Data Science - One of the technological advances of the 4th industrial revolution

Today, technological advances are actively helping people automate most of the work in their daily lives. The world of technology is going through the dramatic changes we often talk about - the Fourth Industrial Revolution, typically Data Science. Data science has been ranked as one of the hottest professions and the demand for people working with data is exploding. 

From ancient times, the desire to understand and grasp data trends has been ignited by our ancestors. Thanks to data on population surveys, ancient governments could effectively tax or accurately predict the risk of disasters. Since then, humans have continuously researched and exploited various aspects of data science to apply them in different fields of work. In today's era, it is undeniable that data science has a significant impact on various aspects of our lives. Therefore, data science can be understood as an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Data scientists work closely with relevant parties of businesses to understand their objectives and identify how data can be used to achieve those goals. They design data modeling processes, create algorithms and prediction models to extract the data needed by businesses, then help analyze the data and share detailed information with colleagues.

Do we really need a Ph.D. to become a data scientist?

There is a misconception that you need a Ph.D. to become a data scientist. In fact, data science is suitable for anyone with some computer skills and a passion for self-learning. Knowledge of computer science or programming is not mandatory.

Data Science is a process, not an event. It is the process of using data to understand different things, to understand the world. For me, it's when you have a model or hypothesis about an issue, and you try to validate that hypothesis or model with your data. Data science is the art of discovering insights and trends hidden behind data. It's when you translate data into a story. So, using storytelling to create deep understanding. And with these insights, you can make strategic choices for a company or organization. Data science is a field of processes and systems for extracting data from various forms, whether structured or unstructured. Data science is the study of data. Like biology is the study of biology, physics, the study of physical reactions. Data is real, data has real attributes, and we need to study them if we're going to work with them. Data science relates to data and some scientific disciplines. The definition or name was introduced in the 80s and 90s when some professors were reviewing statistics programs, and they thought it would be better to call it data science. But what is Data Science? I see data science as a human endeavor to work with data, to find answers to questions they're exploring. In short, it's more about data than about science. If you have data, and you're curious, and you work with data, and you manipulate it, you explore it, analyzing data, trying to get answers from data, that's data science. At this point, data science is relevant because we have tons of data available. We used to worry about the lack of data. Now we're drowning in data. Previously, we didn't have algorithms, now we have algorithms. Previously, software was very expensive, now it's open source and free. Previously, we couldn't store a large amount of data, now with a small cost, we can have countless datasets with very low costs. So, tools for working with data, the diversity of data, and the ability to store and analyze data, all are cheap, all are available, all are everywhere, present right here. Now is the best time to become a data scientist.

Everyone you ask will have a relatively different description of data science, but most people agree that its data analysis component is crucial. Data analysis is not new. What's new is the large amount of data available from incredibly diverse sources: from log files, emails, social networks, sales data, patient information files, sports performance data, sensor data, security cameras, and many other sources.

In addition to having more data available than ever before, we also have the computational power needed to provide useful analysis and discover new knowledge. Data science can help organizations understand their environment, analyze existing issues, and reveal previously hidden opportunities. Data scientists can use data analysis to supplement an organization's knowledge by researching data, discovering the best ways to use that data to provide value to the business.

So, what's the process of data science?

Many organizations will use data science to focus on a specific issue, so it's necessary to clarify the question the organization wants to answer. This first and most important step determines how the data science project progresses. Good data scientists are curious and ask questions to clarify the needs of the business. The next questions are: "What data do we need to solve the problem?" and "Where will that data come from?"

Data scientists can analyze structured and unstructured data from various sources, and depending on the nature of the problem, they can choose to analyze the data in many different ways. Using multiple models to explore data will help them see patterns, prototypes, and outliers at times, this will help confirm what the organization suspects, but sometimes it will be completely new knowledge, leading the organization to a new approach.

When the data has shown all this valuable information, the role of the data scientist becomes that of a storyteller, conveying the results to the project's stakeholders. Data science experts can use useful data visualization tools to help stakeholders understand the essence of the results and the proposed actions. Data science is changing how we work, how we use data, and how we approach the world.

References: https://www.coursera.org, https://courses.funix.edu.vn

Compiled by the eNao author team - technology experts from the Developer industry sector