Data Science is becoming more and more important in the modern world, especially with multiple companies. A lot of universities and data science courses train students to become seasoned professionals in this field. This is due to the new volumes of information that businesses are receiving almost non-stop — about user behavior, user preferences, purchasing data, and so on. Such a considerable amount of various information, which is challenging to analyze by traditional methods and computers, is called Big Data.
It is no exaggeration to say that data is the king of data science because any business insight or conclusion drawn from data analysis can answer any challenging question, thus leading a company to prosperity and customer satisfaction.
Let’s dive into more details about data, its types, and other useful information.
Two Types of Data
There are two types of data — traditional and big data.
Traditional data is not a scientific or “official term,” but is used to illustrate the difference between this type of information and big data.
Traditional data is stored in databases that contain structured tables that include numeric, text, or any other values. This type can be easily handled by one computer.
Traditional information can come from different sources. Usually, we can get it from client records — for instance, we can store data about a data science course student — his or her full name, age, contact information, number of visits, number of contacts to the customer service, and so on.
Another type is big data, which is a large chunk of information that is much bigger than traditional data. It is distributed across multiple computers and can’t be handled and processed efficiently. There are many ways we can receive big data — from social media platforms (Facebook, Twitter, LinkedIn, Quora and so on), financial data, information from our mobile phones, online courses, and other sources.
1. Structured data
When data is structured, it means that it is well-organized and can be stored, retrieved, and processed in a pre-established format. For instance, information about a student of Data Science courses will be organized in a database in a pre-established and ordered format, like his or her full name, age, address, and so on.
This type of data is difficult to organize or categorize. It does not have any specific form. Usually, unstructured information is represented in forms of texts or multimedia files. For instance, great examples of unstructured data are email messages, word document, ppt files, video, and audio files.
It is said that from 80% to 90% of all the information that a company has is unstructured and it is increasing all the time.
Semi-structured data contains both structured and unstructured data that cannot be appropriately categorized, but it does have some properties, like tags, that can be analyzed.
Structured, Semi-structured, and Unstructured Data come in different formats — numbers, text, video files, audio files, images, pdf files, mobile data, emails, social media posts, and other forms.
Big Data is continually growing, and it is captured and retrieved in real-time. For instance, posts on a social media profile of data science courses are being created in volumes even now, and velocity allows companies to grasp the speed of data.
We generate data from different sources — e-commerce platforms (for instance, Amazon, eBay), social media platforms like Facebook, Instagram, Pinterest, online courses, or any other way. Thus, we can generate terabytes, petabytes, exabytes of data.
Raw Data is the type of data that can come in different forms, for instance, in surveys, cookies of a website, user-behavior data and must be converted into a more understandable form that can be processed further for data analysis.
This type of data processing is quite understandable as you label information according to its category, for instance, into categories of numbers, or text, digital image, or any other.
2. Data cleansing
This type is also called data scrubbing. It is also used for organizing data, for example, you get rid of inconsistent data like missing values or misspelled information.
3. Data balancing
Not all data is perfect. If the categories in the data have unequal observations, it will be challenging to show the representation of the population. Thus, we can use balancing methods. For instance, we can extract an equal number of observations for each category and prepare these observations for processing.
4. Data shuffling
We shuffle cards to avoid patterns and repetitions. The same goes for data. We shuffle datasets to prevent any element of bias.
5. Data masking
Companies do care about their clients’ privacy. Thus, data masking help businesses analyze data without compromising customers, but still, give an opportunity to conduct data analysis. Original information is concealed with false and random data.
Big Data in IT
Big data is used in absolutely various industries — IT, healthcare, hospitality, finance, banking, education, eCommerce, entertainment, manufacturing, and many others.
Still, probably the industry that benefits from big data the most is information technology. This industry uses data science, which effectively combines different methods to extract insights from big data, for example, it utilizes artificial intelligence, machine learning and also applies the most sophisticated technologies and systems.
Big data won’t be slowing down. On the contrary, it will be increasing exponentially. That is why professionals who have considerable knowledge and skills in data will be in high demand by all companies across various industries and parts of the world.
The best way to learn about Big Data and to become a skilled professional in this field is to sign up for data science courses. So, if you are interested and want to know more — you can contact us, and we will get in touch with you shortly and provide you with all the necessary information about data science courses in Kyiv.