What is data science? In simple words about complex
Data science is about what surrounds us and influences our decisions. This is the art of getting knowledge from data, which everyone has to learn to varying degrees. Indeed, today data science is of great importance for business, industry and research against the backdrop of growing digitalization. When you hear about Data science for the first time, you will most likely find it something incredibly complex and inaccessible. But as soon as you understand this topic a little, you will discover this discipline for yourself from a completely different side.
Data science is several directions at the same time: mathematics, statistics, programming, analytics, artificial intelligence. Together, this is all used for subsequent decision-making and strategic planning.
What is Data Science and what are the goals?
Data Science is a discipline that combines different roles, tools, and processes that help gather useful business insights. In general, work with data occurs in several stages, namely:
Collection. This is the beginning of the life cycle and in this case both raw structured and unstructured data from a huge number of sources can be used. Collection occurs through a variety of methods, from manual input, web browsing, to real-time streaming of data from systems and devices. The source can be structured and unstructured data: log files, video, audio, images, social networks, and more.
Storage and processing. Since data is presented in different formats and structures, companies are considering different storage systems based on the type of data. In turn, data storage and structure standards are set by management teams. This simplifies workflows associated with analytics, machine learning, and deep learning models. This phase includes cleaning, transforming, and merging data using ETL (Extract, Transform, Load) jobs or other technologies. This is necessary to improve the quality of data before uploading it to the storage. All this may sound very complicated, but if you study this topic, Data Science will seem like something incredibly interesting.
Analysis. At this stage, perhaps, the most interesting begins. Data scientists perform pre-analysis to explore patterns, ranges, and distributions of values in data. The research aims to encourage data analysts to develop hypotheses for A/B testing. It is also necessary to determine the relevance of the data for use in predictive analytics, machine learning, and/or deep learning modeling. Depending on the accuracy of the model, companies often rely on this information to make business decisions.
Formation of the report. The final stage is the publication of the analysis results in the form of reports and other data visualization options. In this form, it is much easier to perceive information and how it affects the business. You can create visualizations using the components of the R or Python programming languages.
The question is, what is all this for?
We can only say one thing for sure, Data Science helps in informed decision making and forecasting. Data Science has found application in various fields and has become an interdisciplinary field that integrates knowledge from statistics, mathematics, programming and domain knowledge to analyze data, extract information and identify patterns. And to understand the importance of Data Science and how it affects decision making, it is worth considering the role of this discipline in different contexts.
Data processing and analysis
With the help of Data Science, you can analyze large amounts of data that have been obtained from different sources. Careful analysis helps uncover hidden patterns that can influence decision making.
Forecasting and modeling
Data science involves the use of statistical and machine learning models to predict future events and generate results based on historical data. For example, take forecasting demand for products, prices in the market, or customer behavior. With all this information, manufacturers can produce as many products as necessary to cover demand or set prices based on the purchasing power of customers. And this is far from the only example where forecasting can be used.
Process optimization
Where without it. Through deep analysis, it is easier for companies to find gaps in business processes and find solutions that will help optimize the work of the entire company or a specific department in a way that increases efficiency.
Development of recommender systems
Data Science makes a significant contribution to the creation of personalized recommendations. You can find them in online stores or on streaming services like Spotify or YouTube. Users only receive content or product offers that match their preferences.
Making decisions
Data Science is the tool that can provide factual information and analytics. The data obtained helps to make more informed decisions that will help in business.
Predicting customer behavior
With data science, it is sometimes much easier to predict customer preferences and behavior. This, in turn, allows you to quickly and accurately fine-tune your marketing strategies and improve your customer experience.
Risk and Vulnerability Analysis
Do not forget about the role of Data Science in risk analysis and finding vulnerabilities in various areas, such as finance or cybersecurity.
Data Science is used in many areas, including medicine, science, logistics, energy, and more. And wherever this discipline finds its application, it plays a key role in decision making, optimization and performance improvement. In turn, data scientists correctly apply the information received, but they are not necessarily directly responsible for all processes.
When talking about the responsibilities of a data scientist, also known as a Data Scientist, it’s worth noting that they can overlap with those of an analyst, especially when it comes to exploratory data analysis and visualization. But in general, the requirements for the skills of a data specialist are much wider in comparison with the same analyst. And what they have in common is the use of common programming languages such as R and Python.
To perform all these tasks, Data Scientist must have certain computer science skills. The duties of such a specialist go far beyond the ordinary business analyst or data analyst. And here we come to the topic of Data Scientist skills. For such specialists, it is important to be able to do the following:
Know enough about the business to ask relevant questions and be able to identify business vulnerabilities.
Apply statistics and informatics for analysis.
Use a wide range of tools and methods to prepare and extract data. Here it is important to know not only the database, but the tools of intellectual analysis, as well as data integration methods.
Extract actionable data with predictive analytics and artificial intelligence.
Write programs to automate data processing and calculations.
Explain how the results can be used to solve business problems.
Collaborate with other data scientists.
These are skills that are in high demand, and if you decide to start a career in Data Science, try learning different programming languages for data processing and analysis.
The development of Data Science brings a lot of advantages to modern companies, namely:
Ability to predict current income and evaluate business performance, as well as determine the direction of the company’s development.
The ability to model new tactics and strategies for their successful implementation in the company.
Automate processes to reduce operating costs, improve the efficiency of the organization.
Leverage AI-powered solutions to provide high quality products and services to customers, thereby increasing customer satisfaction.
Use of Data Science in various fields
Data Science is a universal discipline that uses methods, algorithms and tools to extract knowledge and information. But the most interesting thing is that it has found application in various areas, providing improved productivity, making more informed decisions and optimizing business processes. Most often, Data Science is used in areas such as:
Business and marketing. Data Science has found application in this field to analyze sales, consumer and market data. Access to analytics tools allows you to identify trends and understand consumer behavior. In addition, one should not exclude the possibility of forecasting demand, sales to optimize stocks.
Healthcare. The amount of data that is used in medicine is hard to even imagine, but it can be used to predict disease risks or improve diagnosis. In general, there can be many applications, including for predicting treatment outcomes, optimizing medical records, and so on.
Finance and banking. This area, like the healthcare industry, is dominated by a huge amount of data that can be used to develop risk models for credit scoring and fraud prevention, to determine optimal investment strategies, or to analyze customer behavior.
Transport and logistic. Data Science is already widely used to optimize routes and schedules to reduce costs as well as improve vehicle efficiency. It is also worth mentioning the forecasting of demand for transport services for more efficient planning and provision of services, monitoring and data processing to ensure security.
Including Data Science is widely used in education, public administration, public services, and so on. This discipline is useful for developing monitoring and predictive systems to improve public safety and crisis management, and in general, the possibilities of using Data Science are constantly expanding. It is worth noting that the successful use of Data Science requires a good understanding of business needs and competence in analysis and machine learning.
Overview on the main tools in Data Science
Now about the important. Data scientists use various data analysis and processing tools in their practice. In most cases, they rely on popular programming languages for research. Most of the tools are open source and support built-in statistical modeling, machine learning, and graphics capabilities. The most popular programming languages in this area are:
R. It is an open source programming language with its own development environment – R Studio.
Python. It is a dynamic and flexible programming language that includes many libraries such as NumPy, Pandas, Matplotlib for fast analysis.
The Data Scientist also uses SAS in his practice – this is a complex set of tools, including for visualization. In addition, Data Scientists gain skills in using big data processing platforms such as Apache Spark, the Apache Hadoop open source framework, and NoSQL databases. In general, the choice of tools that are used to analyze and process data is incredibly large and it is rather difficult to cover all of them in one article.
