WorksheetsFPA Final Exam
Total questions: 83
Worksheet time: 2hrs 24mins
Data is generated at an unprecedented speed. Real-time or near-real-time data such as social media feeds or sensor data.
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What are the four complexities of data?
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What challenges comes from data variety?
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It refers to the quality and accuracy of data.
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Data that is not related to the study and needs to be removed.
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Data comes from various types and formats; including structured and unstructured (images, text, videos, etc.)
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What are the factors that contribute to data validity?
Accuracy
Timeliness
Consistency
Completeness
All of the above
It refers to a type of data complexity that is a broad amount of data that is not interrelated. This is also known as the Big Data.
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This refers to the data organized into a predefined format, typically in relational databases or spreadsheets.
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This refers to information associated with specific geographical locations. It includes maps, satellite, imagery, GPS coordinates, and geographic information system (GIS) data.
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It refers to the quality, accuracy, and reliability of the data. It is about dealing with noisy, incomplete, or inconsistent data.
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In the fruit analogy, what do you call the 1% of the whole that makes sense to the whole analysis?
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This refers to data that lacks a predefined structure or organization. It does not fit neatly into tables or databases and is often text-heavy.
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It encompasses various forms of media, such as images, videos, audio recordings, and presentations and analyzed through patterns.
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It is generated continuously and in real time. It comes from sources like sensors, IoT devices, social media feeds, website clickstreams, financial market data, and more.
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Data complexity refers to formats such as tweets, medical data, search engine queries, etc.
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It is the rate at which data is being generated, captured, or delivered.
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Data complexity refers to petabytes, terabytes, megabytes.
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Data complexity refers to time speed such as every millisecond, every hour, every day.
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A search technique that aims to understand the meaning and context of the user queries and search results, rather than relying solely on keyword matching.
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A software system that analyzes large volumes of unstructured information to discover, organize, and deliver relevant knowledge to the client or the application end-user.
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It is a key component of data modeling that defines the associations or connections between entities.
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It is the method of searching for information or content by using specific words or phrases.
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It is a key component of Data Modeling that represents the objects, concepts, or things in the real world that are relevant to the system being modeled.
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What are the two main concepts of searching your data?
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It is a key component of data modeling that is used to uniquely identify instances of an entity. They ensure data integrity and provide a way to establish relationships between entities.
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It is a key component of data modeling that defines the rules and restrictions that data must adhere to.
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An approach used in customer behavior prediction that refers to identifying customers who are most likely to stop using a product or service.
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It is a key component of data modeling that is defined as the format and properties of the values that can be stored in attributes. Common data types include integers, floating-point numbers, strings, dates, etc.
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It is a key component of data modeling that is the process of organizing data in a database to minimize redundancy and improve data integrity
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An important application of predictive analytics in which it helps in making informed business decisions and implementing targeted strategies to enhance customer experiences and drive growth.
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An approach used in customer behavior prediction that identifies opportunities by predicting which products or services a customer is likely to be interested in based on their past behavior and preferences. This enables businesses to recommend relevant products or services to customers, increasing the chances of additional purchases and higher revenue.
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It is a key component of data modeling that refers to the properties or characteristics of entities.
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It refers to the process of creating a conceptual representation of data structures and relationships with a system.
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It is a key component of data modeling that involves creating visual diagrams to represent the entities, attributes, relationships, and constraints.
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An approach used in customer behavior prediction which refers to a sales technique where a salesperson attempts to redirect or divert a customer's attention or interest from a particular product or brand to an alternative product or brand that they believe would be a better fit for the customer.
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The practice of encouraging customers to purchase a comparable higher-end product than the one in question
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A practice of inviting customers to buy related or complementary items
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An approach used in customer behavior prediction that involves estimating the potential value a customer will bring to a business over their entire relationship.
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It is a key component of data modeling that involves documenting the design decisions, definitions, and specifications related to the data model
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An approach of customer behavior prediction that uses scenario to make informed business decision.
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The result of similarity measure is called?
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In a data matrix which contains items, objects, instances, or observations?
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It is a valuable technique for interpreting large volumes of data. It involves identifying hidden clusters of similar data items, providing insights such as the number and characteristics of these groups within the data set.
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An approach used in customer behavior prediction that involves forecasting when and what customers are likely to buy. By analyzing historical transaction data and customer characteristics, predictive models can be built to estimate the likelihood of a customer making a purchase in the near future.
