Business Data Analytics(Practical Paper)

The statement describes the typical process of developing data models, which includes progressing from a conceptual data model to a logical data model and, finally, to a physical data model for implementation with a specific database provider.

Data science encompasses a wide range of activities related to working with data, making it a comprehensive term.

It includes data analysis, statistical inference, predictive modeling, and other data-related tasks.

Data science is the most appropriate choice because it covers the entire field, making it the best fit for the diagram.

Data Analysis (A) - Why it's not as suitable:

Data analysis is an essential component of data science, but it represents only one aspect of the broader field.

The diagram implies a more comprehensive term is needed to describe all activities, not just data analysis.

Statistical Inference (C) - Why it's not as suitable:

Statistical inference is a valuable part of data science, particularly in drawing conclusions from data.

However, data science involves many other activities beyond statistical inference, such as data cleaning, machine learning, and more.

Predictive Modeling (D) - Why it's not as suitable:

Predictive modeling is a significant element within data science, especially for making predictions based on data.

Nonetheless, data science encompasses a broader spectrum of tasks beyond just predictive modeling, such as data exploration and feature engineering.

Data modeling in the context of CRISP-DM typically refers to the process of designing and creating models that represent the relationships between data points and structures in order to better understand and analyze the data. This can include various types of models, such as entity-relationship diagrams, data flow diagrams, or more complex machine learning models, depending on the specific data mining project.

(i) Data mining does indeed relate to the process of turning raw data into useful information. Data mining involves various techniques and algorithms to discover patterns, relationships, or insights within large datasets.

(ii) The second statement is not true. Data mining using built-in algorithms does not guarantee a specific result. The outcome of data mining depends on the data, the algorithms used, and the specific goals of the analysis. While data mining can provide valuable insights, it doesn't guarantee a particular outcome because the results are dependent on the nature of the data and the algorithms applied.

The "Vs" of data—Volume, Velocity, Variety, Veracity, and Value—are used to describe different characteristics or aspects of data in the context of big data and data analytics. Among these, the "Variety" refers to the diversity and heterogeneity of data types, including structured, semi-structured, and unstructured data. Data with multifactor, unstructured, and dynamic attributes falls under the category of data variety.

Discrete data consists of distinct, separate values that are typically counted and often involve whole numbers. The number of children is an example of discrete data because it represents a count of individual items (children) and takes on distinct, separate values (0, 1, 2, 3, ...). This is different from continuous data, which can take on any value within a range (like height or test scores).

Ms Dare Mongare is using predictive analytics for risk management, which involves analyzing historical data and trends to make predictions about future risks that the company may face.

She is also using prescriptive analytics for cash budgeting, which goes beyond prediction and suggests specific actions or recommendations. In this case, scenario analysis is a form of prescriptive analytics, as it explores different future scenarios and provides guidance on how to handle them.

The principle of Fairness in the Unified Ethical Frame for Big Data Analytics involves considering the potential impacts of data use on all interested parties and ensuring that the use of data does not result in unfair discrimination, bias, or harm to any specific group or individual. It emphasizes the need to treat all stakeholders fairly and equitably when collecting and analyzing data. Option A aligns with this principle by emphasizing the importance of considering the impacts on all parties involved.

SQL (Structured Query Language) is a programming language used for managing and querying relational databases. It is not a cloud computing platform or service in itself. While SQL databases can be hosted on cloud platforms like Azure (A) and AWS (B), SQL itself is not a cloud computing application or service.

Options A, B, and D (Azure, AWS, and Alibaba Cloud) are all examples of cloud computing platforms or services that provide infrastructure, storage, and various other cloud-based solutions.

In data science and database modeling, the terms "binary", "quaternary" and "unary" are used to describe relationships between entities:

A "binary" relationship is between two entities.

A "quaternary" relationship involves four entities.

A "unary" relationship involves only one entity.

These terms are used to specify the arity or number of entities involved in a relationship within a data model or database schema. However, binary relationships are much more common and widely used in practice compared to quaternary and unary relationships.

