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Data-Driven Decision Making – WGU C207: Qs & As
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Data-Driven Decision Making – WGU C207: Qs & AsDefine analytics ✔️Ans - Turning information into knowledge and
developing fact based strategies to gain a competitive edge.
Descriptive/Diagnostic analytics ✔️Ans - Encompasses the set of
techniques that describes what has happened in the past.
Predictive analytics ✔️Ans - Consists of techniques that use models
constructed from past data to predict the future or ascertain the impact of
one variable on another.
Prescriptive analytics ✔️Ans - Decision models that indicate the best
course of action to take.
Three characteristic of big data ✔️Ans - 1. Structured and unstructured
large volumes
2. Analytics is used to discover and communicate meaningful patterns
3. Analysis of big data requires a system of organization
Davenport-Kim Three-Stage Model* ✔️Ans - A decision-making model
developed by Thomas Davenport and Jinho Kim that consists of three
stages:
1. Framing the problem, (why are you trying to do this and what do we
already know)
2. Solving the problem (Choosing the model, collecting the data, analyzing
the model)
3. Communicating results (Communicating and acting on the results)
Categorical or discrete data variables ✔️Ans - Represents types of data
that can be divided into groups or categories.
Example: race, sex, age group, and educational level, happy, etc.
There are two types of categorical data ✔️Ans - Nominal and Ordinal
,Nominal data* ✔️Ans - Identifies, groups, or categories only. Data
cannot be arranged in an ordering scheme. They are used to name or label
a series of values.
Examples, names, pass/fail, gender, race, the color of the eye
Data can not be ordered and makes no sense to calculate means or
standard deviations off this.
Ordinal data* ✔️Ans - Data is placed into some order by some quality.
They are usually non-numeric captions like happiness or discomfort. It is
the ORDER that matters.
They provide good information about the order of values, like a customer
satisfaction survey.
Examples: top ten best cities to live in or top 10 college football teams.
(ordinal-order matters)
Numerical or continuous variables ✔️Ans - They can accept any value
and can measure nearly anything. Height, weight, BP, etc...
Two types of numerical data ✔️Ans - Interval and Ratio
Interval data* ✔️Ans - It has an order to it and has equal intervals
apart.
It gives us the order of values with the ability to quantify the difference
between each one.
Differences between values can be found and meaningful, but they DO NOT
have a true 0. ( no temperature, no true 0 time, or no true 0 dress size)
These can be added or subtracted but not multiplied
(interval means space in between which is the important thing to
remember).
, Ratio data* ✔️Ans - Data similar to interval data but does have an
absolute 0.
Ratios are meaningful. (Length, Width, Weight, Distance, age, income, stock
prices, repeat customers...)
Data can be added, subtracted, multiplied, or divided.
Qualitative research ✔️Ans - Data not characterized by numbers,
usually textual, visual, or oral responses.
Examples: Hair color, car colors, letter grades, types of coins in a jar.
ANYTHING THAT DOES NOT HAVE A NUMER IN IT!
Quantitative research ✔️Ans - Gives you numerical data.
Three elements to a quantitative study design ✔️Ans - 1. Units, this is
the subject or object that is being observed. (Example: Students are broken
up into three different groups)
2. Treatments, the procedures being applied to each subject. (Examples:
which learning is best, lecture, simulation, or no treatment. Each unit gets a
different treatment in this example)
3. Responses, the effect of the experimental design. (What were the results
of each group, who got the best grades based off the different treatments.)
Data managements responsibility ✔️Ans - Clean and organize data sets,
make sure clean good data is provided.
5 questions asked about data management ✔️Ans - 1. Is the data
relevant to your business
2. Is the data complete, do you have all the data
3. Is the data accurate
4. Is the data available and assessable
5. Is the data timely and up to date
Do all data measurements have some degree of error? ✔️Ans - Yes
4 common errors in data ✔️Ans - 1. Random errors, caused by
unknown and unpredictable errors. The environmental conditions or the
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