Podcast
Questions and Answers
Which phase of decision-making involves specifying various courses of action to address a problem?
Which phase of decision-making involves specifying various courses of action to address a problem?
- Choice
- Design (correct)
- Intelligence
- Review
Which of the following best describes 'bounded rationality' in decision-making?
Which of the following best describes 'bounded rationality' in decision-making?
- The idea that rationality is limited by the tractability of the decision, cognitive limitations of the mind and time available to make the decision. (correct)
- Ignoring available information and making quick decisions based on intuition.
- Making decisions based solely on emotional factors rather than logical reasoning.
- Always selecting the option that provides the maximum possible benefit, regardless of cost.
What is the primary goal of 'prescriptive data analytics'?
What is the primary goal of 'prescriptive data analytics'?
- Summarizing historical data to gain insights.
- Creating visualizations of data for easier understanding.
- Identifying the best course of action given known parameters. (correct)
- Predicting future outcomes based on past data.
Which component of Business Intelligence (BI) involves transforming raw data into actionable insights?
Which component of Business Intelligence (BI) involves transforming raw data into actionable insights?
What kind of insight does data science help to extract?
What kind of insight does data science help to extract?
Which type of 'unknown' refers to something we don't know, but we are aware that we don't know it?
Which type of 'unknown' refers to something we don't know, but we are aware that we don't know it?
What is the primary function of a dashboard in an organization?
What is the primary function of a dashboard in an organization?
What does 'data mashup' primarily involve?
What does 'data mashup' primarily involve?
Why is data visualization important in data analytics?
Why is data visualization important in data analytics?
What is the main purpose of 'self-service analytics'?
What is the main purpose of 'self-service analytics'?
What is involved in 'embedded BI'?
What is involved in 'embedded BI'?
What is the role of augmented analytics?
What is the role of augmented analytics?
What is the ultimate goal of machine learning?
What is the ultimate goal of machine learning?
Which of the following is NOT one of the 'four Vs of big data'?
Which of the following is NOT one of the 'four Vs of big data'?
What does 'Veracity' refer to in context of the 'four Vs of Big Data'?
What does 'Veracity' refer to in context of the 'four Vs of Big Data'?
Which of the following is a tool used in descriptive analytics?
Which of the following is a tool used in descriptive analytics?
What is the primary purpose of data visualization in the context of assisting decision-makers?
What is the primary purpose of data visualization in the context of assisting decision-makers?
In data analytics, what does 'Drill down' commonly refer to?
In data analytics, what does 'Drill down' commonly refer to?
Which data analysis technique is used to discover co-occurrence relationships among activities performed by specific individuals or groups?
Which data analysis technique is used to discover co-occurrence relationships among activities performed by specific individuals or groups?
Which of the following best describes the process of 'Geocoding'?
Which of the following best describes the process of 'Geocoding'?
Flashcards
Intelligence Phase
Intelligence Phase
Identify the problem/opportunity, collect data, and set goals/assessment criteria.
Design Phase
Design Phase
Specify courses of action, analyze feasible alternatives, and evaluate each alternative.
Choice Phase
Choice Phase
Select an alternative course of action for execution.
Review Phase
Review Phase
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Bounded Rationality
Bounded Rationality
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Satisficing
Satisficing
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Optimizing
Optimizing
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Descriptive Analytics
Descriptive Analytics
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Predictive Analytics
Predictive Analytics
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Prescriptive Analytics
Prescriptive Analytics
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Business Intelligence (BI)
Business Intelligence (BI)
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Data Science
Data Science
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Known Unknown
Known Unknown
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Unknown Unknown
Unknown Unknown
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Dashboard
Dashboard
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Data Mashup
Data Mashup
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Data Visualization
Data Visualization
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Machine Learning
Machine Learning
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The Four Vs of Big Data
The Four Vs of Big Data
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Advanced Data Analytics
Advanced Data Analytics
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Study Notes
- Four phases of decision making:
- Intelligence: Identify problem/opportunity, collect info, establish a goal with assessment criteria.
- Design: Specify courses of action, analyze feasible alternatives, evaluate each against the established criteria.
- Choice: Select an alternative course of action.
- Review: Monitor and control the choice execution, return to any previous phase if needed.
