data science:
Data Analysis tools: R, Python, Statistics, SAS, Jupyter, R Studioetc.
Data Warehousing: ETL, SQL, Hadoop, AWS Redshift etc.
Data Visualisation tools: R, Jupyter, Tableau, Cognos.
Machine learning tools: Spark, Mahout, Azure ML studio.
Life- cycle Of Data Science:-
Discovery:-In this section, it is to seek out the very fundamental entities e.g.number people in the project, what will be technology to be used etc.
Data Preparation:- Here we need to clean , reduce,transform and integrate the data.
Model PLanning:- We need to find a relationship between the input variable and the one we already have.
Model Building:-We build the model by using clustering and classification techniques.
Operationalize:-It gives a clear overview of the overall project and its evaluation.
Communicate Results:- Now finally we get our output and can be sent to authorities.
Features of Data Science:-
It is an extension of data analysis.
It involves statistical and probablistic knowledge as it predicts future results.
It is inspired by Artificial Intelligence and Machine Learning.
Application of Data Science:-
1)Image Recognition:-Our device recognizes our face and unlock itself as in smart phones.
2)Speech Recognition:- Some software easily identify our voice and executes the command given by us e.g. Alexa, google assistant etc.
3)Gaming World:-It is enhancing and enriching user experiences.
4)Healthcare:-It helps a lot in illness e.g tumour detection etc.
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