Data Science Algorithms Explained On Real Life Examples

Data Science Algorithms Explained On Real Life Examples


Without data science and next-generation technology transforming data into actionable insights, basic data remains meaningless. Thus, data science is a game changer for organizations looking to unlock the potential of their data and increase the value of their insights. Whether we notice it or not, data science is present in our day-to-day lives, especially with the intensive use of marketing, logistics, and healthcare applications to name a few. 

It’s a universal truth that well-managed data rules the modern world. Sources estimate that bad data costs the US up to $3.1 trillion a year, making data science all the more important. Every day, it becomes clearer that processing and analyzing data brings a lot of value, and that's where data science algorithms comes in. Executives have heard that data science is a sexy field and that data scientists are like modern-day superheroes, but most still don't know how valuable data science is to their organizations. Let's take a look at what data science can do for us.

Data science is important because it uses tools, methods, and technology to find meaning in data. Modern organizations have a lot of data because there are so many devices that automatically collect and store information. Information is collected by online systems and payment portals in e-commerce, medicine, finance, and every other part of life. We have text, audio, video, and image data available in vast quantities. So what will we do with it?

In this article, we talk about the algorithmic importance of data science with real-life examples showcasing its value in our modern world.

Let’s get going!

What is Data Science?

When you hear the word "science”, you might think of a field that uses systematic steps to get results that can be tested and you would be correct! Data science is rapidly becoming one of the most sought-after fields for qualified individuals. 

Successful data professionals of today know they need more than just the ability to analyze massive datasets, mine databases for value, and write data-related code. If they are to unearth actionable intelligence for their organizations, Successful Data Scientists must be well-versed in all aspects of the data science life cycle and possess the adaptability and depth of knowledge necessary to maximize investment returns at every stage of the process.

Data Science Algorithms Explained On Real Life Examples

But, what is data science exactly? Data science is the study of data to obtain business-relevant insights. It is a method for analyzing massive volumes of data that integrates principles and practices from the domains of mathematics, statistics, artificial intelligence, and computer engineering. This analysis assists data scientists in posing and answering questions such as ”What occurred?”, ”Why did it occur?”, ”What will occur”, and ”What can be done with the results?”.

The term "data science" has been around for a long time, but its meaning and associations have changed over time. In the 1960s, the word was used as a different name for statistics. In the late 1990s, people who worked in computer science made the term official. A proposed definition for data science said that it was “...a separate field with three parts: designing data, gathering data, and analyzing data”. Even after its first 10 years, the term still wasn't used outside of academia.

In 2008, when companies realized they needed people who were good at organizing and analyzing huge amounts of data, the term "data scientist" was created. Effective data scientists are able to ask the right questions, collect data from a variety of sources, organize the data, turn the results into solutions, and explain their findings in ways that help business leaders make good decisions. Almost every industry needs these skills, which makes skilled data scientists more and more valuable.

Why are algorithms in data science and mathematical methods so important?

In the field of data science, the discipline of mathematics is very important because mathematical ideas help find patterns and make algorithms. For algorithms in data science to work, you need to know a lot about statistics and probability theory (or a data scientist). Data science requires you to:

  • Come up with ideas
  • Try things out to get information
  • Assess data quality
  • Sort out and clean up data sets
  • Set up and organize data so it can be analyzed.

As such, data scientists often use programming languages like SQL and R to write algorithms that help them collect and analyze large amounts of data. When they are well-written and well-tested, algorithms can find information or trends that people miss. They can also make the process of gathering and analyzing data much faster.

Mathematics and algorithms in data science are the pillars of data science used to change how businesses work in many ways. No matter how big or small a business is, its leaders need a strong data science algorithms strategy to drive growth and stay ahead of their competition. 

Below we name some of the main benefits of data science with mathematics and algorithms.

Find out about unknown patterns of change

Data science helps businesses find new patterns and relationships that could change the way they do business. It can show how to make changes to resource management that don't cost much but have a big effect on profit margins. 

Create new products and ways to solve problems

Data science can find problems and gaps that wouldn't be seen otherwise. More information about purchase decisions, customer feedback, and business processes can lead to new ways of doing things both inside and outside the company. Some businesses are even based on making services by using data science to collect, aggregate, cleanse, organize, and sell enriched data to other businesses.

