DS&A

Created by Simaque Hibath Rafeek

p.16

What are Ad Hoc Reports?

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p.16

Customized reports useful for obtaining specific data required by business users.

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p.16
Data Analysis Techniques

What are Ad Hoc Reports?

Customized reports useful for obtaining specific data required by business users.

p.19
Data Analysis Techniques

What is the focus of inferential statistics?

To compare differences between two groups of buyers.

p.8
Data-Driven Decision Making

What does data science support?

Reasoning and decision making under various sources of uncertainty.

p.18
Data-Driven Decision Making

What is an alert?

An automated message or notification indicating that a predefined event or error condition has occurred.

p.11
Management and Processing of Data

What is the foundation of data analytics?

Having appropriate data that are of sufficient quality and appropriately organized for the analysis task.

p.16
Data Analysis Techniques

How can business users generate their own reports?

By modifying standard report queries or filter values to get real-time data on demand.

p.16
Data Analysis Techniques

What is another example of a custom report?

A report describing the number of hospital patients for every diagnosis code for each day of the week.

p.1
Introduction to Data Science and Analytics

What is the primary focus of the course IT3080?

Introduction to Data Science and Analytics.

p.20
Strategic vs Operational Decision Making

How does business forecasting benefit a company?

It allows the company to make long-term plans and prepare for changes in the market.

p.22
Predictive Modeling and Optimization

How does optimization differ from other statistical analyses?

It provides the actual answer rather than just driving exclusions.

p.15
Data-Driven Decision Making

Are standard reports useful for long-term decision making?

They are useful to some extent, but not for making long-term decisions.

p.19
Data Analysis Techniques

How can business experts utilize statistics?

To solve their problems.

p.20
Predictive Modeling and Optimization

What is an example of business forecasting?

Time series forecasting sales revenue in the future.

p.15
Data-Driven Decision Making

What is an example of a standard report?

Monthly or quarterly financial reports.

p.19
Data Analysis Techniques

What is the purpose of statistical analysis?

To analyze and interpret numerical data for making inferences about a population from sample data.

p.22
Predictive Modeling and Optimization

What is the primary purpose of optimization in business analysis?

To identify the best possible action for a situation.

p.5
Data Creation Statistics

What can businesses identify by analyzing user data?

Current and potential audience, emerging trends, and how audiences interact with content.

p.8
Management and Processing of Data

What is the focus of the management aspect of data science?

The management and processing of data.

p.7
Strategic vs Operational Decision Making

What type of decisions consider the entire organization?

Strategic decision making.

p.7
Strategic vs Operational Decision Making

What should organizations consider when making plans?

What plans should we make?

p.7
Strategic vs Operational Decision Making

What action needs to be taken in operational decision making?

What action needs to be taken?

p.13
Data Analysis Techniques

What technique is used to predict future trends based on historical data?

Forecasting.

p.9
Data Analytics Techniques Comparison

What are some techniques included in data analytics?

Data mining, text analytics, machine learning, statistical learning, and mathematical optimization.

p.20
Predictive Modeling and Optimization

What is business forecasting?

A projection of a business’s future developments based on trends, patterns, and current and historical data analysis.

p.5
Data Creation Statistics

Why is understanding user-created data useful for business organizations?

It helps in identifying current and potential audiences, emerging trends, audience interactions, and customer sentiments.

p.17
Data Analysis Techniques

What is Online Analytical Processing (OLAP)?

A technology that performs multidimensional analysis at high speeds on large volumes of data.

p.12
Management and Processing of Data

What is the first common data processing task?

Data assessment and cleaning.

p.7
Strategic vs Operational Decision Making

What is a key question in strategic decision making?

What is the trend?

p.4
Data Creation Statistics

How many posts do Facebook users like every minute?

More than 4 million.

p.13
Data Analysis Techniques

What does OLAP stand for in data analysis?

Online Analytical Processing.

p.13
Data Analysis Techniques

What type of analysis involves interpreting data to find patterns?

Statistical Analysis.

p.3
Data Analytics Techniques Comparison

What is the importance of comparing different data analytics techniques?

