Malaysia Data Science

What Is Data Science?

According to revolutionary research published in 2013, 90% of the world's data was produced in the preceding two years. Allow it to sink in. We've acquired and processed 9x the quantity of data in only two years as compared to the preceding 92,000 years of human history combined. And it shows no signs of slowing down. It's estimated that we've already produced 2.7 zettabytes of data, with that amount expected to rise to 44 zettabytes by 2020. 

What are we going to do with all of this information? How can we make it work for us? What uses does it have in the actual world? Data science is the field that deals with these issues.

Every organization will claim to be engaged in some type of data science, but what does it entail? Data science is devoted to the extraction of clean information from raw data for the formulation of actionable insights. The field is growing so quickly and revolutionizing so many industries that it's difficult to fence in its capabilities with a formal definition, but in general, data science is devoted to the extraction of clean information from raw data for the formulation of actionable insights.

Our digital data dubbed the 'oil of the twenty-first century', is the most important in the sector. In business, research, and our daily lives, it offers enormous benefits. Your commute to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even your fitness tracker's health data are all useful to various data scientists in different ways. Data science is responsible for giving us new goods, offering breakthrough insights, and making our lives more convenient by sifting through huge lakes of data, looking for connections and patterns.

Why Data Science?

Traditionally, the data we had was generally organized and modest in quantity, and it could be evaluated using basic business intelligence tools. Unlike old systems, when data was primarily organized, today's data is mostly unstructured or semi-structured. Take a look at the data trends in the figure below, which reveals that more than 80% of data will be unstructured by 2020.

This isn't the only reason for Data Science's popularity. Let's take a closer look at how Data Science is employed in many fields.

• What if you could deduce your clients' specific requirements from current data such as their previous browsing history, purchase history, age, and income? You probably had all of this data before, but now that you have a larger and more diverse set of data, you can train models more efficiently and provide more precise product recommendations to your clients. Wouldn't it be fantastic if it meant more business for your company?

• To further appreciate the importance of Data Science in decision-making, consider a different situation. What if your automobile was smart enough to take you home? Self-driving cars construct a map of their environment using live data from sensors such as radars, cameras, and lasers. It uses complex machine learning algorithms to make judgments based on this data, such as when to speed up, when to slow down, when to overtake, and where to take a turn.

• Let's take a look at how Data Science may help with predictive analytics. Take, for example, weather forecasting. To create models, data from ships, aeroplanes, radars, and satellites can be gathered and evaluated. These models will not only anticipate the weather but will also aid in the prediction of natural disasters. It will assist you in taking adequate precautions ahead of time and saving countless lives. 

How Does Data Science Work?

Data science entails a wide range of disciplines and areas of knowledge in order to generate a comprehensive, complete, and sophisticated view of raw data. To efficiently filter through confusing volumes of information and communicate just the most critical portions that will help drive innovation and efficiency, data scientists must be adept in everything from data engineering, arithmetic, statistics, sophisticated computers, and graphics.

To develop models and make predictions using algorithms and other approaches, data scientists rely extensively on artificial intelligence, particularly its subfields of machine learning and deep learning.

In general, data science has a five-stage life cycle that includes:1: 

• Data acquisition, data input, signal receiving, and data extraction are all examples of data capture. 

• Data warehousing, data cleansing, data staging, data processing, and data architecture must all be maintained. 

• Data mining, clustering/classification, data modelling, and data summarization are all steps in the process. 

• Data reporting, data visualization, business intelligence, and decision-making are all things that need to be communicated. 

• Exploratory/confirmatory, predictive analysis, regression, text mining, and qualitative analysis are all examples of analyses.

Data Science Prerequisites

Here are some technical terms you should be familiar with before diving into the world of data science. 

• Artificial Intelligence (AI) 

The backbone of data science is machine learning. Data scientists must have a strong understanding of machine learning (ML) as well as a fundamental understanding of statistics. 

• Modelling 

Based on what you already know about the data, mathematical models allow you to make rapid calculations and predictions. Modelling is a subset of Machine Learning that entails determining which algorithm is best for solving a particular issue and how to train these models. 

• Statistics 

The foundation of data science is statistics. With a firm grasp on statistics, you can extract more intelligence and provide more relevant outcomes.

• Programming 

A good data science project necessitates some amount of programming. Python and R are the most widely used programming languages. Python is particularly popular because it is simple to learn and supports a variety of data science and machine learning packages. 

• Databases 

A good data scientist should know how databases function, how to maintain them, and how to extract information from them.

Final Thought

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.