Technology Values in Accounting Unit 1

Exploratory data analysis (EDA) is:

Focused on using historical financial data to make predictions and forecast about future outcomes

The initial exploration of financial data to understand its characteristics, distributions, and relationships

Focused on summarizing and describing historical financial data

About discovering patterns, relationships, and insights from large volumes of financial data

The initial exploration of financial data to understand its characteristics, distributions, and relationships

Financial data possesses several dimensions or characteristics that define its nature and influence the way it is analyzed and interpreted . The heterogeneity dimension:

Refers to the data formats and organization, as well as as to whether it is structured or unstructured

Refers to the source of the data and its quality, consistency, and reliability

Refers to the correctness and precisions of the data

Poses challenges in data integration, normalization and conversion

Poses challenges in data integration, normalization and conversion

Technique used in statistical analysis include:

Hypothesis testing, regression, analysis, correlation analysis, and time series analysis

Data profiling, cleaning, transformation, and visualization.

Charts, graphs, and other visual elements to presents complex data in an intuitive and understandable manner

Data visualization, summarization and aggregation

Hypothesis testing, regression, analysis, correlation analysis, and time series analysis

The "IF" function in excel takes two arguments: a condition and value to return if the condition and a value to return if the condition is false.

True

False

False

Primary keys must only be:

Non-missing for the records they identify

Numbers

Unique to and non-missing for the records they identify

Unique to the records they identify

Unique to and non-missing for the records they identify

Which of the following best describes the purpose of a primary key?

To provide information about an observation

To identify the observation represented by each row in the data

To provide information about the table in which the observation is stored

To create a relationship between two tables

To identify the observation represented by each row in the data

Data cleaning can involve:

Outlier detection: identifying and handling outliers that might skew analysis

Standardization: converting financial data into a common format or unit to enable consistent comparisons and calculations.

Data and time formatting ensuring consistent data and data for accurate analysis and visualization

Derived attributes: creating new attributes or variables based on existing

Outlier detection: identifying and handling outliers that might skew analysis

Unstructured data lacks a predefined format and can include text, images, email, or other forms of information.

True

False

True

Data cleaning can involve:

Calculations and derivation: performing calculations and deriving new financials metrics from existing data

Duplicate removal: identifying and eliminating duplicate records to avoid double-counting or erroneous analysis

Filtering: applying filters or conditions to include or excluded specific data based on predefined criteria or business rules

Data reconciliation: performing reconciliation processes to ensure the accuracy and consistency of financials transaction across different systems or data sources

Duplicate removal: identifying and eliminating duplicate records to avoid double-counting or erroneous analysis

Techniques used in business intelligence include:

Data warehousing, modeling, dashboards and key performance indicators (KPIs).

Hypothesis testing, regression analysis, correlation analysis, and time series analysis.

Clustering, classification, association analysis, and anomaly detection.

Regression analysis, time series forecasting, machine learning algorithms, and predictive modeling.

Data warehousing, modeling, dashboards and key performance indicators (KPIs).

Techniques used in descriptive analytics include:

Data visualization, summarization and aggregation.

Data profiling, cleaning, transformation, and visualization.

Hypothesis testing, regression analysis, correlation analysis, and time series analysis.

Regression analysis, time series forecasting, machine learning algorithms, and predictive modeling.

Data visualization, summarization and aggregation.

A database is:

A structured collection of data that is organized in a way that enables efficient storage, retrieval, and manipulation of data.

A digital ledger technology that is used to store and record transactions in a secure and decentralized manner.

An essential tool in data analytics for communicating complex data in a way that is easy to understand and interpret

A central repository that can be used to store and manage large volumes of data from multiple sources.

A central repository that can be used to store and manage large volumes of data from multiple sources.

Normalization is:

Analyzing historical data to understand what happened in the past.

The process of organizing data in a database to minimize redundancy.

The accuracy and consistency of data over its entire lifecycle.

A technique use to link data between parts of a spreadsheet or database.