- Bounded rationality: Rationality is limited by decision tractability, cognitive ability, and time available.
- Satisficing: Decision-making strategy searching available alternatives until an acceptable solution is found, from "satisfy" and "suffice."
- Optimizing: the process of finding a cost effective alternative that produces the best achievable performance under constraints by maximizing desired effects.
- Descriptive data analytics: Summarizes historical data for useful information, preparing data for future in-depth analysis.
- Predictive data analytics: Utilizes data analytics methods/techniques to model and make predictions about unknown events.
- Prescriptive data analytics: Finds the best course of action among choices, given known parameters.
- Business Intelligence (BI): Best practices, software, infrastructure, and tools transforming structured data into actionable insights for informed business decisions.
- Data Science: Multi-disciplinary field using expertise, methods, programming, algorithms and statistics to extract knowledge from big data sets to predict future trends.
- Known unknown: Something we don't know, and we know that we don't know it.
- Unknown unknown: Something we don't know, and we don't realize it.
- Known known: Something we know, and we know that we know it.
- Dashboard: Graphical user interface providing at-a-glance views of KPIs for an organization/department.
- Data mashup: Integration of two or more data sets from various business/external sources without ETL.
- Data visualization: Representing abstract business/scientific data as images/diagrams/graphs to aid understanding.
- Modern BI: Allows users to do product reports and analysis on the fly and share data with other users to make decisions and optimize business results.
- Self-service analytics: BI form enabling managers/users to perform queries and generate reports with nominal IT support.
- Embedded BI: Integrating self-service analytics tools/capabilities within commonly used software apps.
- Augmented analytics: Machine learning and AI in BI tools automating data preparation and insights sharing.
- Machine learning: Scientific algorithms identify patterns in big data to learn from the data and create insights.
- Data product: Technical function encapsulating an algorithm, designed for direct integration into core applications.
- Resilient distributed dataset (RDD): Fault-tolerant, immutable, distributed collection of objects that can be processed in parallel across a cluster.
- The four Vs of big data:
- Volume: Handle the sheer volume of big data, providing thorough analytics.
- Variety: Expand analytics from structured data warehouses to include semi-structured/unstructured sources.
- Velocity: The speed at which data is stored, analyzed, and reports are generated.
- Veracity: Repairing incomplete, missing, or duplicated data.
- Advanced data analytics: Examination of data using sophisticated methods/techniques to discover insights, predict, and generate recommendations.
- Tools used in Descriptive Analytics:
- Data mining: Software analyzes unstructured, semi-structured, and structured data to categorize and find correlations/patterns among fields.
- Data visualization: Presenting data graphically to help decision-makers grasp concepts or identify new patterns.
- Digital dashboard: Static/interactive electronic interface acquiring and consolidating data across an organization.
- Affinity analysis: Data mining technique that discovers co-occurrence relationships among activities performed by specific individuals or groups.
- Drill down: Searching for focused detailed info from general info, such as quarterly to monthly to daily sales.
- Geospatial data: Data with an explicit geographic component, ranging from vector/raster data to tabular data with site locations.
- Text mining: Deriving high-quality information from text using software to recognize concepts, patterns, topics, keywords, and attributes.
- Sentiment analysis: Uses natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract and quantify affective stages.
- Geographic information system (GIS): Tool capturing, storing, manipulating, analyzing, and visualizing geographic data on a map.
- Geocoding: Reads input text (like an address) and outputs latitude/longitude coordinates.
- Linear regression: Statistical method analyzing relationships between dependent and independent (explanatory) variables, where a simple regression has one explanatory variable while multiple regression has two or more.
- Time series regression: Model estimating a variable's trending direction over time.
- Trend: Data points that go up, down, or stay flat over time.
- Rate of change: Extent of relative change between data points over time.
- Cycles: Regularly repeating patterns in data, such as end-of-quarter sales patterns.
- Constant time series: A time series in which the mean value of the time series is constant over time.
- Trended time series: A time series where the means values can fluctuate by season.
- Decision optimization: Calculates variable values to lead to an event's optimal value.
- Rules-based decision-making: Helps novices make expert-like decisions.
- Cognitive computing: Uses machine-learning algorithms.
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