Data science is a multidisciplinary field that uses scientific techniques and computational algorithms to collect valuable insights and knowledge from structured and unstructured data.
It involves mathematics, statistics, statistical modeling, computer science, database technologies, programming, predictive analytics, signal processing, artificial intelligence, machine learning, neural networks, signal processing, and many more advanced processes.
Data science has become one of the most rapidly emerging fields of the 21st century. Its application areas are very broad and comprehensive.
Today, more than 1000 organizations and private facilities work individually and collaboratively to address some of the most challenging problems in society. The benefits of their research are immeasurable.
Let’s dig deeper and find out some of the most common applications of data science.
12. Airline Operation Management
Evaluate passenger demand across different routes and increase profit per seat
Companies like EasyJet and Southwest Airlines have turned operational challenges into successful data science use cases.
The ultimate benefits of incorporating data science into the airline industry include accurate responses to current and future market demands, improved planning of routes, better revenue management, and implementing profitable marketing strategies such as customer loyalty programs.
With data science, aviation companies can improve their pricing strategy and manage inventory. Many have successfully increased profits per seat by more than 20 percent. Some carriers also analyze billions of searches on their website every year to determine optimal routes and flight times.
11. Intent Analysis
Allows businesses to be more customer-centric
You might be familiar with the term ‘sentiment analysis.’ It’s a method of analyzing a message and deciding whether the underlying sentiment is negative, positive, or neutral. Intent analysis steps up the process by analyzing the user’s intention behind a message and determining whether it relates to a complaint, suggestion, query, opinion, or news.
Intent analysis systems combine machine learning with various analytics functions, ranging from low-level tokenization and syntax analysis to high-level sentiment analysis.
Consider the example of social posts that show different intentions for a smartphone.
- “Does it have an OLED screen?” — a query
- “It could have used 5000mAh battery instead of 4200mAh” — a suggestion
- “The camera quality is not good” — a feedback
Data science can identify the pattern of the intentions. It allows businesses to be more customer-centric, especially in areas like sales and customer support. From taking feedback to dealing with a large number of queries and offering a personalized service, intent analysis can be a key tool. It can also be used to detect spam such as invalid emails, messages, and phone calls.
10. Detecting Financial Fraud
Spot inconsistencies in transactions
Frauds that involve credit card transactions, income tax return claims, insurance claims, etc., are major concerns for businesses and governments. There is no specific software or algorithm that works for all kinds of frauds in all industries. The characteristics of the problem vary in every situation.
Thus, every data science tool is designed differently to detect inconsistencies within the domain of each industry. Some of these tools treat fraud detection as a supervised classification problem, and some have their own way of addressing the problem, such as cluster analysis, time series analysis, breakpoint analysis, real-time monitoring of transactions, etc.
Different methods for detecting different types of fraud:
- Neural nets are used to detect financial statement fraud.
- Bayesian learning neural networks can efficiently detect medical insurance fraud, telecommunications fraud, and fraudulent transactions done from credit cards.
- The link analysis technique utilizes record linkage and social network methods to find relationships among known fraudsters to other individuals.
- Unsupervised machine learning algorithms are used to identify novel types of fraud.
9. Real-Time Route Optimization
Minimize distance and travel costs
Using the power of data science and applied engineering, we can accurately forecast travel times between two locations.
Let’s say a delivery company has 1,000 sales routes, 50 stores, and a strong customer base of 50,000. The aim is to deliver packages to all customers as fast as possible while covering less distance. This is an NP-hard problem.
The company can use a three-dimensional approach and sophisticated route mapping algorithms to resolve the challenge with great precision. These data science algorithms map locations in proximity and create subsets for delivery points that are closer to each other.
Most companies use branch-and-bound or dynamic programming and genetic algorithms to obtain state-of-the-art solutions. It helps them save significant operational expenses by reducing the number of delivery vehicles without delaying packages.
8. Crime Analysis
Crime map and analysis of crime in Spain
Solve crime cases faster and predict future criminal activities at specific locations
Crime analytics can be viewed as a branch of analytics that involves using statistical tools and techniques to examine various data in order to resolve the crime at faster rates and predict crimes that might happen in the future based on past events.
This includes analyzing internal police operations, crime victims, disorder, and quality of life issues. The insights (extracted with data science) can be used for patrol activities, crime prevention, criminal investigation and prosecution, and the evaluation of police efforts.
Modern tools provide a framework for visualizing the crime networks and examining them by different machine learning techniques using Google Maps and various R packages.
7. Target Advertising
Display ads to the right audience to decrease customer acquisition costs
Good advertising has always been one of the main reasons behind the company’s success. But it’s not just about promoting the product with a catchy phrase; it’s also about delivering the message to the right people at the right time and in the right context.
Data science has become critical for advertisers and marketers, who need to analyze thousands of signals in real-time and deliver ads to the right audience at the right moments. Machine learning is also essential to analyze the past behavior of the user (site visits, searches, purchases).
The more data you have, the better targeting result you will achieve. Following are the use cases of target advertising.
- Visual merchandising: is a marketing practice in the retail industry that involves optimizing the presentation of products and services. It involves lighting, color combinations, creative visual displays, and other elements to attract customer attention.
- Programmatic advertising: is defined as the automated buying and selling of online advertising space. It allows brands or agencies to purchase ad impressions on publisher websites or applications within milliseconds through a sophisticated ecosystem.