To choose the most suitable method for specific data analysis needs.

p.21
Predictive Modeling and Optimization

Name a technique used in predictive modeling.

Association Rule Mining.

p.6
Data-Driven Decision Making

Why can human decision makers make mistakes?

Because they cannot oversee everything happening within the business.

p.8
Introduction to Data Science and Analytics

What is data science?

A multidisciplinary field that deals with technologies, processes, and systems to extract knowledge and insight from data.

p.5
Data Creation Statistics

How can understanding user data improve a business?

By enhancing business potential, expanding the customer base, and refining marketing strategies.

p.15
Data-Driven Decision Making

What is another example of a standard report besides financial reports?

Monthly sales reports.

p.14
Techniques for Deriving Business Value from Data

What role does information play in creating business value?

Information is processed data that provides context and relevance for decision-making.

p.7
Strategic vs Operational Decision Making

What type of decisions relate to daily operations?

Operational decision making.

p.7
Strategic vs Operational Decision Making

What is a key question in operational decision making?

What’s happening right now?

p.13
Data Analysis Techniques

What is predictive analysis used for?

To forecast outcomes based on data patterns.

p.21
Predictive Modeling and Optimization

What is another technique used in predictive analysis?

Classification.

p.6
Data-Driven Decision Making

What is a common issue in decision making within business organizations?

Decisions often occur based on instincts or gut feeling.

p.16
Data Analysis Techniques

Give an example of an Ad Hoc Report.

A report showing how many items were sold over a certain period.

p.22
Predictive Modeling and Optimization

What does optimization focus on in decision making?

Discovering the optimal suggestion for a process.

p.18
Data-Driven Decision Making

What is the primary purpose of alerts?

To allow users to receive critical business information quickly and efficiently.

p.12
Management and Processing of Data

What is the third common data processing task?

Data transformation.

p.3
Techniques for Deriving Business Value from Data

What is one technique used to derive business value from data?

Predictive analytics, which forecasts future trends based on historical data.

p.2
Data-Driven Decision Making

What is data-driven decision making?

A process of making decisions based on data analysis and interpretation.

p.2
Management and Processing of Data

What is involved in the management and processing of data?

Organizing, storing, and maintaining data for effective use.

p.9
Techniques for Deriving Business Value from Data

What role does visualization play in data science?

It helps in reporting and understanding data insights effectively.

p.11
Management and Processing of Data

Why is data preparation necessary before analytics?

Because data seldom arrive in a suitable state and need to be transformed into a proper form for analysis.

p.11
Management and Processing of Data

What should be done prior to data analytics?

Data should be specially prepared for the task of analytics.

p.19
Data Analysis Techniques

What does descriptive statistics help identify?

Mean, median, and mode of sales in a month.

p.6
Data-Driven Decision Making

What is the benefit of data-driven decision making?

It helps make business decisions backed up by analyzed data.

p.19
Data Analysis Techniques

What does causal analysis aim to identify?

Factors affecting a person's decision to go for digital banking.

p.15
Data-Driven Decision Making

What do standard reports typically describe?

They describe just 'what happened' in a particular area.

p.17
Data Analysis Techniques

What capability does OLAP provide to users?

The ability to analyze multidimensional data interactively from multiple perspectives.

p.5
Data Creation Statistics

What insights can businesses gain about active customers from user data?

Understanding what active customers think and feel about the brand.

p.8
Data Analysis Techniques

What does the analytical aspect of data science involve?

Analytical methods and theories for data analysis and optimization.

p.17
Data Analysis Techniques

Give an example of a query that can be performed using OLAP.

Find the number of laptops sold in Q1 in Australia.

p.4
Data Creation Statistics

What percentage of the data on the internet has been created since 2016?

90%.

p.14
Techniques for Deriving Business Value from Data

What is the significance of wisdom in business intelligence?

Wisdom involves applying knowledge to make sound decisions that create long-term value.

p.10
Introduction to Data Science and Analytics

What is data science?

A field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

p.3
Types of Data and Representation Methods

What are the different types of data?

