Unit 9: Data Science and Data Gathering — Short Questions

Qualitative data refers to categories or labels that describe qualities or characteristics rather than quantities. Nominal data is used to label or categorize items without implying any order.

Structured Data is organized and formatted for easy searching and analysis: examples include data in spread-sheets and traditional databases. Unstructured Data is more free-form and doesn't fit into a specific format.

A spreadsheet is a digital application or software tool that organizes, analyzes, and stores data in tabular format. It is composed of a grid of rows and columns, with each intersection forming a cell.

A backup is a copy of important data or files stored separately from the original, used to protect against data loss.

NoSQL stands for "Not Only SQL" and refers to a variety of database technologies designed to handle different types of data that may not fit well into traditional databases.

Data pre-processing is the initial and important step in working with data. It involves cleaning and organizing it for analysis.

Ordinal data represents categories with a meaningful order, though the differences between categories are not uniform.

Data science is the branch of knowledge, in which computer programming skills along with mathematics and statistics are used to extract meaningful information from a collection of data.

A computer system is a fundamental and important part of modern life. It has revolutionized the way we work, communicate, learn, and entertain ourselves. In today's world, we are surrounded by a lot of data, which may be on our computer system or otherwise.

Big data plays a significant role in the Internet of Things (IoT) ecosystem by enabling the collection, storage, processing, and analysis of massive volumes of data generated by IoT devices. IoT devices generate continuous data.

Data visualization is the graphical representation of data to get meaningful insights, trends, and patterns from data. The visual elements which help in data visualization are charts, graphs, maps, figures, and dashboards, etc.

These include websites, databases, and digital tools where you can find and collect information. For example, if you're researching the most popular pets, you might use the internet to find statistics or articles about pet ownership.

It is a method of collecting information from individuals. The basic purpose of a survey is to collect data to describe different characteristics such as usefulness, quality, price, kindness, etc.

• Primary Data Collection: Involves the collection of original data directly from the data source or via direct interaction with the respondent.
• Secondary Data Collection: Involves data collection using existing data collected by someone else for some purpose. Such data is usually available in the form of published material like research papers, books, websites, etc.

Data collection is the systematic process of gathering, documenting, and organizing data from a variety of sources to extract significant insights, facilitate informed decision-making, or allow for rigorous analysis.

Data Science plays a crucial role in determining the place of various products in various shelves of the supermarket. Data science involves gathering, analyzing, and interpreting large amounts of data to find patterns and useful information.

It is information about the data in a sample. It can help understand the values better. It may include the total number of values, minimum value, maximum value, along with the mean value and the standard deviation corresponding to a data collection.

Statistics is the analysis of the frequency of past events and probability is to predict the likelihood of future events. Statistics is used in various fields such as science, business, economic, social sciences, and more, to understand patterns, trends, and relationships within data.

E-commerce, short for electronic commerce, refers to the buying and selling of goods and services over the internet. It encompasses a wide range of online transactions, including retail sales, business-to-business (B2B) transactions, online auctions, and digital downloads of products like software, music, and books.

The three Vs of big data are:
• Volume: Refers to the amount of data. Big data deals with huge volumes of low-density, unstructured data.
• Velocity: Refers to the speed of data, which is the fast rate at which data is received.
• Variety: Refers to the various formats and types of data that are available. Traditional data types were structured and fit neatly in a relational database.

• Predictive Maintenance: It is a maintenance strategy that uses the analysis of existing data to predict when equipment, machinery, or a product is likely to fail.
• Fraud and Compliance: Big data analytics can identify and detect unusual suspicious patterns and anomalies.

The range is the simplest measure of spread. It is the difference between the highest and lowest values. For example, for the test scores 60, 75, 83, 91, and 95, the range is 95-60=35.

Variance measures how spread out the values are from the mean. It gives a sense of how much the values in a dataset vary from the mean. Formula for the variance is:
S² = Σ(xi-x̄)²/(n-1)

Formula for standard deviation is:
S = √(Σ(xi - x̄)²)/(n - 1)