What is Data Science?
There’s a huge amount of data in the world, and its quantity continues to grow. It can be numerical, textual, image-based, or video-based. Users generate data when they leave comments on social media, send messages to customer support, and search or purchase online.
Data science is one of the major fields in IT. Data scientists seek ways to extract insights from large volumes of data that help companies make decisions and grow their businesses. For example, a data scientist might forecast seasonal demand for a product or the likelihood of customer churn.
In the “Data Science Specialist” course, students learn to use real-world projects to create programs based on mathematical models to find patterns in data and make forecasts.
Terminology for Beginners: What You Need to Know About Data Science
For those new to data analysis, navigating the concepts, terminology, and disciplines of data science can be challenging. Let’s explore the structure of data science.
Data science specialists work with big data, or Big Data, which is terabytes of information from various sources. For example, large companies collect order and customer data to study their target audience and demand. This helps them forecast profits and decide which business areas to develop further.
Big Data is information so vast that it’s impossible to collect and process it manually. Therefore, data science specialists are looking for ways to analyze it and extract useful information.
What is needed to work in Data Science:
● Mastering technical tools:
Data science specialists need computer knowledge and skills. Currently, the most important ones are programming in Python and SQL and processing big data using Apache Spark and Hadoop MapReduce. However, tools are constantly evolving. Therefore, it is important to be prepared to learn new programs and programming languages.
● Understand Mathematics and Statistics.
Mathematical algorithms are the foundation of data science. A good place to start immersing yourself in Data Science is by studying linear algebra, mathematical analysis, probability theory, and statistics. Without this knowledge, it will be difficult to understand the algorithms of machine learning models and select the right ones for your business needs.
● Understand the subject area and business processes.
To find effective solutions to problems, you need to study the domain for which these solutions are needed. Understand how it is structured and what metrics influence the results. Often, data science specialists do not work for clients permanently; they are invited to implement a specific project. For example, to find ways to reduce costs, optimize logistics, or free employees from routine tasks. And in each new project, a data science specialist begins with research into the specifics of the company and its market.
● Possess analytical thinking.
For many professionals, such as managers or lawyers, analytical thinking is a soft skill. It helps them interact more effectively with colleagues and clients, achieve goals faster, and grow professionally. For data science specialists, analytical thinking is a mandatory professional competency. In their work, they need to ask questions, look for patterns, and predict possible outcomes. Therefore, analytical thinking is often listed as a requirement for data science job candidates.
● Be able to visualize data.
This means presenting data in graphical form, such as charts, histograms, maps, or dashboards. Visualization simplifies working with data: it allows you to clearly see the results of analysis, quickly identify patterns, and formulate hypotheses. Data scientists and analysts use tools such as Matplotlib, Seaborn, and Plotly for data visualization.
Promising professional fields with this set of competencies are AI, ML, and DL. Let’s take a closer look at them and understand how these concepts are related.
Artificial Intelligence, or AI, is the creation and development of systems capable of solving intellectual problems. For example, such systems can recognize disease symptoms in medical images or play chess with a human. Artificial intelligence technologies are based on big data. Using various methods, specialists create algorithms that imitate human thinking.
Machine Learning, or ML, is a branch of AI that focuses on training computer systems to solve problems and make data-driven predictions. For example, predicting the weather for the next month based on meteorological observations from the previous 10 years. Machine learning technologies underpin the recommendation algorithms on streaming platforms like YouTube and Boomplay Music. To offer users content they are most likely to enjoy, these programs process data about tracks they listen to and movies they watch, and use it to generate recommendations.
Machine learning uses models—programs based on algorithms such as decision trees and linear regression. Big data is fed into the model and training is initiated: the program analyzes the information and processes it according to a specified algorithm.
Deep Learning, or DL, is a more complex form of machine learning. It is based on neural networks—mathematical models with multiple elements: nodes and layers. Deep learning requires even more data than machine learning. However, the resulting system can solve more complex problems, such as recognizing voices and objects in images, analyzing text, and generating responses to user queries. Deep learning technologies have been used to create, for example, ChatGPT, YaGPT, and Midjourney.
Where is Data Science applied?
Data scientists work in a variety of fields. Here are some of them:
● Medicine:
Data scientists are working on systems that help doctors diagnose patients based on their test results. Models for these systems are trained using large amounts of medical data: X-rays, lab results, MRIs, and CT scans. These systems are then implemented in medical facilities. These technologies automate doctors’ routine work: they don’t need to waste time studying images; the systems prompt them with specific findings. This speeds up diagnosis and treatment.
● Business
Data science methods help companies solve many different problems. For example, they can create financial forecasts, manage risks, evaluate potential investments, analyze the market, competitors, and user behavior, and even find the best routes for transporting goods.
● Industry:
Data scientists help control product quality, monitor the technical condition of equipment, promptly identify anomalies in its operation, and optimize production processes. For example, Yandex Data Factory specialists created a model for a large metallurgical plant that provides recommendations for adding impurities during the steel smelting process.
● Information Security
Machine learning algorithms and big data are used to detect cyberattacks and track unauthorized access to data. Models trained on big data can detect various anomalies, such as unusual user behavior online, which could be caused by an account hack. Models help information security specialists quickly identify potential problems. Data science methods are also used to find errors in systems that make them vulnerable to hacking.
Basic steps of working with data
Work on a Data Science project can be divided into four major stages:
1. Develop data requirements.
First, define the project’s goal. For example, a company needs to forecast product demand: when and what people buy most often. This will prevent them from buying more from suppliers than they can sell, thereby reducing costs.
To understand what data will help achieve the goal, it’s important to study the subject area: the market, competitors, and the company’s operational structure. Data requirements are then compiled into a technical specification for the client.
2. Prepare the data.
Let’s say a data scientist has been given order data for the past 10 years. Now they need to make sure it’s suitable for training the model.
The data is cleaned of errors, such as duplicate values, omissions, typos, and anomalies. Exploratory data analysis, or EDA (short for exploratory data analysis), is performed to identify patterns and deviations, relationships, and dependencies between variables to take them into account when training the model. Various methods are used for this, such as factor, correlation, and cluster analysis.
3. Find a solution.
Based on the problem, you need to select a suitable approach to solve it. At this stage, you need knowledge about the data, how different machine learning models work, and what results they produce.
For example, linear regression will predict demand for a product. But only if there’s a linear relationship between the factors influencing that demand. If this isn’t the case, it’s better to use other models. You can select several models at once to test them and compare the results.
After selecting an approach, a sample is formed on which the model will be trained.
4. Build the model.
At this stage, models are built and trained. They are then tested for accuracy on a small set of real data. If the model fails, the training set parameters are adjusted, or a new model is selected, and a new data set is generated.
Despite the challenges of working in data science, it’s an interesting field of study. Mastering it can lead to good earnings, solving interesting problems, and influencing company processes.
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