The process of organizing data in a database to minimize redundancy.

When using Excel to split the data in a column, such as when you have values separated by commas, the following Excel function is most useful:

Text to Columns, or the same functions accessed through the Import Wizard

CONCAT

Relationships

VLOOKUP

Text to Columns, or the same functions accessed through the Import Wizard

The main goal of predictive analytics is to identify patterns and trends in the data that can be used to make predictions about future events or behaviors.

True

False

True

Data standardization involves:

Enhancing financial data by integrating external data sources, such as market data, economic indicators, or industry benchmarks.

Converting financial data into a common format or unit to enable consistent comparisons and calculations.

Summarizing data by aggregating values at various levels such as time periods and organizations.

Applying conditions to include or exclude specific data based on predefined criteria or business rules.

Converting financial data into a common format or unit to enable consistent comparisons and calculations

In Excel, the formula IF(C2=1,"Yes","No") will return "No" if the value in cell C2 is the text string "1".

False

True

True

Primary keys must only be:

Unique to the records they identify.

Numbers.

Unique to and non-missing for the records they identify.

Non-missing for the records they identify.

Unique to and non-missing for the records they identify.

The ETL process is:

a set of procedures used to extract data from various sources, transform it into a consistent format, and load it into a target system or data warehouse for further analysis and reporting.

the initial exploration of financial data to understand its characteristics, distributions, and relationships.

focused on summarizing and describing historical financial data.

Using optimization algorithms and decision-making tools to identify the best course of action based on available data.

a set of procedures used to extract data from various sources, transform it into a consistent format, and load it into a target system or data warehouse for further analysis and reporting.

A key aspect of data mining in structured financial data is:

Data reconciliation: performing reconciliation processes to ensure the accuracy and consistency of financial transactions across different systems or data sources.

External data integration: enhancing financial data by integrating external data sources, such as market data, economic indicators or industry benchmarks.

Data exploration: exploring the data to understand its characteristics, identify relevant variables and gain insights into the data's distribution.

Date and time formatting: ensuring consistent date and time formats across financial data for accurate analysis and visualization.

Data exploration: exploring the data to understand its characteristics, identify relevant variables and gain insights into the data's distribution.

Blockchain is a digital ledger technology that is used to store and record transactions in a secure and centralized manner.

False

True

False

It is often just as easy to analyze data that is not standardized or consistent across sources as data that is made to be standardized and consistent across sources.

False

True

False

In the ETL process, the E represents:

Loading data into a target system or data warehouse for storage and analysis.

Extracting financial data from various source systems.

Transforming data to ensure consistency, accuracy, and compatibility across different sources.

A set of procedures used to extract data from various sources, transform it into a consistent format, and load it into a target system or data warehouse for further analysis and reporting.

Extracting financial data from various source systems.

Filtering involves:

Identifying and handling missing values in financial data by imputing them using statistical techniques or considering alternative data sources.

Combining data from multiple sources or tables based on common identifiers to create a unified dataset.

Sorting the data and manually adding subtotals.

Applying conditions to include or exclude specific data based on predefined criteria or business rules.

Applying conditions to include or exclude specific data based on predefined criteria or business rules.

Data mining is:

About discovering patterns, relationships and insights from large volumes of financial data.

The graphical representation of financial data to visually communicate insights and patterns.

Focused on summarizing and describing historical financial data.

The use of technologies, tools, and processes to analyze and present financial data for strategic decision-making.

About discovering patterns, relationships and insights from large volumes of financial data.

In the context of data ownership, data privacy refers to:

The technical aspects of data management.

The legal rights and control that an individual or organization has over data.

The responsible management and governance of data within an organization.

The protection of personal or sensitive data and the rights of individuals regarding the collection, use, and disclosure of their data.

The protection of personal or sensitive data and the rights of individuals regarding the collection, use, and disclosure of their data.

VLOOKUP has four arguments. The first argument specifies:

The value you are interested in matching.