Real-time optimization

It's hard for businesses, especially big ones, to respond to changes in the real world right away. This can cause businesses to lose a lot of money or stop working. Data science can help businesses predict change and act in the best ways possible in different situations.

Mathematical methods for data science

The development of Artificial Intelligence (AI) and Machine Learning (ML) have made it easier and faster to process data. In the field of data science, classes, degrees, and jobs have grown up in response to demand from the business world. In the next few decades, demand for data science is likely to continue growing quickly, because it takes skills and knowledge from many different fields.

Linear Algebra

A key part of making algorithms in data science for machine learning is knowing how to build linear equations. These are used to look at and observe data sets. Algorithms for linear regression leverage algebra in loss functions, regularization, covariance matrices, and to support vector machine classification, all of which are parts of Machine Learning.

AI and Machine Learning


Linear algebra can solve any kind of problem more quickly because it works faster. This is due to its characteristics (details to here) that optimize data throughput. It helps to know the ideas and history behind the different kinds of algorithms for data science used in today's technologies. It helps us make decisions and mostly works with matrices and vectors. It can be accessed using the Numpy Library in the Python programming language.

Numerical Methods: Regression

Regression is a way to find a link between two data points that don't seem to have anything in common with each other. Most of the time, the connection is shown as a graph or curves based on a mathematical formula. Regression is used to guess the value of the other data point when the value of the first data point is known. For example:·

  • The rate at which diseases spread through the air. 
  • The link between customer satisfaction and the number of workers.
  • The link between the number of police stations and crimes in the same area.

Numerical Methods: Classification

Sorting information into specific groups or categories is called "classification." Computers are taught how to find and sort information. Known data sets are used to build decision algorithms in a computer to quickly process and sort the data. For example:

  • Sort items by whether they are popular or not.
  • Sort insurance applications according to how risky they are.
  • Sort social media comments into positive, negative, or neutral.

The data science process is followed by people who work in data science.

Probability theory

This is important for testing hypotheses and figuring out things like the Gaussian distribution and the probability density function. 


This is important when working with classifications like logistic regression, discrimination analysis, and hypothesis testing, and distributions in machine learning.

Algorithms in Data Science Applied in Real-Life Applications


Data science applications are especially helpful to the healthcare industry. 

  • Medical image analysis: Detecting malignancies, artery stenosis, and organ delineation are some of the methods and frameworks used to find the best parameters for tasks like categorizing lung texture. It uses machine learning techniques like support vector machines (SVM), content-based medical picture indexing, and wavelet analysis to classify the texture of solids.
  • Genetics and Genomics: Through research on genetics and genomics, Data Science applications make it possible to improve customized therapy. The goal is to find specific biological links between genes, diseases, and how people respond to medications. This area promises to help us understand how DNA affects our health.
  • Drug Development: From the first screening of medicinal compounds to predicting the success rate based on biological variables, data science applications and machine learning algorithms simplify and speed up this process while giving each step a new perspective. Instead of "lab tests," these algorithms use a lot of math modeling and simulations to predict how the chemical will act in the body. The goal of computational drug discovery is to build computer model simulations in the shape of a physiologically accurate network, much like a digital twin. This makes it easier to predict what will happen in the future with high accuracy.
  • Health bots and virtual assistants: AI-powered smartphone apps, which are often chatbots, could help with basic healthcare needs. Just describe your symptoms, ask questions, or use a wearable monitoring device like a smartwatch or smart shirt, and you'll get important information about your health condition from a huge network of symptoms and effects. 
  • Apps: The latest AI-driven web and mobile applications remind you to take your medicine on time and, if needed, help you make an appointment with your doctor.

For example, an algorithm made by researchers at the Massachusetts Institute of Technology can find differences between 3D medical images, like MRI scans, more than a thousand times faster than a person can. Because of this time savings, doctors can deal with urgent problems found in the scans and may be able to save the lives of their patients.


Natural language processing (NLP) and recommendation systems are two examples of data science and machine learning ideas that help e-commerce. E-commerce platforms use these kinds of methods to look at what people buy and what they say about it in order to learn useful information for growing their businesses. They look at texts and online surveys with natural language processing used in collaborative and content-based filtering to help people evaluate data and get better services. The e-commerce industry has been influenced by data science in many ways, such as figuring out who the best customers are, predicting needed goods and services, figuring out the style of popular items, figuring out how to set the best prices, and many more.

