Hi, Siri… What’s the weather in New York? Alexa… Can you please tell me today’s date? How does this virtual assistant know what I am asking for? Well, this is one of the applications of data science that works on the speech recognition concept.
Moreover, it is clear that demand for data science is increasing from the last 5 years in the world. That is why the applications of it are also gradually increasing.
Below I have explained all the necessary data science applications. This will help you understand how the application of data science is applied in this technical era.
What is data science?
You might be thinking that why am I explaining what data science is? The straightforward answer to this is that several people do not know what data science exactly is.
Data science is nothing but a method to glean or collect useful insights from unstructured and structured data. To do this, the approach ranges from statistical analysis to ML (machine learning).
It has been noticed that several organizations employed data science for transferring data into various values. These values can be as:
- business agility,
- new product’s development,
- improved revenue,
- make better decisions,
- improved customer experience,
- cost reduction, and so on.
Therefore, we can say that data science provides insights and purposes to the collected data by the organization from various resources.
|What is the main purpose of data science?|
The main purpose of data science is to create the means to extract business-related insights from the raw data. It needs understanding how information and value carry out in the business. Moreover, it requires knowing how to identify business opportunities.
The data science team always tries to determine the key data assets, which turn into data pipelines. To perform it, various maintainable tools and creative and realistic solutions are required.
Some of the known applications of data science involve tools for optimizing the wind turbines’ placement in wind farms or card fraud monitoring solutions that banks are using.
What are the applications of data science?
Data science created a major mark in 2008 on the health industry. Google members were successful in mapping the flu outbreaks in real-time.
Moreover, Google quickly rolled a tool with frequent updates on Google Flu Trends. But unfortunately, after some time, it was not enabled to perform. This defines that health care is one of the most potential applications of data science.
Now, let’s take an example of it:
ONCORA MEDICAL: Best for cancer care recommendations
Place: Philadelphia, Pennsylvania
How this uses data science: Machine learning (ML) is being used for Oncora software. It uses to develop recommendations for cancer patients by analyzing the history of past cancer patients.
A New York company’s radiologists collaborate with Oncora data scientists. Both have worked really well as the algorithms of Oncora learn to recommend radiation and chemotherapy treatments.
In the early days, people shopped from the physics places. But nowadays, each individual can shop from the personalized digital malls.
Several retailers have tailored the web storefront as per the data profile of their viewers. Moreover, some stores have adjusted their price as per the customer’s budget. All this is performed by data science.
Let’s take an example to understand it.
SOVRN: Automated Ad Placement
Place: Boulder, Colorado
How this uses data science: Sovrn deals with the outlets like ESPN, Bustle and the advertisers. As deals carry out millions of times in a single day, Sovrn has worked with large data to make useful insights.
Moreover, these insights are compatible with Amazon and Google. That is why its interface helps in monetizing the media along with the human oversights. Because of this, it becomes quite easy to deal with the advertiser.
In the 2000s, the budget of Oakland Athletics was really small even though they could not recruit quality players. To get quality players, the general managers redefined the quality and used the game statistics that other teams ignored.
This strategy helped various managers to make the playoffs. This is how data science helped the managers to get quality players.
Let’s take an example to understand it.
BRITISH OLYMPIC ROWING TEAM
Place: London, England
How this uses data science: Before the Rio Olympics, the British rowing team has ramped the data gathering on the players. Using the rowing and longitudinal weight-lifting data, they began with the model athlete evolution. Apart from this, it helped the coach to identify potential rowers.
What are some of the other applications of data science?
|Email Spam Filtering: |
To filter out spam emails, the machine learning algorithms detect the patterns of words and fake emails. These words are used for promoting the business products’ advertisements and offering top discount offers.
It is one of the applications of data science that predicts the remaining search word typed by the user. The predictive search works on the AI. Moreover, these work using the concept of machine learning, natural language processing, and deep learning.
It basically includes 4 steps. (a) identifying the misspelled word (b) find the string to compute minimum edit distance (c) filtering the most suitable word that matches with the misspelled word (d) calculate word probability to give the best prediction.
|Virtual Assistant: |
It is also known as a digital assistant or AI assistant. This works on the natural language voice commands. After speech recognition, it interprets the user’s commands and performs the most practical and relevant tasks.
|Image Recognition: |
In the image recognition process, you can use Python’s Pytesseract module that reads text data given in an image and converts it into a data string. This can be further displayed in Python.
|Fraud and Risk Detection: |
The finance department uses data science concepts for fraud and risk detections. It uses artificial neural network systems. With its help, it is also possible to detect claims or charges outside the norms and much more.
With the help of data science, robotics can exclusively use for achieving human-level performance over multiple pre-programmed tasks.
Why Data science is the future?
It is pretty helpful to notice that the data scientist’s role considers being a buzzworthy career. Moreover, data science is providing power to the marketplace and various job opportunities to the individuals who are studying data science.
Those people who are pursuing their studies in the field of data science are making a valuable contribution to societies and companies on a large scale.
These are the most common reasons why data science is taken to be the future of companies. Moreover, the applications of data science are rapidly increasing day by day. That is why data science can be the future in the upcoming years.
It is quite clear that there are tons of applications of data science. Moreover, various industries like transport, healthcare, e-commerce, and others use data science concepts.
Moreover, data science is a wide field that is why its applications are also diverse and enormous. If an industry wants to boost its production, data science can help them. Therefore, data science considers being an important aspect for all organizations.
Hope you like the blog. If you like it and want to get similar application topics on various technologies, comment in the comment section. I will be happy to provide you a detailed blog about what you want to know. If you need any help in data science then you can have the best data science assignment from us.
Frequently Asked Questions
The several advantages of Data Science are:
Data Science is significantly in high demand.
A highly paid career.
Data Science helps in making data insights better.
It is versatile.
Data helps you to make products smarter.
It eliminates boring tasks and offers exciting things to do
It has been seen that the data science team consists of diverse background people. These fields can be physics, statistics, operational research, chemical engineering, economics, mathematics, computer science, and so on. Moreover, you can see that various data scientists hold bachelor’s degrees in ML (machine learning) and statistics.
There are four components of Data Science, which include:
Data Visualization & Operationalization.
Data Analysis & Models.