Midterm Flashcards
Mendoza Vision Statement
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
Why is a vision important? For a leader
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
What’s a leader without a vision?
Travels in circles
Can there be honorable business?
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What obligations do we have based on the wealth that we earn?
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What is business analytics?
The process of transforming data into insights to improve business decisions
3 primary methods of business analytics
Descriptive
Predictive
Prescriptive
Descriptive Analytics
Interpretation of historical data to identify trends and patterns (focus of course). EXPLAINS PATTERNS HIDDEN IN THE DATA. Only based on historical data.
Predictive Analytics
Use of statistics to forecast future outcomes
Prescriptive Analytics
Application of testing and other techniques to determine which outcome will yield the best result in a given scenario
The big idea
aka the unicorn
Unique point of view, must convey what is at stake, A COMPLETE SENTENCE (only 1)
3 minute story
What you say if you only have three minutes to tell the audience everything they need to know.
Population Data
Population: The complete set of data. All possible values considered.
Sample Data
A section of the population selected for analysis. can be selected with stratification too
Types of Data
1.qualitative/categorical
a. nominal
b. ordinal
2. quantitative
a. Discrete
b. Continuous
Qualitative Data
Collected through observations, conversations, surveys, discussion.
Not numerical
(nominal or ordinal)
Nominal Data and its common visualization
Within qualitative data.
The order of the data is arbitrary (irrelevant). Examples: Eye color, Application status, Gender.
Common visualizations: Pie and bar charts.
Ordinal Data and its common visualization
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).
Quantitative Data and common visualizations
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
Discrete Data
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
Continuous Data
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 _
Charting data steps
Start with the function (trend, vital piece of info, pattern)
Consider the user (how they interact with data)
Make it as clean as possible
Trends are the result of factors like…
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.
Trends
Show the general direction in which something is changing
Uptrends are marked by rising data points.
Patterns
Business Pattern
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
Types of graphs
Symmetric
Unimodal: one clear peak
Skewness: right (peaks right)
Gaps and outliers
shown by patterns
Relationships in Data
Show connections or associations between concepts or ideas
SWOT Analysis
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)
The Analytics Process
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
First steps in Analytics Process
Contact with client, business request, convert to a business problem, frame the problem with objectives, data, models
Leadership topics
Vision, Remember the good days, Effectiveness (the law of the lid), Influence, Navigation and process, Passion, Momentum, Motivation (intrinsic, external, integrated…), Priorities, Gratitude, Communication,
Understand the Business Problem, questions to ask
What is the business problem, what are the objectives, requirements of the stakeholders, requirements of the business.
establish the MVP
Minimum, Viable, PLAN
Data Cleansing
Fixing incorrect, incomplete, duplicate or erroneous data in a data set.
you need to: identify data errors, changing/updating/removing data to correct them.
Importance of clean data
Improved decision making, boosted efficiency, competitive edge.
Passion notes
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.
Data Transformation
Process of converting, structuring data into a usable format that can be analyzed to support decision making.
Data transformation benefits
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.
Data Model: what is it and what does it help business with?
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.
Lookup Table (data model term)
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.
Data Table (data model term)
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.
Normalization (data model term)
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)
Matrix Visualization
matrix makes it easier to display data ACROSS MULTIPLE DIMENSIONS. automatically aggregates the data and enables you to drill down.
Leadership topics continued
Motivation (intrinsic, external, integrated…)
Calculated Measures
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.