For example, a company that sells online might use data science to find out that too many customer questions are being asked after business hours. Research shows that customers are more likely to buy if they get a quick answer instead of one the next business day. By offering customer service around the clock, the business makes 30% more money. Moreover, that same business might sell its enriched customer information to other businesses (though probably not to their competitors).

Targeted Advertising

Statistics can be used to test how well marketing campaigns work, such as through hypothesis testing. It's also used to figure out why people buy a certain brand, for example, through techniques like causal effect analysis or survey design, as well as to make personalized recommendations through predictive modeling or clustering.

Customer Insights

There might be a wealth of useful information about your customers' habits, demographics, hobbies, aspirations, and more in the data you collect about them. Understanding the fundamentals of data science may help make sense of the abundance of consumer data.

When a consumer visits your website or brick-and-mortar store, browses your inventory, adds something to their cart, pays for an item, opens an email, or responds to a social media post, you gain valuable information about that customer. A process known as "data wrangling" is required after you have verified the accuracy of the data from each source before integrating it.

One example is connecting a customer's email address with their other identifiers like their credit card number, social media handles, and transaction IDs. Using the combined data, you can draw conclusions and identify patterns in their actions. Knowing your target market and their motivations can help you create a product that meets their needs and drives successful marketing campaigns.

Transportation and Logistics

The most important change or breakthrough that data science has brought to transportation is the development of cars that can drive themselves. Data science has gotten a foothold in transportation by looking at trends in how much fuel is used, how drivers act, and how vehicles are tracked.

Other applications include those by popular ride-sharing services like Uber that use data science to improve their prices, delivery routes, and uses of their resources. They do this by combining things like customer profiles, geography, economic indicators, and logistics providers.

Another example of data science in transportation is a truck-based shipping company that uses data science to cut down on the time trucks are out of service when they break down. They can figure out which routes and shift patterns cause trucks to break down more often and make changes to truck schedules and routes. They can also make and distribute a stock of common spare parts that need to be replaced most often so that their trucks can be fixed correctly and more quickly.

Banking and Finance

Your company's security and the safety of sensitive information could be greatly strengthened with the help of data science. Institutions like banks use complex machine-learning algorithms to monitor customer account activity and identify any suspicious behavior.

Data is being generated at an exponential rate, allowing computer algorithms to detect fraud more quickly and effectively than humans. Such algorithms can be used to protect sensitive information even if you don't work in the financial sector.

It could benefit your company to educate employees on data privacy to prevent the misuse of customers' personal information. This includes credit card details, medical records, Social Security numbers, and other contact details which can leave digital remnants that are interesting to criminals.

Robotics and Manufacturing

Programming a robot from scratch each time it needed to perform a new function or adapt to a new real-time trend entailing vision-oriented activities was a time-consuming process. Using AI and ML, data scientists have trained robots to follow a predetermined course of development, learn novel behaviors from labeled data, adapt after gaining the ability to recognize errors in existing data, etc. Therefore, it is conceivable for robots to evolve with minimal human assistance, simplifying the work at hand for scientists. (...and no jokes about Terminators)

Production as we know it is being revolutionized by robots. Robots are commonly used nowadays, both for mundane activities and for those that humans may find too challenging or dangerous. 

Also, robots are not confined to the physical world. Digital robots (bots), can emulate the procedures of humans. Bots take on mundane computer work so the humans on the job can do what they’re best at: work with other humans. Aren’t sales and customer service good jobs for humans? 

Robot models, controlled by artificial intelligence, help manufacturers meet rising demand as they invest more resources in automating their processes. Additionally, the primary impact of industrial robots is on the enhancement of a product's quality. Each year, improved models make their way onto the factory floor and completely reshape the way things are made. And no doubt. companies now have easier access to lower-priced industrial robots than ever before.

Unlocking the Potential of Algorithms in Data Science

Innovations in artificial intelligence and machine learning have made it easier and faster to process data. Demand from the business world has led to an ecosystem of classes, degrees, and jobs in the field of data science. Data science is expected to grow quickly in the coming decades. This is because it requires skills and knowledge from many different fields.

Svitla Systems has rich experience and expertise in data science algorithms and mathematics, making it an industry-leading partner for clients looking to transform their business with actionable and effective insights from their data and from their data partners. 

If you care to learn more about how we can help your data science, mathematics, and algorithm-based projects, reach out to our representatives who will be happy to help.

by Svitla Team

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