Everything About Data Science.
The study of data and algorithms for problem-solving can be
summarized as data science. Understand what this field of study is, how it
works, what it is used for, and how it has become so essential in the business
world.
 |
| Everything About Data Science. |
Everything About Data Science.
The evolution of digital life has generated a new wealth for the business world: data. In other words, the recording of information that is constantly being produced, whether by people or machines, can become a powerful business tools for companies.
However, for this data to become a source of knowledge, there needs to be someone who studies and analyzes them correctly, and this is where the field of data science comes into play.
What is data science?
Data science is a comprehensive and multidisciplinary field of study that encompasses data, algorithms, and technologies with the ability to extract valuable insights from structured and unstructured data.
The goal of extracting this information is to find answers to complex problems and situations, identify trends, and generate insights through various types of analyses.
For example, identifying a high volume of traffic to specific content on your website may indicate that it is performing well, which could be an opportunity to explore similar topics further.
The information obtained through data science is, in most cases, used for making important decisions, such as creating new products or services, updating existing products, making changes in businesses, and even predicting the future of an organization.
What is the purpose and how does Data Science work?
The main purpose of data science is to transform data, whether structured or unstructured, into knowledge for a company or project. This is because isolated or disorganized data, without analysis, is merely scattered information. Therefore, they need to go through a process, such as data science, to become a source of knowledge that can serve as a basis for actions and improvements, providing a competitive advantage for companies.
However, it is worth noting that this competitive advantage is related to various areas within a company. The knowledge generated from data can be utilized in marketing, sales, HR, product development, customer experience, finance, and any other area that strategically uses information for its actions.
With this in mind, we can understand that data science is not only useful for a specific type of company or a single industry. It can be employed by companies from various fields of operation, as long as there is a valid process for data capture, storage, and processing.
This necessary process for executing data science also includes validating the sources and accuracy of the data, ensuring that the results are precise and true, and not compromising decision-making based on them.
Importance of data science for companies:
It is no surprise that nowadays, the amount of data generated at every moment is enormous. From payment systems to voice searches on devices like smart speakers and smart TVs, there are countless sources of data collection.
However, raw data, without any type of treatment or processing, doesn't tell us much because it's just isolated groups of information. This is where data science becomes important, as it can identify opportunities and important insights for companies by conducting different types of studies and data analysis.
The information obtained through data science can generate recommendations for strategic changes and improvements within a company, from expense control to the development of new products and technologies.
For example, a baby diaper brand wants to expand its business in a new city. Therefore, they need to gather raw data such as the number of pregnant women and babies aged 0-3 years in each city, and sales volume in pharmacies and supermarkets, among other factors. Then, with data science, this data is processed, meaning it's organized, combined, and analyzed, to provide market forecasts and projections that will help define the best path for business expansion.
Data science allows us to have a broad view of market trends, predictions based on historical events, data correlations and associations, behavior patterns, and even ways to group information. It is through data science that we can, for example, predict buying behaviors during events or special dates, or identify sales patterns based on different days of the week, among countless other possibilities.
What are the processes in data science?
The practice of data science involves several fundamental steps to achieve the answers that a project or a company needs. Below, we will see the sequence of these steps and what happens in each of them.
Data collection:
The process begins with data collection. The sources of data may vary depending on each company, but it is possible to use CRM systems, ERPs, mobile devices, IoT (Internet of Things) devices, and cloud data, among other diverse sources.
Data transportation and protection:
In this part of the process, the data collected from various sources within the company are transferred to corporate networks, where they are centralized so that responsible professionals can proceed with the process.
Data protection is crucial here to ensure that only authorized individuals can access this information, as data breaches can cause various problems for the organization.
Data storage and processing:
After transportation, the data needs to be stored in infrastructures capable of processing and validating them appropriately. Nowadays, most companies opt for cloud services for information storage. This way, the data is kept in secure yet easily accessible locations for those involved in the project.
Data analysis and sharing of results:
After completing the more "operational" steps, the main stage for data science begins data analysis. This is one of the most important aspects of a data scientist's work, where algorithms, calculations, formulas, and analytical models are applied to obtain the desired answers and insights.
The analysis of the information obtained through data studies should generate reports and recommendations, which are shared with other areas so that they can serve as the basis for new strategies and decision-making.
Tools for data science:
The day-to-day work of data scientists can involve different tools, depending on the specific role and type of company.
Among the most commonly used programming languages in the field, we can mention R and Python, both open-source, user-friendly, and accessible to professionals at various levels.
It is also common for data science professionals to use tools such as Microsoft Excel or Google Sheets, Microsoft Power BI, Tableau, and Oracle Analytics Cloud for data visualization and analysis.
Difference between data science, Artificial Intelligence, and Machine Learning:
It is very common for data science to be seen in association with Artificial Intelligence (AI) and Machine Learning, as they are often worked on together.
However, make no mistake, each is responsible for a different function. In summary, we can differentiate them as follows:
- Machine Learning: It is a set of methods used for automatic pattern identification (learning) by an algorithm (the machine), which can be used for decision-making or performing actions such as path identification, and facial recognition, among others. It is a branch of Artificial Intelligence.
- Artificial Intelligence: Artificial Intelligence refers to the use of computers and algorithms to perform routine tasks typically carried out by humans, such as driving cars, approving credit requests, or controlling enemy characters in a video game. It can utilize Machine Learning (e.g., driving a car) or may not (e.g., controlling video game characters).
- Data Science: It is a field of study where insights are obtained through data using resources for data analysis, data visualization, Machine Learning, and Artificial Intelligence. It allows us to identify information such as purchasing patterns, behavior patterns in applications, and sales cycles, among others.
Who are data professionals?
There is often some confusion regarding the professionals who are part of the data field. To help you understand the difference between each type of professional, we will explain the responsibilities of a data scientist, a data engineer, and a data analyst.
Data Engineer:
A data engineer is responsible for creating algorithms and developing systems and structures to gather and centralize data. They should have the ability to identify trends and use algorithms to make the process more efficient and accurate.
Data Scientist:
A data scientist has knowledge and experience in computer science, statistics, and mathematics and has one of the broadest roles in the data field. Their responsibilities are focused on analyzing large volumes of data (big data) and gaining insights from it.
Within the field of data science, there is another type of professional that has been widely used by companies: the citizen data scientist. This is a professional from another area, such as marketing, sales, and human resources, among others, who may not have extensive experience in data, mathematics, and statistics but has been trained in data science to work on projects in the field with their unique perspective and experience.
For example, a marketing specialist can identify potential business opportunities through data analysis, or a product professional can identify opportunities for creating new products or improving existing ones.
Data Analyst:
Finally, a data analyst is responsible for collecting, analyzing, and interpreting information and insights obtained from data. Their recommendations are essential for data-driven decision-making by other departments within the company.
While a data scientist relies more on algorithms and machine learning to create statistical models for predictive results, data analysts have a more business intelligence-oriented perspective, focusing on generating insights and analyzing patterns.
What is the job market like for Data Science?
A career in data science is definitely among the careers of the future. We can see that a career in data has a very promising future in the job market. in the first half of 2021, there was a 485% growth in the number of job openings for data professionals.
Finally, we can say that Data Science will have a great demand in the future. Don't hesitate to enroll in one of the online Data Science courses to acquire this knowledge and consequently add a positive impact to your monthly salary. I wish you success.