Advantages of Data Science | disadvantages of Data Science
This page covers advantages and disadvantages of Data Science and its basics. It mentions benefits or advantages of Data Science and drawbacks or disadvantages of Data Science.
What is Data Science?
The data science is the technology which handles and works with big data in 21st century. We know huge amount of data is generated by internet users daily and handling the same effectively is a big challenge.
As shown in the figure, data science involves many different fields which include machine learning, statistics and data analysis.
Lots of data are generated from various sources such as sensors installed at various places, social media posts, pictures and videos captured by mobile phones, e-commerce transactions, internet surfing and so on. This data is known as "Big data". The science of dealing with this data is known as data science.
Data science utilizes multiple skills such as statistics, mathematics and specific business domain knowledge to help organizations in many ways. It helps businesses to understand consumer behaviour, fine tune its messaging and capture new market share.
Following steps are followed in data science by data scientists.
• Data gathering • Data preparation • Exploration• Model Building• Model validation • Model deployment.
Benefits or advantages of Data Science
Following are the benefits or advantages of Data Science:
➨It helps organizations to reduce costs, get into new markets, tap opportunities in different demographics, gause effectiveness of marketing campaign and launch new product or service.
➨The domain is in high demand.
➨It offers abundance of job positions.
➨It helps in securing highly paid career.
➨It helps in achieving highly prestigious positions.
➨It is versatile branch of data.
Drawbacks or disadvantages of Data Science
Following are the drawbacks or disadvantages of Data Science:
➨Mastering data scienece is difficult but not impossible.
➨Data science is a blurry term.
➨It requires large amount of domain knowledge to become data scientist.
➨There is concerns of data privacy in this domain as data is accessible by data science experts.
➨Arbitrary data may yield unexpected results.
➨It is difficult to develop competencies in the data science team.
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