Analytics MIDTERM
1. Data Analytics: a comprehensive process to analyze data and produce outputs that can
inform decision-making
2. Describe the chain that leads from data to a decision Data
Information
Knowledge
Wisdsom
Decision: Data: raw numbers Information:
contextualized raw numbers knowledge:
patterns, trends, insights wisdom: breaking of
a knowledge shield
decision: leads to some action by managers
3. Great leaders ask great : Questions
4. Buisness Analytics: data analytics applied in the context of buisness
5. Examples of the application of analytics in buisness: marketing analytics, supply chain
analytics, HR analytics, healthcare analytics, financial analytics, sports analytics
6. Big Data: extremely large data sets that may be analyzed computationally to reveal patterns,
trends, and associations, especially relating to human behavior and interactions.; has 4
components: velocity, volume, veracity, variety
7. Describe the 4 V's of Big data: Volume-huge amount, need a better level of IT infrastructure to
store information that is much bigger than a laptop could hold
Variety-so many different types of data is collected in so many different forms, for example
image data, video data, canvas data, biometric data
Velocity-constantly coming in at such high volumes in specific directions that are hard to
keep up with
Veracity-the degree of uncertainty in the data that is used to make a decision; we must make
sense of the data even though there is veracity/uncertainty
8. Biometric Data: you fingerprints, skin cells, hair, and saliva
9. What are soft skills required for a data analyst?: understanding goals and problem solving,
analytical and critical thinking, presentation and communication skills, foundational
knowledge in some particular buisness field
10.What are hard skills required for a data analyst?: data visualization, excel, databases and
SQL, foundation of machine learning, knowledge in analytical and stat techniques
, Analytics MIDTERM
11.What is the evolution of buisness analytics?: From descriptive analytics, to predictive, and to
prescriptive analytics
12.What does descriptive analytics tell us?: what happened and what is hap- pening
13.What does prescriptive analytics tell us?: why should we do it
14.What does predictive analytics tell us?: what will happen and why will it happen
15.What form of analytics does hindsight match with? what are some exam- ples of hindsight?:
Descriptive analytics-dashboards, scorecards, data warehous- es
16.What form of analytics does insight match with? what are some examples of insight?: predictive
analytics-data mining, regression analysis, time series, haz- ard, discriminant
17.What form of analytics does foresight match with? what are some exam- ples of foresight?:
prescriptive analytics-optimization, simulation, decision model- ing
18. Descriptive analytics use
infrastructure needed tools
used
disadavantage: commonly known as buisness intelligence, used to create dash- boards/reports
to show past/current events, need data warehouses for them. Use SQL, PowerBI, Tableau, and
Qlik. It is disadvanatged in the fact that it does not explore root causes behind observed trends
and can also predict future outcomes, based on historical data analysis
19.Dashboards: a visual representation of company performance, use Key Perfor- mance
Indicators KPI, created with descriptive analytics
20.report: a collection of visualizations and contain much more detailed information than
dashboards, created with descriptive analytics
21.Buisness Intelligence (BI) is an alternative name for...: descriptive analytics
22.How does amazon use descriptive analytics?: they anticipate shopper pur- chase and cut
down on shipping time by starting the process of shipping products to users before they even
make a purchase, leveraging past data, behavioral information, and finding patterns.
23. Descriptive analytics gives insights
on... W ,W ,W , and H -
: what, when who, and how
24.Data Warehouses: central repository of info that can be analyzed to make more informed
decisions; data inflows into a warehouse from transactional systems, regional databases, etc. on
a regular cadence
1. Data Analytics: a comprehensive process to analyze data and produce outputs that can
inform decision-making
2. Describe the chain that leads from data to a decision Data
Information
Knowledge
Wisdsom
Decision: Data: raw numbers Information:
contextualized raw numbers knowledge:
patterns, trends, insights wisdom: breaking of
a knowledge shield
decision: leads to some action by managers
3. Great leaders ask great : Questions
4. Buisness Analytics: data analytics applied in the context of buisness
5. Examples of the application of analytics in buisness: marketing analytics, supply chain
analytics, HR analytics, healthcare analytics, financial analytics, sports analytics
6. Big Data: extremely large data sets that may be analyzed computationally to reveal patterns,
trends, and associations, especially relating to human behavior and interactions.; has 4
components: velocity, volume, veracity, variety
7. Describe the 4 V's of Big data: Volume-huge amount, need a better level of IT infrastructure to
store information that is much bigger than a laptop could hold
Variety-so many different types of data is collected in so many different forms, for example
image data, video data, canvas data, biometric data
Velocity-constantly coming in at such high volumes in specific directions that are hard to
keep up with
Veracity-the degree of uncertainty in the data that is used to make a decision; we must make
sense of the data even though there is veracity/uncertainty
8. Biometric Data: you fingerprints, skin cells, hair, and saliva
9. What are soft skills required for a data analyst?: understanding goals and problem solving,
analytical and critical thinking, presentation and communication skills, foundational
knowledge in some particular buisness field
10.What are hard skills required for a data analyst?: data visualization, excel, databases and
SQL, foundation of machine learning, knowledge in analytical and stat techniques
, Analytics MIDTERM
11.What is the evolution of buisness analytics?: From descriptive analytics, to predictive, and to
prescriptive analytics
12.What does descriptive analytics tell us?: what happened and what is hap- pening
13.What does prescriptive analytics tell us?: why should we do it
14.What does predictive analytics tell us?: what will happen and why will it happen
15.What form of analytics does hindsight match with? what are some exam- ples of hindsight?:
Descriptive analytics-dashboards, scorecards, data warehous- es
16.What form of analytics does insight match with? what are some examples of insight?: predictive
analytics-data mining, regression analysis, time series, haz- ard, discriminant
17.What form of analytics does foresight match with? what are some exam- ples of foresight?:
prescriptive analytics-optimization, simulation, decision model- ing
18. Descriptive analytics use
infrastructure needed tools
used
disadavantage: commonly known as buisness intelligence, used to create dash- boards/reports
to show past/current events, need data warehouses for them. Use SQL, PowerBI, Tableau, and
Qlik. It is disadvanatged in the fact that it does not explore root causes behind observed trends
and can also predict future outcomes, based on historical data analysis
19.Dashboards: a visual representation of company performance, use Key Perfor- mance
Indicators KPI, created with descriptive analytics
20.report: a collection of visualizations and contain much more detailed information than
dashboards, created with descriptive analytics
21.Buisness Intelligence (BI) is an alternative name for...: descriptive analytics
22.How does amazon use descriptive analytics?: they anticipate shopper pur- chase and cut
down on shipping time by starting the process of shipping products to users before they even
make a purchase, leveraging past data, behavioral information, and finding patterns.
23. Descriptive analytics gives insights
on... W ,W ,W , and H -
: what, when who, and how
24.Data Warehouses: central repository of info that can be analyzed to make more informed
decisions; data inflows into a warehouse from transactional systems, regional databases, etc. on
a regular cadence
