What happens to all this data? Where does it go? What do we use it for? And, What do most popular data science terms resemble?

Data science is the answer to all these questions. Data science is the field of science that applies algorithms, methods, and processes to extract information from random and non-random data. Simply put, it is the process of finding patterns among the noise and using it to predict and solve various problems across a wide range of applications. I will come back to 6 data science terms that everybody must know in 2023, very shortly.

It harvests the power of statistics, data analysis, and modeling, along with the use of data structures, algorithms, and machine learning to interpret the findings from massive hordes of data. From Spotify’s recommendation algorithms to predicting human behavior, data science has a wide variety of applications in today’s fast-paced world.

At Sunway College, we primarily focus to bring the change that is required in the current digital space. The current generation is the internet generation. According to a report by Statista, in 2021 alone, humanity created 74 zettabytes of data. That’s 74000000000 terabytes of data, in ONE year. That’s up from 59 zettabytes in 2020, and just below the projected 94 zettabytes of data in 2022.

Most Popular terms in Data Science

To understand data science, here are a few of the basic keywords and terms that you need to know about today.

Algorithms:

In computer science, we can define an algorithm as a specific set of processes used to solve a problem or perform computation. Algorithms are hardware and software-specific instruction list that complete the given process in hand. For example, the simplest of algorithms perform tasks like sorting a set of numbers in descending order, while the most complex set of algorithms guide and work on a natural language processing system.

Algorithms are widely used in the field of data science. Since a lot of data science is focused on big data, data scientists require a precise algorithm to reduce the massive processing times and cost of operating on these data. Some of the common algorithms used in the field of data science include Linear Regression, Naive Bayes, Decision Trees, Gradient Boosting algorithms, etc.

Data Mining:

We can define data mining as the process of converting raw data into useful information. Data mining involves looking into large sets of data in a database, analyzing patterns and behaviors of the data entities, and using the result to create better algorithms, strategies, and plans for the business.

Data science, as the name suggests, heavily operates on data. This means it requires a large amount of data to work with. It involves various data mining and warehousing techniques on the data with classification, clustering, and predictive analysis of the given data. Large companies use data mining to create better algorithms, understand customer behavior and create AI and ML-powered hardware and software.

Big Data

Big Data can be defined as data, that is BIG (literally!). It defines large volumes of data that can be structured or unstructured, mined from various sources in a large quantity. There are five Vs that define big data: Variety, Volume, Velocity, Value, and Veracity. Big Data is sourced from large, complex sources.

Big Data poses a unique challenge addressed by data science. It is a massive capital for businesses and companies that can analyze this data to understand customer behavior, insightful analysis of products and services offered, and predict their purchase and usage behavior. Data science uses various algorithms and processes to derive insights from the big data.

Deep Learning

In computer science, we can define deep learning as the process of machine learning that uses structures similar to human brains (called neural networks). Deep learning is used to make better algorithms, data sets, and AI hardware and software.

Deep learning uses powerful computers and large sets of data with output (called supervised learning) and without output (called unsupervised learning) to understand and predict patterns and outcomes.

In Data Science, we employ the use of neural networks and methodologies to study big data with massive models on a large level computational scale. Usually, we utilize deep learning to create better algorithms, and insights on specifics such as user experience of a product, facial recognition, behavior pattern processing, etc. Data science combines the power of deep learning, data, and computer science to generate new insights and value for businesses.

Web Scraping

Web scraping is the web data extraction process used to extract data from websites. It is the process of using automated technology to obtain large amounts of unstructured data, usually in the form of HTML, from a large number of websites. This data is then structured, stored methodically in a database, and used for analysis, training, and research purposes.

Data scientists use various existing frameworks such as Scrapy, a python-based web crawler. Another better alternative data scientists use is Beautiful soup, a similar tool to extract a large amount of data from websites and store it in databases. We use web scraping for price monitoring across e-commerce platforms, and email marketing. Similarly, emotion analysis across social media platforms and news monitoring purposes are also done using web scraping. This data can also be fed to machine learning algorithms for training and prediction purposes. 

Natural Language Processing (NLP):

One of the most frequent data science terms. Natural Language Processing is a subfield of deep learning that is concerned with Human Linguistics. It is the process of identifying and understanding meaningful phrases from a human language and generating meaningful phrases and sentences that look like a natural form. NLP includes two sub-sections, Natural Language Understanding (NLU), and Natural Language Generation (NLG).

Natural Language Processing involves five phases. Lexical (structure) analysis, parsing, semantic analysis, discourse integration, and pragmatic analysis. Some of the well-known areas of NLP are Speech recognition (Siri, Google Assistant), Chatbots(Maya chat, ), and Optical Character recognition (Google Lens,).

Conclusion

We can understand the importance of data science in today’s technology-infused world. There are wide domains for data science, and understanding these terms better help students and enthusiasts get serious in this space. We have introduced two new courses in a digital space. BSc (Hons) Computer Science and Artificial Intelligence.

Our Bachelor’s course in Computer Science and Artificial Intelligence solely focuses on preparing students with the required skills academically and practically to pursue a career in artificial intelligence and data science. We do have a team of field experts and academically profound professors with their guidance. You can achieve the best in the data science and artificial intelligence field.