Data Science is a science that extracts valuable information for business from raw and unstructured data. It is applicable in almost every field of activity.
Collection, processing, analysis of information, and subsequent forecast based on it help to find optimal solutions to various problems. What tools are used by a Data Scientist, what is his work, and how is it in demand in business? We will tell in our article.
What is Data Science
Data Science, or “data science,” is a professional activity related to collecting, storing, and processing large amounts of data. The importance of this field of science in the modern world must be considered as more and more organizations realize the need to use big data to make business decisions. As a result, the demand for data scientists is growing, and new jobs are emerging that can provide promising career opportunities for those with programming, analytical thinking, and statistics skills.
Data Science (DS) uses scientific methods to work with data, such as mathematical statistics, logical principles, and modern visualization tools. Like scientists in other scientific fields, a Data Scientist uses data collection to measure processes around them and then applies scientific methods to analyze data and look for patterns that can help solve specific problems.
Benefits of the Data Science Concept
The study and development of data science are extremely useful for modern business because it allows you to:
- Predict the current revenue and performance of the business, and understand where the company is moving, thanks to the analysis of large amounts of data.
- Model new tactics and strategies that can be implemented based on data analysis and predictive results.
- Automate processes, reduce costs and improve business efficiency using Data Science.
- Provide customers with AI-powered solutions that improve the quality of products and services. A data scientist can develop and implement such solutions to increase the competitiveness of a business.
Basic Concepts of Data Science
There are several key terms in Data Science, such as artificial intelligence, machine learning, deep learning, big data, and data science. Although they are related, each term has its unique features.
- Artificial Intelligence is the field of developing intelligent systems that can work and act like humans. The emergence of AI is associated with the advent of Alan Turing’s machines in 1936. Despite a long development history, AI still needs to be able to replace humans in most areas completely. AI versus humans in chess and data encryption are two sides of the same coin.
- Machine Learning is the creation of tools for extracting knowledge from data. ML models are trained on data independently or in stages: supervised learning on human-prepared data and unsupervised – working with natural, noisy data.
- Deep learning is the creation of multi-layer neural networks in areas where more advanced or faster analysis is required and traditional machine learning cannot cope. “Depth” is provided by several hidden layers of neurons in the network that perform mathematical calculations.
- Big data is the work with a large amount of often unstructured data. The specifics of the sphere are tools and systems that can withstand high loads.
- Data Science is a field based on extracting meaning from data, visualizing it, gathering insights, and making decisions based on that data. Data scientists use their knowledge and skills to draw insights that help companies and organizations make important decisions.
History of Data Science
The history of data science began long before the volumes of data generated became unbelievably high. In 1966, the Committee on Data for Science and Technology (CODATA) was established to collect, evaluate, store, and retrieve critical data for scientific and technical purposes. The committee included scientists, professors, and several countries’ Academies of Sciences representatives.
Today, humanity generates a huge amount of data daily, for example, when clicking, scrolling through pages, and watching videos and photos on online services and social networks.
In the mid-1970s, Danish computer scientist Peter Naur coined the term Data Science. He defined this discipline as the study of the life cycle of digital data from inception to use in other fields of knowledge. Over time, this definition has become more flexible and broad.
In the 2010s, data volumes began to grow exponentially, thanks to the ubiquity of the mobile Internet, the popularity of social networks, and the general digitization of services and processes. This has led to the fact that the profession of a data scientist has become one of the most popular and in demand. In 2012, the position was named The Sexiest Job of the XXI Century.
The development of Data Science took place in parallel with the introduction of Big Data technologies and data analysis. Although these areas often overlap, they should be distinct from one another. All of them involve working with large amounts of information. Data analytics answers questions about the past (for example, changes in the behavior of customers of an Internet service over the past few years). At the same time, Data Science looks to the future. DS specialists create models based on big data that can predict what will happen tomorrow, including the demand for goods and services.
How Data Scientists Work
The main task of a Data Scientist is to extract useful information for business from large volumes of data, identify patterns, and create and test hypotheses through modeling and developing new software.
Such specialists use tools to achieve their goals, such as statistical modeling packages, big data technologies and NoSQL DBMS, programming languages , and business intelligence information systems.
From this, we can conclude that Data Science covers areas of knowledge such as mathematics (mathematical analysis, mathematical statistics, and mathematical logic), informatics (software development, databases, models and machine learning algorithms, Data Mining), and systems analysis (methods of subject analysis). Areas, Business Intelligence). Data Science is one of the most sought-after and highly-paid IT professions.
