Midterm

Question 1

Mendoza Vision Statement

Answer 1

will be a premier global business school widely recognized for innovative research, rigorous education programs and formative student experiences, all informed by our Catholic character

Question 2

Why is a vision important? For a leader

Answer 2

Vision leads the leader
It paints the target
It sparks and fuels the fire within, drives leader forward, and is the light that others follow

Question 3

What’s a leader without a vision?

Answer 3

Travels in circles

Question 4

Can there be honorable business?

Answer 4

-

Question 5

What obligations do we have based on the wealth that we earn?

Answer 5

-

Question 6

What is business analytics?

Answer 6

The process of transforming data into insights to improve business decisions

Question 7

3 primary methods of business analytics

Answer 7

Descriptive
Predictive
Prescriptive

Question 8

Descriptive Analytics

Answer 8

Interpretation of historical data to identify trends and patterns (focus of course). EXPLAINS PATTERNS HIDDEN IN THE DATA. Only based on historical data.

Question 9

Predictive Analytics

Answer 9

Use of statistics to forecast future outcomes

Question 10

Prescriptive Analytics

Answer 10

Application of testing and other techniques to determine which outcome will yield the best result in a given scenario

Question 11

The big idea

Answer 11

aka the unicorn
Unique point of view, must convey what is at stake, A COMPLETE SENTENCE (only 1)

Question 12

3 minute story

Answer 12

What you say if you only have three minutes to tell the audience everything they need to know.

Question 13

Population Data

Answer 13

Population: The complete set of data. All possible values considered.

Question 14

Sample Data

Answer 14

A section of the population selected for analysis. can be selected with stratification too

Question 15

Types of Data

Answer 15

1.qualitative/categorical
a. nominal
b. ordinal
2. quantitative
a. Discrete
b. Continuous

Question 16

Qualitative Data

Answer 16

Collected through observations, conversations, surveys, discussion.
Not numerical
(nominal or ordinal)

Question 17

Nominal Data and its common visualization

Answer 17

Within qualitative data.
The order of the data is arbitrary (irrelevant). Examples: Eye color, Application status, Gender.

Common visualizations: Pie and bar charts.

Question 18

Ordinal Data and its common visualization

Answer 18

Within qualitative data.
The order of the data is particularly defined.
Examples: Olympic medals, Likert scale surveys…
You cannot state, with certainty whether the intervals between values are equal.
Only shows sequences (cannot use stat analysis).

Question 19

Quantitative Data and common visualizations

Answer 19

Numeric. Includes discrete or continuous)
Measurable data, allows statisticians to perform arithmetic operations to find population parameters like mean.
Common visualizations: histograms, scatter plots, box plots, pie charts, line graphs, bar graphs

Question 20

Discrete Data

Answer 20

can take a specific value that is separate and distinct. Not related to any other value.
Ex: number of cars per family, number of defective products on production line.

Have finite values, cannot be subdivided.

Common visualizations: number line, bar graph, frequency table

Question 21

Continuous Data

Answer 21

can take numeric values within a specific range or interval. Can take any possible value that the observations in a set can take.
Ex: temperature readings, each reading can take on any real number value on a thermometer.

Discrete data contains the integer, while continuous data stores the fractional numbers.

bar, line and histograms often used.

Examples: time it takes to _, distance between _

Question 22

Charting data steps

Answer 22

Start with the function (trend, vital piece of info, pattern)
Consider the user (how they interact with data)
Make it as clean as possible

Question 23

Trends are the result of factors like…

Answer 23

The result of long-term factors like population size changes, shifting demographic characteristics of the population, improving tech, changes in competitive lanscape, changes in consumer preferences.

Question 24

Trends

Answer 24

Show the general direction in which something is changing
Uptrends are marked by rising data points.

Question 25

Patterns Business Pattern

Answer 25

A repeated occurrence or sequence. A set of recurring or related elements (business activities, events, weak or strong signals) indicating a business opportunity or threat Can be seen in a singular table or until a chart/graph is used

Question 26

Types of graphs

Answer 26

Symmetric Unimodal: one clear peak Skewness: right (peaks right)

Question 27

Gaps and outliers

Answer 27

shown by patterns

Question 28

Relationships in Data

Answer 28

Show connections or associations between concepts or ideas

Question 29

SWOT Analysis

Answer 29

realistic, fact based, data driven look at the strengths and weaknesses of an org., initiatives, or industry. Needs to keep analysis accurate. Pulls internal info (Strengths and weaknesses) and well as external info (opportunities and threats)

Question 30

The Analytics Process

Answer 30

PREPROCESSING Identify business problem Identify data sources Select the data Clean the data Transform the data Analyze the data POSTPROCESSING Interpret, Evaluate, and Deploy the Model

Question 31

First steps in Analytics Process

Answer 31

Contact with client, business request, convert to a business problem, frame the problem with objectives, data, models

Question 32

Leadership topics

Answer 32

Vision, Remember the good days, Effectiveness (the law of the lid), Influence, Navigation and process, Passion, Momentum, Motivation (intrinsic, external, integrated…), Priorities, Gratitude, Communication,

Question 33

Understand the Business Problem, questions to ask

Answer 33

What is the business problem, what are the objectives, requirements of the stakeholders, requirements of the business.

Question 34

establish the MVP

Answer 34

Minimum, Viable, PLAN

Question 35

Data Cleansing

Answer 35

Fixing incorrect, incomplete, duplicate or erroneous data in a data set. you need to: identify data errors, changing/updating/removing data to correct them.

Question 36

Importance of clean data

Answer 36

Improved decision making, boosted efficiency, competitive edge.

Question 37

Passion notes

Answer 37

Its the first step to achievement: desire determines your destiny. It increases your willpower: if you want something bad enough you'll find the willpower to do it. You'll be more productive and dedicated. Friendship is the foundation of influence, its the most positive relationship you can develop on the job with your coworkers. Friendship is a shelter against sudden storms.

Question 38

Data Transformation

Answer 38

Process of converting, structuring data into a usable format that can be analyzed to support decision making.

Question 39

Data transformation benefits

Answer 39

Makes it better organized, easier for humans and computers to use. Properly formatted and validated data improves data quality and protects applications from null values, unexpected duplicates, etc. Data transformation facilitates compatibility between applications, systems, and types of data.

Question 40

Data Model: what is it and what does it help business with?

Answer 40

Visual representations of an enterprises data elements and the connections between them. Enable business and technical resources to collaboratively decide how data will be stores, accessed, shared, updated and leveraged.

Question 41

Lookup Table (data model term)

Answer 41

A table where you'll have a field with a column of unique values for each row/record They generally have Primary Keys which is a field that uniquely identifies each row of a table.

Question 42

Data Table (data model term)

Answer 42

Contains numbers or values, at the most granular level possible. Will contain foreign keys columns that can be user to connect to each lookup table. Foreign keys will not be unique.

Question 43

Normalization (data model term)

Answer 43

The process of organizing the tables and columns in a relational database to reduce redundancies and preserve data integrity. (joining tables based on like fields)

Question 44

Matrix Visualization

Answer 44

matrix makes it easier to display data ACROSS MULTIPLE DIMENSIONS. automatically aggregates the data and enables you to drill down.

Question 45

Leadership topics continued

Answer 45

Motivation (intrinsic, external, integrated...)

Question 46

Calculated Measures

Answer 46

A summarization of any data. POWERBI measure are the way of defining calculations (using DAX formula language). Give us aggregate values from multiple rows from a table.