What is Data Science?

Data science is still a popular issue among trained people and companies focused on gathering data and extracting useful insights to help businesses flourish. Any company may benefit from a large amount of data, but only if it is processed effectively. When we entered the age of big data, the need for storage increased dramatically. Until 2010, the primary focus was on developing cutting-edge infrastructure to store this valuable data, which would subsequently be accessed and analyzed to provide business insights. The attention has switched to processing this data now that frameworks like Hadoop have taken care of the storage portion.

Data Science, in its broadest sense, is the study of data, including where it comes from, what it represents, and how it may be turned into useful inputs and resources for business and IT strategy.

Three components of Data Science:

Data science is made up of three parts: data organisation, data packaging, and data delivery (OPD of data). Let's take a quick look at them:

• Data organization: Data organization is the process of designing and executing the physical storage and structure of data after using optimum data handling techniques. 
•  Data packaging: Data packaging is where prototypes are made, statistics are applied, and visualizations are constructed. It entails changing and integrating data in a logical and visually pleasing manner. 
• Presenting the data: Presenting the data is where the story is told and the value is received. It ensures that the final result is conveyed to those who need to know.

Data science tools

To design models, data scientists must be able to write and run code. Open source tools that incorporate or support pre-built statistical, machine learning, and graphical capabilities are the most popular programming languages among data scientists. The following languages are among them:

• R is the most popular programming language among data scientists. It is an open source programming language and environment for building statistical computation and graphics. R includes libraries and tools for purifying and preparing data, building visualizations, and training and assessing machine learning and deep learning algorithms, among other things. Scholars and researchers in the field of data science utilize it frequently. 
• Python is a general-purpose, object-oriented, high-level programming language with a characteristic abundant usage of white space that promotes code readability. Numpy for managing big dimensional arrays, Pandas for data processing and analysis, and Matplotlib for creating data visualizations are just a few of the Python tools that help with data science.

Data scientists must be able to work with large data processing systems. They must also be proficient in a variety of data visualization tools, including the basic graphics tools included with business presentation and spreadsheet applications, commercial visualization tools such as Tableau and Microsoft PowerBI, and open source tools such as D3.js (a JavaScript library for creating interactive data visualizations) and RAW Graphs.

What exactly is a Data Scientist? 

On Data Scientists, there are numerous definitions accessible. In basic terms, a Data Scientist is a person who specializes in data science. The phrase "Data Scientist" was coined after it was realized that a Data Scientist gathers a great deal of data from many scientific disciplines and applications, such as statistics and mathematics.

Who is a Data Scientist?

A data scientist finds key questions, acquires relevant data from a variety of sources, saves and organizes the data, deciphers meaningful information, and eventually converts it into business solutions and communicates the results in order to positively impact the organization. 

Aside from developing complicated mathematical algorithms and synthesising enormous amounts of data, data scientists have shown communication and leadership abilities, which are required to deliver quantifiable and concrete benefits to a variety of corporate stakeholders.

What is the role of a Data Scientist? 

Data scientists are experts in certain scientific subjects who use their knowledge to solve difficult data challenges. They work with a variety of topics such as mathematics, statistics, and computer science (though they may not be an expert in all these fields). They make extensive use of cutting-edge technology in order to uncover answers and reach critical conclusions for an organization's growth and development. Data Scientists offer data in a far more usable format than the raw data they have access to in both organized and unstructured formats.

The phrase "Data Scientist" was coined after it was realized that a Data Scientist gathers a great deal of data from many scientific disciplines and applications, such as statistics and mathematics. They make extensive use of cutting-edge technology in order to uncover answers and reach critical conclusions for an organization's growth and development. Data Scientists offer data in a far more usable format than the raw data they have access to in both organized and unstructured formats. 

Data scientists, like any other scientific field, must constantly ask and answer the questions of What, How, Who, and Why of the data they have access to. They must create a well-defined strategy and endeavor to achieve the desired goals in a reasonable amount of time, effort, and money.

Business Intelligence (BI) vs. Data Science

Company intelligence (BI) is the process of analysing prior data in order to get foresight and insight into business patterns. Here, BI allows you to prepare data from both external and internal sources, execute queries on it, and construct dashboards to answer issues like quarterly revenue analysis or business concerns. In the near future, BI can assess the influence of certain occurrences. 

Data Science is a more forward-thinking, exploratory strategy that focuses on studying previous or present data and anticipating future events in order to make educated decisions. It provides solutions to the open-ended questions of "what" and "how" events take place. 

Let's have a look at some of the differences.

Conclusion 

For the foreseeable future, data will be the lifeblood of the commercial world. Knowledge is power, and data is actionable knowledge that may determine whether a company succeeds or fails. Companies may now estimate future growth, predict possible challenges, and design informed success strategies by incorporating data science approaches into their operations.

Read more about Data Science in Malaysia.