Data Science: Tools, Statistics, and Enterprise Cases

Data science offers world-changing solutions for startups across the globe. Thanks to predictive analytics and machine learning tools, businesses can develop brand-new products, provide immaculate customer service, and make smart choices. How does it work? Let us tell you.

What Is Data Science?

This is a branch of computer science related to collecting, processing, and analysing data, as well as a search for effective solutions. It helps businesses analyze huge arrays of information, make use of it, and streamline business processes.

Data science uses machine learning tools (ML) and Big Data – a complex of technologies for data collection, storage, processing, and analysis. We can say that data science is the intersection of mathematics, programming, analytics, and even linguistics.

To better understand what data science is, let`s take an example. The online store has millions of users who make purchases daily, paying for them with a smartphone or by card. This purchase data can be collected and analyzed. The results of the analysis can enhance the company's work, increase its revenue, and improve customer experience. Based on the data, they can provide special offers, better solutions, and maintain proper support service.

Data science has emerged recently. The term first appeared in the scientific community in 2001, and spread in 2008 thanks to leading data specialists at Facebook and LinkedIn. By that time, the IT giants had attracted hundreds of millions of users to their products and realized that big data about their behavior could benefit business and the Internet industry as a whole.

How Does Data Science Work?

The data science work principles can be depicted as a cycle of five stages:

Collecting

Each time you need to determine the volume and structure of the data and collect them in a convenient form for processing, it`s necessary to set the purposes of data analysis and select the required methods for the following stages.

Preparation

It includes the creation of a database architecture and storage system, checking, and validating.

Processing

At this stage, the system clusters and classifies data arrays. Here you also need mathematical models and software tools such as machine learning.

Analysis

The data scientists study the simulation results and search for answers to the initial hypotheses, conduct qualitative and quantitative analysis. For example, they can identify errors in business processes or predict the behavior of data in the future.

Communication

It means data visualization, creation of analytical reports and suggestions: what data-based decisions can be made to make the business work more efficiently.

What Tasks Can Data Science Solve?

Demand forecasting

Companies have large amounts of data on sales of their goods and services over the past years. By analyzing this data using Machine Learning, brands can identify patterns, forecast future demand, and rebuild business processes.

Recommendation system

Internet services have data on each user's views of their content, from videos, movies, and music to articles and pages of goods and services. Machine learning can analyze preferences to offer the most relevant content to them.

Dynamic pricing

Online commerce and booking systems have data on sales of various goods and services to different categories of buyers. Data science enables companies to determine the best prices for their products and services that will help increase revenue.

Chatbots

Machine-learning-powered chatbots can answer customer questions faster and more accurately. This helps businesses solve most of their problems and reduce the load on the call center.

Statistics

The data science market has made an astonishing leap in the recent years. In 2019, it was valued at only $4 billion but by the end of 2020 soared by 7,5 times. In 2020, the market size reached roughly $95 billion having tripled its valuation.

By 2026, the data science industry is expected to exceed $300 billion at a Compound Annual Growth Rate (CAGR) of 28%. According to different sources, the estimate may vary from $270 billion to $320 billion.

The key market driver is the increasing popularity of big data and growth of cloud-based solutions across the globe. Some worldwide largest financial organizations have already integrated data science tools to optimize the operational costs and reach better customer experience. Thanks to its deep insights and advanced analytics, businesses can receive valuable information about their customers and build efficient long-term strategies.

Enterprise Use Cases of Data Science

Data science solutions can provide brands with valuable customer insights and significantly increase their income. Let`s see the way businesses from different areas can take advantage of data science.

E-commerce

With the help of data science, online retailers can provide customers with relevant recommendations and special offers. The system analyzes users` behavior, identify their needs and wants, as well as forecast their actions. This enables e-commerce companies to create a personalized experience and develop efficient marketing strategies.

Most e-commerce sites, such as Walmart, Amazon, eBay, Target, have their own teams of data specialists who calculate the type of product, weight, functions, and other factors to set up the appropriate recommendation algorithm.

On top of that, data science tools help online platforms enhance customer retention based on their preferences, habits, and expectations. They get answers to critical questions like who their customers are, where they live, what kind of products they need, and how the company can better serve them.

Healthcare

Today, data scientists use a wide range of algorithms to treat oncological diseases. Doctors can make diagnostics based on tumor images via neural networks and prescribe correct treatment.

There are also special algorithms to analyze DNA data obtained from single cells. In addition, data science solutions can help medical organizations model organs, analyze the gene sequence, and develop next-generation medicals.

The synergy of data science and medical technologies has greatly boosted the development of solutions for the diagnosis of oncological, autoimmune, and neurodegenerative diseases. Services based on data analysis and machine learning are able to predict the spread of viruses and search for new kinds of treatment.

Finance

Data science can be applied for various financial activities, from managing user data to creating trading strategies. New models of machine learning enable fintech organizations calculate and minimize financial risks. Such companies have to store and process a huge amount of data which is difficult to handle manually. Therefore, data science tools are committed to facilitating the data management and building models for sustainable development.

Another function financial organizations can benefit from is credit scoring. Machine learning algorithms are able to analyze dozens of factors that determine the solvency of borrowers, including modeling past expenses. This approach is applicable when working with both regular and new customers.

Transport

Data science solutions for transport and logistics are based on mathematical modeling and machine learning methods and are aimed at 3 main business strategies: increasing sales, reducing costs, and managing risks.

By using predictive analytics, transport companies can timely forecast malfunctions of vehicles and avoid delayed delivery of goods, as well as undesirable costs for repair of broken equipment.

Besides, logistic businesses can manage warehouses through data science technologies. They help companies increase warehouse turnover and enhance the service quality. Modern technologies and accumulated knowledge in the field of predictive analytics make it possible to process a full range of unique corporate data more efficiently and make forecasts considering the individual needs of the company.