Data Science vs. Big Data
This is part II of the lecture series. Please read Complete Introduction to Data Science: Lesson 1.
Online purchases, multimedia forms, instruments, financial logs, sensors, text files, and other sources all contribute to the data. Unstructured, semi-structured, and structured data are all possibilities.
Data from blogs, digital audio/video feeds, digital images, emails, mobile devices, sensors, social networks and tweets, web pages, and online sources are examples of unstructured data. Data from system log files, XML files, and text files are examples of semi-structured data. OLTP, RDBMS (databases), transaction data, and other formats are examples of structured data that has already been processed in some way.
This is all “big data,” and putting it to good use is a 21st-century imperative. Simple business intelligence tools, or even data analytics tools, simply cannot process massive amounts of data from disparate sources. Data science, on the other hand, provides companies with advanced, complex algorithms and other tools for analyzing, cleansing, processing, and extracting meaningful insights from data.
There is no such thing as a single data science tool, skill, or method. Instead, it is a scientific approach to big data processing that employs applied statistical and mathematical theory as well as computer tools.
The interdisciplinary strengths of data cleansing, intelligent data capture techniques, data mining, and programming are combined in the foundations of data science. The ability of a data scientist to capture, maintain, and prepare big data for intelligent analysis is the result.
Although the two roles are sometimes confused, this is one point that distinguishes the work of a data scientist from that of a data engineer. The data engineer prepares data sets for the data scientist to work with and extract insights from, but it is data scientists, not “data science engineers,” who perform the intelligent analysis.
In the field of data science, big data is the raw material. Big data is the raw material for data science, which provides the techniques for analyzing the data. It is defined by its velocity, variety, and volume (the 3Vs).
Statistics vs. Data Science
Data science is an interdisciplinary field that includes statistics as well as applied business management, computer science, economics, mathematics, programming, and software engineering. Data science problems necessitate the collection, processing, management, analysis, and visualization of large amounts of data, and data scientists employ tools from a variety of fields, including statistics, to accomplish these objectives.
Data science and big data have a close relationship, and most big data is in unstructured formats with some non-numeric data. As a result, as a data scientist, your job entails filtering out noise and extracting useful insights.
Acquisition, architecture, analysis, and archiving are four data areas that require specific design and implementation. Data science’s “4As” are unique to the field.
Statistics is a broad field that necessitates subject matter expertise. It deals with the analysis of numerical and categorical data, and statistics is an applied field with applications in a variety of fields, including data science.
Statistical theory and methods, for example, enable data scientists to collect data more effectively, analyze and interpret it for specific purposes, and draw conclusions to solve specific problems. When designing and conducting research, data scientists frequently use statistical protocols to ensure that their findings are valid and consistent.
Data scientists can also use statistical methods to thoroughly explore and describe data while fairly summarizing it. Finally, statistical protocols are critical for making accurate predictions and drawing insightful conclusions.
Data Science vs. Data Mining
Data mining is a technique used in both business and data science, whereas data science is a scientific field of study. The goal of data mining is to make data more useful for a specific business purpose. Data science, on the other hand, aims to develop data-driven products and outcomes, usually in the context of business.
Data mining is primarily concerned with structured data, as data science is concerned with large amounts of raw, unprocessed data. Data mining, on the other hand, is a skill that is part of the science, and it is something that a data scientist might do.
Artificial Intelligence vs. Data Science
The term “artificial intelligence,” or (AI), simply refers to computer simulations of human brain function. Learning, logical reasoning, and self-correction are some of the characteristics that indicate this type of brain function. To put it another way, an AI is a machine that can learn, correct itself as it learns, reason, and draw inferences on its own.
Artificial intelligence can be broad or specific. The term “general AI” refers to the types of intelligent computers that we see in movies all the time. They can perform a wide range of tasks that require reasoning, judgment, and thought, almost as well as humans. This has not yet been accomplished.
Narrow AI, on the other hand, employs the same “thinking” abilities, but for very specific tasks. For example, IBM’s Watson is an AI that, under the right conditions, can interpret certain types of medical records as well as or better than humans for diagnostic purposes.
Artificial neural networks are being developed by scientists and engineers in order to achieve artificial intelligence. However, even for a very specific purpose, teaching machines to think like a human brain requires a massive amount of data. This is where data science, as a field, meets artificial intelligence, as a goal, and machine learning, as a method.
The Intersection of Data Science and Machine Learning
Machine learning, AI, and data science all work together. Machine learning is a branch of data science that involves feeding massive amounts of data to computers so that they can learn to make informed decisions in the same way that humans do.
Most people, for example, learn what a flower is without even thinking about it as children. The human brain, on the other hand, learns by doing—by collecting data—on which specific characteristics are associated with flowers.
With human assistance, a machine can do the same thing. The machine can learn that various petals, stems, and other features are all connected to flowers as humans feed it massive amounts of data.
In other words, humans provide the machine with training data or raw data so that it can learn all of the data’s features. If the training was successful, new data testing should show that the machine can recognize the features it learned. If not, more or better training is required.
Machine Learning vs. Data Science
Statistics is a natural extension of data science. With the help of new technologies, it evolved alongside computer science to handle massive amounts of data.
Machine learning, on the other hand, is a process that is part of data science. Machine learning enables computers to learn without the need for explicit programs for each piece of data, and to do so more effectively over time.
Computers use algorithms to train themselves in machine learning, but those algorithms require some source data. That data is used as a training set by the machine, which allows it to improve its algorithm by tweaking and testing it and optimizing it as it goes. It uses statistical techniques like naive Bayes, regression, and supervised clustering to fine-tune the various parameters of its data science algorithms.
Other techniques that require human input, on the other hand, are included in data science as we know it today. For example, a machine can use unsupervised clustering to train another machine to detect data structures in order to improve a classification algorithm. However, a human must still classify the structures identified by the computer to complete the process—at least until it is fully trained.
Data science encompasses data that is not generated by any mechanical process, computer, or machine, and extends far beyond machine learning. For example, data science encompasses survey data, clinical trial data, and virtually any other type of data—the full spectrum.
Data science entails more than just using data to train machines. Far from being restricted to statistical data issues, data science encompasses machine learning and data-driven decision-making. Data integration, data engineering, and data visualization, as well as distributed architecture and the creation of dashboards and other business intelligence tools, are all part of it. In fact, data science encompasses any data deployment in a production environment.
So, instead of a data scientist creating insights from data, a machine learns based on the insights that the data scientist has already perceived. And, while a machine can develop its own insights based on existing algorithmic structures, it must start with structured data.
In short, a data scientist must be familiar with machine learning, which incorporates a variety of data science techniques. However, data from a mechanical process or machine may or may not be included in a data scientist’s definition of “data.”