A bubble chart is a type of data visualization that can effectively present relationships involving more than two variables. It extends the concept of a scatter plot (option A) by adding a third variable that is represented by the size of the bubbles. In a bubble chart, each data point is represented by a bubble, and the position of the bubble on the X and Y axes represents two variables, while the size of the bubble represents a third variable. This allows for the visualization of relationships among three variables simultaneously.

Options C (Column Chart) and D (Line Chart) are useful for presenting data involving one or two variables, but they are not as effective for showing relationships involving more than two variables.

A line graph displays information as a series of data points connected by straight line segments. It is commonly used to represent trends or changes over time by connecting data points along the X and Y axes, showing the progression of values and facilitating the visualization of patterns and fluctuations.

According to data protection laws like the General Data Protection Regulation (GDPR) in the European Union, in the case of a data breach, the organization should:

C. Notify the supervisory authority within 72 hours of the incident whether or not it poses a risk to the organization and affected individuals.

This means that organizations are generally required to report data breaches to the relevant supervisory authority within 72 hours of becoming aware of the breach, regardless of whether it poses a risk to the organization or affected individuals. Additionally, in some cases, the affected individuals may also need to be notified depending on the severity and impact of the breach.

Data Dredging (also known as data fishing or p-hacking) is a term used to describe the process of analyzing data in a way that involves testing multiple hypotheses without a priori reasoning or a strong theoretical basis. This practice can lead to the discovery of seemingly significant relationships or patterns purely by chance, rather than because they are genuinely meaningful or representative of the underlying data

Data Bagging: Data bagging, also known as bootstrap aggregating or bagging, is a statistical technique used in machine learning to improve the accuracy and stability of a predictive model. It involves taking multiple random samples (with replacement) from the original dataset and training multiple models on these samples. The final prediction is then often based on the majority vote (for classification problems) or averaging (for regression problems) of the predictions made by these models.

Data Merging: Data merging is a process in data management and analysis where two or more datasets are combined into a single dataset. This is typically done when the datasets share common variables, and the goal is to create a unified dataset that contains all the relevant information from the original sources.

Data Pooling: Data pooling refers to the practice of aggregating data from multiple sources or participants into a single dataset for analysis. It is often used in research or business analytics to increase the sample size and improve the statistical power of the analysis. Data pooling can help uncover trends or insights that might not be apparent in smaller individual datasets.

The data analyst should be less skeptical about the data analysis itself. Once the data has been collected and properly prepared, the analysis techniques and models used should be based on established principles and methodologies. While critical evaluation is important, once the analysis techniques have been chosen and applied correctly, the results and findings are based on those techniques and should be accepted within the context of their proper application.

Data protection laws generally emphasize minimizing data collection and processing to the extent necessary for the stated purposes. The principle is typically to collect and process only the data that is relevant and necessary for the legitimate purposes and to limit excessive data collection.

The other options are in line with common principles of data protection:

Process data lawfully: Data protection laws require that personal data be collected and processed in a lawful and transparent manner, often with the need for consent or a legitimate legal basis.

Ensure data quality: Data protection laws typically require that personal data is accurate and up to date. Organizations are often responsible for ensuring the quality of data they collect and process.

Limit data processing: Data protection laws often impose limitations on the purposes for which data can be processed. Data should only be processed for specified, explicit, and legitimate purposes.

Implementing a database management system (DBMS).
A database management system is a software application that manages and organizes data, making it more accessible and efficient to retrieve. It helps optimize data storage, retrieval, and processing, which can improve data access speed. Furthermore, a well-designed DBMS can also help in implementing data access controls and restrictions, which addresses the issue of providing or restricting access to specific employees or data as needed for security and compliance purposes.

Git is an open-source distributed version control system that is widely used for tracking changes in source code during software development. It allows multiple developers to collaborate on a project while keeping track of revisions and changes to the codebase. Numpy and Scipy are libraries for scientific computing in Python, and Loft does not appear to be a known version control system.

Alteryx is primarily known as a data management and analytics platform that provides a range of data integration, data cleaning, data preparation, and data transformation capabilities. It allows users to manipulate and prepare data for analysis and reporting, making it a data management tool. While it may be used in conjunction with data visualization and presentation tools, its primary focus is on data management and data preparation.

This formula will calculate the sum of the values in cells A1 through A20, providing a subtotal for the variables listed in a vertical range.