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An approach used in customer behavior prediction that clusters customers into distinct groups such as based on their behavior, preferences, or characteristics.
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It is an intelligent separation of data into groups of similar data items.
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It is a table of numbers, documents, or expressions, represented in rows and columns
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In a data matrix which contains the characteristics of an item?
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The strength of a relationship between two or more items can be quantified as (a) : A mathematical function computes the correlation between two data items.
A clustering algorithm which builds a hierarchy of clusters by recursively merging or splitting them based on their similarity.
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It is a clustering algorithm that groups data points based on their density in the feature space. It identifies clusters as regions of high density separated by regions of low density, while also detecting noise points.
Density-based Space Clustering of Applications with Noise
Density-based Spatial Clustering of Applications with Noise
Driven-based Spatial Clustering of Alternatives with Noise
Density-based Spatial Clustering of Alternatives with Noise
It is a clustering algorithm that divides a dataset into K-clusters by minimizing the total squared distance between data points and their assigned centroids.
Example:
Cluster 1: Centroid (low income, high spending score)
Cluster 2: Centroid (medium income, medium spending score)
Cluster 3: Centroid (high income, low spending score)
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It is the process of organizing and categorizing based on specific criteria or attributes. It involves assigning labels or tags to data elements to indicate theoro characteristics, properties, or sensitivity levels.
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It is used to predict the category of the grouping that a new and incoming data object belongs to.
Data Classification
Data Clustering
It requires labeled training data where each instance is associated with a known class label
Data Classification
Data Clustering
It is used to describe data by extracting meaningful groupings or categories from a body of data that contains similar elements
Data classification
Data clustering
Unsupervised learning technique, which means it does not require any predefined class labels.
Data Classification
Data Clustering
It relies on the availability of a representative dataset with known class labels to train the classification model effectively.
Data Classification
Data Clustering
It does not require any prior knowledge or labeled data. It aims to discover patterns or structure in the data on its own.
Data Classification
Data Clustering
A predictive model that can assign class labels to new, unseen data instances.
Data Classification
Data Clustering
Grouping or partitioning of data instances into clusters based on their similarities.
Data Classification
Data Clustering
The support vector uses a mathematical function, often called (a) ?
Predictive models, that use the technique are called? Face recognition, Medical diagnosis, and Fraud detection are examples of classifiers that discover hidden patterns and trends in data. When an unseen relationship comes to light, it can predict the outcome (a class, category, or numerical value) based on newly input data
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A part of the classification model that predicts the class or category of an unknown event, phenomenon, or future numerical value.
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May loan ako sa home credit, Gcasg, Climb bank, Pag-ibig tapos magloloan ako sa isa pang ban pero nagbabayad ako lahat, no delays at walang laktaw. What category rist level am I?
Risky
Moderately Risky
Not Risky
Stages of Data Classification: Entails training the classification model by running a designated set of past data through a classifier. The goal is to teach your model to extract and discover hidden relationships and rules.
Learning Stage
Prediction Stage
Stages of Data Classification: Consists of having the model predict new class labels or numerical values that classify data it has not seen before. The goal is to come up with the test data.
Learning Stage
Prediction Stage
It is an approach to analysis that can help you make decisions.
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What is the formula of the Decision Tree?
(Success Rate x Business Value) + (Failure Rate x Business Loss)
(Success Rate + Business Value) x (Failure Rate + Business Loss)
(Success Rate x Failure Rate) + ( Business Success x Business Loss)
What is the expected value for Food Truck business?
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What is the expected value for Restaurant business?
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What is the expected value for Bookstore business?
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What is the most profitable business to establish?
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It is a supervised machine-learning algorithm used for classification and regression tasks. It is particularly effective for handling complex datasets with clear separations between classes. It used in many applications such as image recognition, medical diagnosis, and text analytics.
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It is a data-classification algorithm that is based on probability analysis. The term probability is often associated with the term event. So you often hear statements along these lines: "The probability of Event X is so and so."
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What is the likelihood that Dennis Trankillo will buy product X?
What is the likelihood that Julie Ann San Juan will buy product X?
What is the likelihood that David Likoka will buy product X?
What is the likelihood that Andrea Patag will buy product X?
What is the likelihood that Juancho Chongkeko will buy product X?
What is the likelihood that Barbie Lafot will buy product X?
Naive Bayes classification algorithm is based ____ and ____?
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