A data scientist is involved in the processes of data extraction, pre-processing, analysis, visualizing and generation of meaningful insights. We generate tons of data by surfing sites through the search engine, online transactions, social media usage, buying products, and whatnot.
This data is used by the data scientist to understand our behavior and target the audience, according to their product and service needs. This is the simplest use case of data science application. The complexity increases with the increase in the problem. We will see some of the use cases of data science.
Applications and use cases of Data Science field
- Search Engines
Google, Bing, Yahoo, Ask, and all other search engines implement data science algorithms to give the result to the user’s query. This is the reason that we get the results we want within a fraction of seconds. Without data science, google is nothing.
- Digital marketing
Data science algorithms target the right audience for the right product or service at the right time. Try searching for an item on Amazon. Notice that when you close it and use any other app or website, it shows you the ad of the item searched or the items related to what you have searched. This is the fruit of data science.
- Image Recognition
Your faces are automatically recognized when you click photos. You might also get an option to tag your friends on Facebook when you upload pictures. You can open WhatsApp in your browser using the WhatsApp web. You can search on google by uploading images. They are all use cases of data science along with image recognition.
- Speech Recognition
Use cases of speech recognition are products like Siri, Cortana, Google voice, etc. It is very beneficial for visually impaired people.
- Comparison of websites
Websites like Trivago, Shopzilla, PriceRunner, and more use data extracted from APIs and RSS feeds to compare prices of various products.
- Airline prediction systems
Airline companies use data science to improve their business. It helps them to predict flight delays, purpose customer loyalty programs, Destination, and route selection, and airplanes to buy.
Challenges while learning data science
Data science is one of the most difficult fields to master as it requires the study of many different technologies and tools. So, developing motivation is very important. You will have to sit a lot of time with data and try to figure out how to employ it successfully. Maintaining calmness is another important aspect.
Sometimes, people are very weak in prerequisites necessary for data science. There are many vacancies available, then there is a lack of a right set of skills and mindset.
You must also know the information about the company or service or the product for which you are applying data science skills. If the objectives of the company are not clear, then the exploration of the data is a waste.
Various other challenges a data scientist might face is finding the right data, its sizing and pre-processing, maintaining the security of the data, deploying the technical aspects of data to a nontechnical audience, transforming the raw data
If you are interested in working on interesting and innovative projects and use cases, overcoming all the challenges mentioned above and have the determination to master the field of data science, then data science course is the best option for you.
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