In recent years, there has been a rapid increase in the demand for specialists in Data Science. This profession is becoming essential for large companies, startups, and small development teams.
Every day, new tasks can be solved with the help of Data Science. Modern machine learning models allow you to solve problems that a year ago seemed unsolvable and, as a result, get more profit. The path in this profession involves the constant development and improvement of skills.
Cloud Data Science Solutions
To work effectively in this industry, you need to be able to work with cloud solutions. Due to the huge amount of data that must be processed, using local machines to work with data could be more efficient and time-consuming.
Instead, cloud clusters allow you to process and analyze data using large-scale networked computing resources. Solutions such as Amazon S3, Microsoft Azure, and Google Cloud allow companies to process large amounts of data from different sources using special software and AI models on powerful cloud computers.
Cloud solutions also greatly simplify the work of Data Scientists since they do not have to worry about software support, updating it, etc.
Differences between Data Scientists from other professions
At first glance, the work of a data scientist and a data analyst may seem similar, but they are different specialties with different competencies. Data analysis is one of the functions of a scientist, whose main result of work is to create models and code based on data analysis.
The main difference between a data scientist and a data analyst is that the former is an engineer who solves business problems as technical problems. At the same time, the latter is a business analyst who is more focused on the business components of the task. A data analyst explores needs, analyzes data, tests hypotheses, and visualizes results, while a data scientist develops tools and models that help solve business problems based on data analysis.
- Tools: a data analyst most often works with ETL warehouses and data marts, while a Data Scientist uses Big Data storage and information processing systems (Apache Hadoop, NoSQL databases, etc.) and statistical packages (R-studio, Matlab, etc.) .).
- Research Methods: A data analyst uses systems analysis and business intelligence methods more often, while a Data Scientist works with the mathematical tools of Computer Science (machine learning models and algorithms and other sections of artificial intelligence).
- Salary: In the job market, the salary of a Data Scientist is usually higher than that of a Data Analyst. This may be due to higher entry skills into the profession: Data Scientist has programming skills, while Data Analyst mainly works with ready-made SQL / ETL tools.
Pros and Cons of Working as a Data Scientist
- An interesting and new profession that allows you to solve non-standard tasks.
- The opportunity to significantly influence the company’s business processes and increase its revenue with the help of Data Science.
- High salary level sometimes exceeds the salaries of front-end and back-end developers.
- Business misunderstanding. Some business owners need to understand why Data Science and Machine Learning are needed and may assign tasks unrelated to data scientists’ competencies, such as reporting, analyzing data, or creating dashboards.
- Unrealistic expectations from the profession. For example, the expectation that a Data Scientist can replace a surgeon and train a robot to perform operations.
- Rapid obsolescence of knowledge. Specialists must constantly learn new technologies and educate themselves to remain in demand in the labor market.
Tasks of a Data Scientist
The tasks that a data scientist solves may differ depending on the company. In large corporations, they can work on several areas at the same time. For example, a scientist might work on credit-scoring tasks and develop speech recognition processes in a bank.
The stages of work on a task for specialists from different fields are similar:
- Clarification of customer requirements.
- Solving the question of the appropriateness of using machine learning methods to solve the problem.
- Data preparation and labeling.
- The choice of metrics to evaluate the effectiveness of the model.
- Develop and train a machine learning model.
- Estimation of the economic effect from the implementation of the model.
- Implementation of the model in production processes and products.
- Model support.
Each new iteration allows you to understand business problems better and refine the solution. Therefore, each step is repeated repeatedly to improve the model and update the data.
Stages of working with data in Data Science
Typically, Data Scientists have a standard workflow that consists of 5 steps:
- Information gathering is the process of collecting both structured and unstructured data from all relevant sources. Various tools are used to do this, from manual entry and scraping of web pages to extracting metrics from proprietary systems.
- Storage and validation are storing data in an appropriate format for further processing, using predefined mechanisms, removing duplicates, filtering out redundant data, etc.
- The analysis is studying the relationships between different pieces of data, identifying patterns, and checking the consistency of the information received.
- Processing and visualization – using various tools, such as artificial intelligence, machine learning models, and analytical algorithms, to process and visualize data.
- Communication is the process of presenting data in tables, graphs, lists, or any other form convenient for demonstrating information to various categories of users. The goal is to make data-based decisions, such as changing the marketing strategy or increasing the company’s budget.