Qualitative, quantitative, structured, and unstructured data.

p.4
Data Creation Statistics

How many tweets are posted per day?

656 million tweets.

p.8
Management and Processing of Data

What are the two main aspects of data science?

Management and processing of data, and analytical methods and theories for data analysis and optimization.

p.14
Techniques for Deriving Business Value from Data

What are the levels of intelligence that contribute to creating business value?

The levels include data, information, knowledge, and wisdom.

p.12
Management and Processing of Data

What is the second common data processing task?

Data integration.

p.13
Data Analysis Techniques

What are the two types of reports used in data analysis?

Standard and Ad-hoc reports.

p.10
Data Analysis Techniques

What is the primary goal of data analytics?

To analyze data to make informed decisions and derive actionable insights.

p.4
Data Creation Statistics

What is the trend in data creation by businesses and users?

They produce massive amounts of data every day.

p.9
Data Analytics Techniques Comparison

What does the second aspect of data science and analytics involve?

Data analytics, including data mining, text analytics, machine and statistical learning, mathematical optimization, and visualization.

p.21
Predictive Modeling and Optimization

Which predictive modeling technique involves estimating relationships among variables?

Regression.

p.17
Data Analysis Techniques

What is the key mechanism that allows OLAP to achieve high performance?

The use of aggregations.

p.17
Data Analysis Techniques

What is another example of a query that can be performed using OLAP?

Find the revenue from selling TVs from all continents in Q2.

p.3
Data-Driven Decision Making

What is a key benefit of data-driven decision making?

It allows for more informed and objective decisions based on data analysis.

p.4
Data Creation Statistics

How many pictures do Instagram users post every minute?

46,740 pictures.

p.3
Types of Data and Representation Methods

How can data types be represented?

Using charts, graphs, tables, and other visualization methods.

p.10
Techniques for Deriving Business Value from Data

How does data science contribute to business value?

By enabling data-driven decision making and optimizing processes.

p.21
Predictive Modeling and Optimization

What is the purpose of predictive analysis?

To make predictions of future events based on current and past data.

p.18
Data-Driven Decision Making

How are alerts typically delivered?

Via email and other communication methods.

p.18
Data-Driven Decision Making

Give an example of how alerts can be used in a business context.

A store manager can be automatically informed when in-stock levels of critical items fall below or rise above a certain threshold.

p.13
Data Analysis Techniques

What is the purpose of alerts in data analysis?

To notify users of significant changes or events.

p.2
Introduction to Data Science and Analytics

What does data science and analytics involve?

The study and application of methods to analyze and interpret complex data.

p.2
Techniques for Deriving Business Value from Data

What are techniques to derive business values from data?

Methods used to extract insights and actionable information from data to enhance business performance.

p.21
Predictive Modeling and Optimization

What is a technique in predictive modeling that mimics the human brain?

Neural Networks.

p.14
Techniques for Deriving Business Value from Data

How does data contribute to business value?

Data serves as the raw material for generating insights and making informed decisions.

p.14
Techniques for Deriving Business Value from Data

How is knowledge defined in the context of business intelligence?

Knowledge is the understanding and awareness gained from analyzing information.

p.10
Types of Data and Representation Methods

What types of data can data science work with?

Both structured and unstructured data.

p.9
Management and Processing of Data

What is the first aspect of data science and analytics?

Data systems such as databases and warehousing, including data cleaning, engineering, monitoring, reporting, and visualization.

p.9
Management and Processing of Data

What is the purpose of data cleaning in data systems?

To prepare data for analysis by removing inaccuracies and inconsistencies.

p.10
Data Analytics Techniques Comparison

What are some common techniques used in data science?

Statistical analysis, machine learning, data mining, and predictive modeling.

p.10
Data Analytics Techniques Comparison

What is the role of algorithms in data science?

To process data and extract meaningful patterns and insights.

p.3
Data Analytics Techniques Comparison

What is a common method to compare data analytics techniques?

Evaluating their effectiveness, accuracy, and applicability to specific business problems.

p.13
Data Analysis Techniques

What does optimization in data analysis aim to achieve?

To find the best solution or outcome from a set of options.

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