E U N O I A

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Data is everywhere. We share and create data all the time. Just in this moment while reading this line you are creating data. With so much data around us, managing it has become a major challenge, but then came the Data Science. It is a way of testing large amounts of data to find hidden patterns, relationships, market trends, consumer preferences, and other details that can help businesses adjust
Here are some great reasons why data science is important and beneficial:

Increase Security:

data science to increase the security of your business and protect sensitive information. For example, banks use sophisticated machine learning algorithms to detect frauds based on deviations from the user's normal financial activities. These algorithms can detect fraud faster and with greater accuracy than humans, simply because of the amount of data generated on a daily basis.

Increasing sales:

Use of data science helps sales growth. Machine learning systems can analyze historical data, perform comparisons and market analysis and, as such, make recommendations on how best, when, and where your product or service will be best sold. In addition, data science can help you improve the accuracy of reaching your intended audience.

Recommendation for marketing & advertising:
For advertisers, it is very important to analyze the behavior of users on their websites. Therefore, using the science of data in advertising, companies may decide:
  • what are your customers' preferences and preferences
  • what kind of information or help they need
  • what they are interested in they want to buy
  • how much they want to pay.
Customer Travel Analysis allows you to create multiple and complete recommendation systems that on the basis of this information reflect the specific products that customers are willing to purchase. In addition, the implementation of these programs helps stores to be closer to the customer and thus run their business.
Conclusion

Data Scientists analyze data for actionable insights and build Machine Learning or Deep Learning models that train on past data to create predictive models. Data Scientists are people who answer questions such as “How many new social media followers am I likely to get next month?” or “What percentage of my customers am I likely to lose to competition in the next quarter” or “Is this financial transaction unusual for this customer?”.
Data Scientists require knowledge of Mathematics, Statistics, and a fair understanding of programming languages, databases, and building data models. They also need to have domain knowledge.