- Smart bidding: is a subset of automated bid strategies that use machine learning to optimize ads for a higher conversion value every time the bid process occurs.
6. Advanced Image Recognition
Recognize patterns and distinguish between multiple image sets
Modern data science software can accurately recognize human faces and match them against all the pictures available in its database. It is smart enough to recognize any special patterns, be it facial expressions or texture. Some programs are designed to collect data from complex diagrams and/or recognize handwritten text.
In addition to facial recognition, data science tools can utilize machine learning methods to detect objects captured in a camera frame. They can detect shapes, colors, and even measure the dimensions of all objects in real-time, providing users with detailed insights into the content of the image.
Both image recognition and object detection are used in various fields, ranging from smart photo libraries and targeted advertising to accessibility for the visually impaired and enhanced research capabilities. Tech giants, such as Microsoft and Google, are heavily investing in image recognition research and related applications.
5. Game Development
Improve players’ experience, engagement strategy, and revenue
There are two major elements that make a game successful: storyline and graphics. They keep players engaged and interested in playing.
The data collected in a game can be used in many different ways. For example, many companies use gaming analytics to obtain specific knowledge of what players want, how much time they spent on each stage, and which part they enjoyed the most.
Data science is utilized to create models, empower machine learning algorithms, and identify optimization points and trends to improve the gaming experience. It enables developers to come up with new game concepts, storylines, and build interactive scenarios using the data gained previously.
Image credit: intellipaat
Facilitates preventive maintenance and fault prediction
The way data science is used in manufacturing is unique in certain ways. This is because there are many different types of manufacturing units, and each has different requirements.
Data science is primarily used to extract valuable information from manufacturing processes. This information can help businesses maximize profits, minimize risks, and analyze productivity.
For example, Raytheon Technologies Corporation uses a software solution called Manufacturing Execution Systems that gathers and evaluates factory-floor data. By analyzing their data, the company found that a screw in one of the modules must be turned 13 times. If it turned only 10 or 12 times, the system flashes an error and halts the installation.
When analyzed properly, the information can be used to
- Estimate machine failure rates
- Identify energy-inefficient components
- Streamline inventory management
- Optimize factory floor space
Companies like GM and Ford evaluate massive amounts of data –including all internal and external sources, from sensors and processors to material quality and performance– to improve production times, minimize energy costs, and maximize profit.
3. Genomics Research
Helps us better understand human health and disease
Over the past decade, biomedical research projects and large-scale collaboration has grown rapidly. As a result, massive amounts of genomic data (2,000 to 40,000 petabytes) have been generated every year.
Data science allows bioinformaticians and geneticists to extract practical insights from such huge and complicated datasets so they can understand how differences in DNA affect human health and disease.
They use data science tools, such as aligners, to analyze the location of individual components of DNA sequence. The software program identifies the locations where a specific human genome sequence differs from other human genome sequences.
These genomic differences may vary. It may be as small as a single DNA letter or as large as chromosomal abnormalities. By analyzing such differences, researchers can figure out what exactly causes common diseases, cancers, and rare disorders.
Improve students’ performance and teaching methods
Data science has the capability to revolutionize the education sector. It can help teachers employ adaptive learning techniques that aim to provide effective and customized learning paths to engage each student.
Several machine learning algorithms, such as decision trees, logistic regression, and random forest, are already being used for this purpose.
Data science also allows administrators to analyze the activities and teaching methods of teachers. It provides valuable information that shows the strengths and weaknesses of faculties. This could help teachers improve accordingly and identify the most effective teaching methodologies.
The University of Nevada has adapted data science methods for analyzing student data and predicting their performance. Another example is the University of Florida, which uses various techniques to identify patterns and trends to provide a customized student experience.
1. Drug Discovery and Development
Data science increases the efficiency of the entire R&D process
The combination of advanced analytics and computing power is making data science a critical core discipline in pharmaceutical research.
The integration of artificial intelligence and machine learning techniques into drug discovery has significantly reduced the time and increased the efficiency of the entire R&D process.
Advanced tools, such as the DeepPurpose toolkit, have been used to unlock more than 50 models for Drug-Target Interaction (DTI) prediction, a basic task in drug discovery. DeepPurpose also facilitates a simple interface for virtual screening and drug repurposing.
Data science solutions developed by Cognizant have helped several pharmaceutical companies improve the laborious process for cross-referencing research clinical trials on cancer drugs.
Frequently Asked Questions
What is the difference between data analytics and data science?
While data analytics focuses on viewing the historical records in context, data science focuses on creating predictive models that can predict or analyze whatever comes next.
For example, a data analyst may synthesize big data to answer questions like “which product(s) generated the most profit in the last fall?” The data scientist, on the other hand, may use machine learning methods to analyze feedback and customers’ behavior and predict which products and services are going to perform better this year.
How much do data scientists get paid?
According to the US Bureau of Labor Statistics, the average salary of data scientists is $111,000 per year. Experienced data scientists (manager-level professionals) make up to $250,000 per year.
California, Texas, New York, Illinois, and Washington are the states with the highest employment level in data scientists and mathematical science occupations.
What is the future of data science platforms?
The adoption of data science platforms is increasing significantly. It provides flexibility to open source programs and scalability of computer resources. Plus, it can be easily aligned with numerous data architecture.
According to the Grand View Research report, the global data science platform market size will reach $26 billion by 2027, growing at a CAGR of 26.9%. Advances in artificial intelligence and neural network will be the key factor behind this phenomenal growth.