Why Data Science is for Business
According to the professional social network Kaggle, using Data Science is a popular practice in companies of all sizes. Research by IDC and Hitachi confirms that 78% of enterprises have increased their data processing lately. Businesses understand that unstructured information can contain important knowledge for the company and influence business results, so they use Data Science to analyze them.
The purpose of working in Data Science is to find effective solutions to business problems. The scope of the technology covers a wide range of areas: retail, e-sports, travel, education, medicine, and many others since data permeates our entire life from birth and contains valuable information that can affect business results. Therefore, experienced specialists are needed in each of these areas.
The following are examples of areas where the application of data science can lead to significant results:
- Forecasting. For example, analyzing huge volumes of sales data can help predict customer behavior in the market in the future. Searching for patterns and general trends can lead to restructuring the business model to increase sales.
- Recommendations. Thanks to advances in data science, there are recommendation services that can consider a particular user’s preferences to offer him the most relevant content. Recommender systems are used in online cinemas and search engines.
- Price setting. The processing of price-related data makes it possible to determine the optimal remuneration for a particular specialist for his work and ensure his competitiveness in the labor market.
- Finding bugs. Data analysis allows you to detect reporting anomalies and deviations, saving companies from fines and sanctions from government agencies.
- Bots. The application of data science allows the creation of chatbots that can help users communicate with a company and reduce the burden on its employees. For example, social media chatbots allow you to minimize the time spent on phone calls and focus on more important tasks.
To make it clearer, here are some examples of how Data Scientists can be helpful:
- Predict whether a new business project will be profitable and worth launching.
- Estimate future demand for certain goods and services.
- Improve and optimize recommendation systems in social networks and other services.
- Help to create devices for automatic diagnostics of patients.
- Improve the transport system, making it safer.
- Help develop face recognition systems on the streets and indoors, and much more.
This is only a small part of the possibilities of using Data Science, and the number of different applications of this science is growing exponentially yearly.
In addition, in any area, there are the following tasks:
- anomaly detection, for example, unusual customer behavior, fraud;
- personalized marketing – emails, retargeting, recommendation systems;
- quantitative forecasts – performance indicators, the quality of advertising campaigns, and other events;
- scoring systems – processing large amounts of data, assistance in making decisions, for example, on granting a loan;
- basic interaction with the client – standard responses in chats, voice assistants, sorting letters into folders.
Industries where Data Science is in demand
The following are examples of industries that use Data Science to solve their problems:
- All business areas, including creating algorithms for forecasting demand and project results.
- Online trading and entertainment services using recommender systems for users.
- Healthcare using disease prediction and health advice.
- Logistics using planning and optimization of delivery routes.
- Transport companies use algorithms to develop the optimal transportation route.
- Digital advertising using automated content placement and targeting.
- Finance using scoring and fraud detection and prevention systems.
- Banks use programs to assess the solvency of customers.
- An industry that uses predictive analytics to plan repairs and production and predict line failures.
- Real estate uses the search and offer of the most suitable properties for buyers.
- IT-sphere using bot programming for search algorithms and artificial intelligence systems.
- Public administration, using employment and economic forecasting, as well as the fight against crime.
- A sport that uses the selection of promising players and the development of game strategies.
Examples of using Data Science in our life
Application of Data Science in banking
- Automatic assessment of the creditworthiness of borrowers.
- User authentication and fraud prevention.
- Analysis of customer income and forecasting demand for cash at ATMs.
Application of Data Science in logistics
- Optimization of delivery routes and improvement of their efficiency.
- Forecasting the profitability of transportation.
- Predicting the probability of accidents and breakdowns due to equipment wear.
- Ensuring the safety of cargo transportation and protection of closed facilities.
Application in the social sphere
One example is Google’s creation of an app for people with visual impairments. The application uses data science algorithms to recognize objects in images from street cameras and transmit information to the user. In addition, the application can recognize text, road signs, barcodes, and other visual objects, which greatly simplifies the life of people with visual impairments.
Many of us come across products and solutions that use Data Science tools daily. For example, Spotify uses them to match tracks to users according to their preferences, while Netflix uses them to offer movies and series. At Uber, data science is used for predictive analytics, demand forecasting, and improving customer experience.
Although data scientists cannot accurately predict companies’ future and consider all risks, Data Science tools help companies make more informed and informed decisions about their future.