What are the three characteristics of Big Data, and what are the main considerations in processing Big Data? Explain the differences between BI and Data Science. Briefly describe each of the four classifications of Big Data structure types. (i.e. Structured to Unstructured) List and briefly describe each of the phases in the Data Analytics Lifecycle. In which phase would the team expect to invest most of the project time? Why? Where would the team expect to spend the least time? Which R command would create a scatterplot for the dataframe “df”, assuming df contains values for x and y? What is a rug plot used for in a density plot? What is a type I error? What is a type II error? Is one always more serious than the other? Why? Why do we consider K-means clustering as a unsupervised machine learning algorithm? Detail the four steps in the K-means clustering algorithm. List three popular use cases of the Association Rules mining algorithms. Define Support and Confidence How do you use a “hold-out” dataset to evaluate the effectiveness of the rules generated? List two use cases of linear regression models. Compare and contrast linear and logistic regression methods.

The three characteristics of Big Data are volume, velocity, and variety.

Volume refers to the large amount of data that is generated and collected. Traditional data processing methods are not capable of handling such large volumes of data. Velocity refers to the speed at which data is being generated and needs to be processed. With the increasing use of internet and mobile devices, data is being generated at an unprecedented rate. Variety refers to the different types of data that are being generated, including structured, semi-structured, and unstructured data. This includes data from social media, sensor data, text data, audio data, and video data.

Processing Big Data involves several considerations. The first consideration is scalability. Since Big Data involves large volumes of data, the processing systems need to be able to scale up or down depending on the requirements. They need to be able to handle the increasing volumes of data without sacrificing performance. The second consideration is speed. Big Data processing systems need to be able to process data quickly in order to derive insights in a timely manner. The third consideration is fault tolerance. With large volumes of data, there is a higher likelihood of failures occurring. Big Data processing systems need to be able to handle and recover from such failures without losing any data. The fourth consideration is data integration. Big Data often comes from various sources and in different formats. Data integration involves combining and transforming this data to make it usable for analysis. The fifth consideration is data privacy and security. With Big Data, there is a need to ensure the privacy and security of the data being processed.

BI, or Business Intelligence, and Data Science are two closely related but distinct fields. BI refers to the use of data to analyze and understand business performance, make informed decisions, and optimize business processes. It involves the use of tools and techniques to aggregate, integrate, and analyze data from various sources in order to provide insights and support decision-making. BI typically focuses on historical data and uses descriptive and diagnostic analytics to understand what happened and why it happened.

Data Science, on the other hand, involves the use of scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. It involves a combination of statistics, mathematics, computer science, and domain knowledge to understand and make predictions or recommendations based on data. Data Science uses a wide range of techniques, including predictive analytics, machine learning, and data visualization, to analyze data and make informed decisions.

In terms of the four classifications of Big Data structure types, they are structured, semi-structured, unstructured, and complex structured. Structured data refers to data that is organized in a consistent format, such as a relational database. Semi-structured data refers to data that has a basic structure but is not fully organized, such as XML or JSON files. Unstructured data refers to data that does not have a predefined structure, such as text documents, emails, or social media posts. Complex structured data refers to data that has multiple layers of structure, such as graphs or networks.

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