The column in your range, from which you want to retrieve a value.

The range of values you want to include in your search.

Whether you need to exactly match the value.

The value you are interested in matching.

Techniques used in data mining include:

Data warehousing, modeling, dashboards and key performance indicators (KPIs).

Charts, graphs and other visual elements to present complex data in an intuitive and understandable manner.

Clustering, classification, association analysis, and anomaly detection.

Data visualization, summarization and aggregation.

Clustering, classification, association analysis, and anomaly detection.

Select * FROM Customers;

In the above SQL query, SELECT is:

A statement indicating that the user wants to retrieve data from the database

The source table for the query

The attributes (columns or fields of the source table) retrieved by the query

A statement indicating that the user wants to retrieve data from the database

In Excel, the formula IF(C2=1,"Yes","No") will return "No" if the value in cell C2 is the text string "1".

False

True

True

In Excel, you will more often refer to data by location than by name or its characteristics.

False

True

True

Techniques used in data visualization include:

Charts, graphs and other visual elements to present complex data in an intuitive and understandable manner.

Data warehousing, modeling, dashboards and key performance indicators (KPIs).

Regression analysis, time series forecasting, machine learning algorithms, and predictive modeling.

Clustering, classification, association analysis, and anomaly detection.

Charts, graphs and other visual elements to present complex data in an intuitive and understandable manner.

To determine the target audience and scope of an analysis, specific details about the context or purpose are not needed.

False

True

False

Blockchain technology has a number of important features, NOT including:

Immutability: Once a block is added to the blockchain, it cannot be altered or deleted, which ensures the integrity and immutability of the data.

Decentralization: Blockchain networks are not controlled by any single entity, which makes them more resilient to attacks and less susceptible to fraud or corruption.

Opacity: All transactions on the blockchain are invisible to all participants, which makes it more difficult to engage in fraudulent or malicious activity.

Security: The cryptographic algorithms used in blockchain networks make it difficult for anyone to tamper with the data or steal information.

Opacity: All transactions on the blockchain are invisible to all participants, which makes it more difficult to engage in fraudulent or malicious activity.

For most data analysis questions, we are most interested in what typically happens in a given situation, rather than what can happen in a single instance.

False

True

True

VLOOKUP has four arguments. The first argument specifies:

The range of values you want to include in your search.

The column in your range, from which you want to retrieve a value.

Whether you need to exactly match the value.

The value you are interested in matching.

The value you are interested in matching.

An inner join of datasets X and Y will combine:

All matching observations between X and Y.

All observations from X and matching observations from Y, with missing values inserted as necessary.

All observations from X and all observations from Y, with missing values inserted as necessary.

All observations from Y and matching observations from X, with missing values inserted as necessary.

All matching observations between X and Y.

When using Excel to split the data in a column, such as when you have values separated by commas, the following Excel function is most useful:

Relationships

CONCAT

VLOOKUP

Text to Columns, or the same functions accessed through the Import Wizard

Text to Columns, or the same functions accessed through the Import Wizard

Descriptive analytics is:

Focused on using historical financial data to make predictions and forecasts about future outcomes.

The initial exploration of financial data to understand its characteristics, distributions, and relationships.

About discovering patterns, relationships and insights from large volumes of financial data.

Focused on summarizing and describing historical financial data.

Focused on summarizing and describing historical financial data.

Descriptive analytics involves:

Analyzing historical data to understand what happened in the past.

Using statistical models and machine learning algorithms to analyze historical data and predict future outcomes.

Using optimization algorithms and decision-making tools to identify the best course of action based on available data.

Analyzing historical data to understand what happened in the past.

Financial data from multiple sources may need to be harmonized and standardized to ensure consistency and comparability.

True

False

True

Which of the following best describes the purpose of a primary key?

To provide information about the table in which the observation is stored.

To identify the observation represented by each row in the data.

To create a relationship between two tables.

To provide information about an observation.

To identify the observation represented by each row in the data.