Data Science is a broad term that encompasses multiple disciplines. It is a rapidly growing field of study that uses scientific methods to extract meaningful insights from given input data. The rapid growth in the field of data science has opened the eyes of researchers interested in this field to explore more into the multiple disciplines that encompass data science.
Let us discuss a few of these broad areas that are fundamental aspects to be covered for mastering Data science.
Machine Learning: Both Machine Learning and Data Science are buzzwords in today’s technical world. Though data science includes machine learning as one of its fundamental areas of study, machine learning in itself is a vast research area of study that requires good skills and experience to expertise. The basic idea of machine learning is to allow machines (computers) to independently learn from the wealth of data that is fed as input into the machine. To master machine learning, a learner needs to have an in-depth knowledge of computer fundamentals, programming skills, data modeling, evaluation skills, probability, and statistics. With the advancement of new technology, machines are being trained to behave like a human in decision-making capability. In doing so, it is necessary to automate decisions that can be inferred by the machines with the interaction with the environment and understanding from past knowledge. The field of machine learning deals with all those algorithms that help machines to get self-trained in this process.
Machine learning techniques are broadly categorized into three types — supervised machine learning, unsupervised machine learning, and reinforcement learning. To master data science, it is good to be thorough with all the types of machine learning that are immensely used by a data scientist for the extraction of meaningful output from the data provided as input.
Deep learning: Deep learning is often used in data science as it is computationally very competent compared to traditional machine learning methods, which require human intervention before being machine trained. The big players in the market such as Google, Microsoft, and Amazon need to deal with large volumes of data on a daily basis for business analysis and effective decision-making. Deep learning helps in analyzing a bulk amount of data through a hierarchical learning process. The amount of data generated in these companies is massive, raw, and unstructured for which deep learning approaches are used to generate meaningful results.
Natural Language Processing (NLP): NLP will ever remain a standard requirement in the field of data science. NLP is a branch of artificial intelligence, just like machine learning. NLP focuses on bridging the gap between human communication and computer understanding. Nowadays, thanks to NLP that made it possible to analyze language-based data equally as humans, such as reading text, understanding speech, measuring sentiments from the text, and extracting valuable text from a bulk amount of available text. The field of NLP is found to be highly beneficial for resolving ambiguity in the various languages spoken worldwide is a key area of study for text analytics as well as speech recognition.
Statistical data analysis: Statistics is a branch of mathematics that includes the collection, analysis, interpretation, and validation of stored data. Statistical data analysis allows the execution of statistical operations using quantitative approaches. Few such important concepts in statistical data analysis include descriptive statistics, data distributions, conditional probability, hypothesis-testing, and regression. Statistical analysis is an essential area of study in data analytics as it provides tools and techniques to analyze and draw inferences from the provided data. It is an excellent discipline for handling data that needs to be analyzed or to deal with uncertainty by quantifying some results.
Knowledge discovery and data mining: Data mining, a major step in Knowledge Discovery from Data (KDD), has evolved as a prominent field in all these years as the demand for discovering meaningful patterns from the data has given rise to meaningful output for data analysis. We are living in a data age where infinite volumes of data are being generated every second. However, we may be data-rich but may become information poor if these data are not rightly utilized. Data alone makes no sense in the analysis world until this data is converted and interpreted to some meaningful form and this is done through the process of data mining in KDD. The few prominent applications of data mining include target marketing, customer relationship management, loan approval decision-making in banking, identifying customer behavior in retail industries, and fraud detection in financial and other sectors. KDD includes a series of clearly defined steps — data selection, data cleaning, data integration, data transformation, data mining, and pattern evaluation.
Text mining: Text mining is similar to text analytics and includes the method of deriving high-quality information from text. It is a variation of data mining that derives high- quality information by formulating patterns and trends using various methods such as statistical pattern learning. Some of the prominent text mining tasks include text clustering, document summarization, sentiment analysis through text, text categorization, and concept extraction. In data science, text mining broadly involves considering the text as input data and then applying various text mining analysis such as lexical analysis or pattern recognition, to interpret the gathered information from the given text.
Recommender systems: The various web services such as Amazon, YouTube, and Netflix, and various e-commerce sites such as Flipkart and Snapdeal use recommender systems to provide suggestions to online users about new and relevant items. The items (such as videos, music, appliances, or books) suggested are based on the types of items being accessed by the user on a particular website. This indirectly helps in providing a pleasant user experience as well as the revenue generation of these businesses increases drastically. In a typical recommender system, the dataset containing customer and product information is fed as input to a filtering technique.
Data visualization: Data visualization can help in identifying outliers in data, improving the response time of analysts to quickly identify issues, displaying data in a concise format, providing easier visualization of patterns, and easy business analysis. Visualization is the graphical representation of data that can make information easy to analyze and understand. Data visualization has the power of illustrating complex data relationships and patterns with the help of simple designs consisting of lines, shapes, and colors. Enterprises are in a constant chase to hunt and use the latest advanced visual analytics tools so as to be able to present their information through simple, precise, illustrative, and attractive diagrams. The findings in a visualization graph may be subtle, yet it can create a profound impact on a data analyst to interpret the information easily. The most challenging part, however, is to learn how data visualization works and in which case which visualization tool serves the best purpose for analyzing precise information.
Computer vision: Computer vision is a field of artificial intelligence that trains machines or computers to understand and analyze the visual world. The images that are considered as data can take many forms, such as images taken from multiple cameras, images generated from video sequences, or multi-dimensional image data generated from a medical scanner. These types of images are fed as input for automating tasks and creating a computer vision system similar to the human visual system. Computer vision differs from image processing in that it uses the three-dimensional structure of images for a varied angle view of an image for a better understanding of a static scene.
Spatial data management: Geographic information systems (GIS) technology has seen a recent uplift in recent years as companies focus a lot on geospatial data that are generated from multiple sources. Geospatial data are structured data that includes object information in the spatial universe. The objects can be buildings, roads, landmarks, ecosystems, and any such landmarks that consist of many spatial features such as the identity of the object, its location, orientation, and dimension. The positional coordinates of images are represented as coordinate systems that are usually stored in tables for reference. Spatial data analysis is a big area of research that includes static spatial data analysis, spatial data mining, and spatial interaction in application domains using neural networks. Usually, points, lines, and areal units are the basic units used for representing spatial phenomena.
The future of data science is undoubtedly one of the most demanding professions today and for years to come. Though the recent study shows that there are innumerable areas of study in data science, we have listed the fundamental areas of study in data science in this article.
Hope this was helpful.