Midterm Exam Flashcards
(42 cards)
3 Primary Methods of Business Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Descriptive Analytics
The interpretation of
historical data to identify trends and patterns
Descriptive analytics, also referred to as exploratory data analysis (EDA), explains the patterns hidden in the data.
These patterns can be:
– The number of market segments
– sales numbers based on regions
– groups of products based on reviews
– software bug patterns in a defect database
– behavioral patterns in an online gaming user database, and more.
These patterns are purely based on historical data and
use basic statistics and data visualization techniques.
Predictive Analytics
The use of statistics to
forecast future outcomes
Prescriptive Analytics
The application of testing
and other techniques to determine which outcome will yield the best result in a given
scenario
The Big Idea
- “The Unicorn”
The Big Idea = The Most Interesting idea! Something the Client MUST continue looking into! - A single sentence
- It must articulate your unique point of view
- Must covey what is at stake
- Must be a complete sentence
3-Minute Story
If you only had 3 minutes to tell your audience EXACTLY what they need to know, what would you say?
Being able to do this removes you from the dependence of your slides or visuals for a
presentation
– What if the boss asks you what you are working on?
– What if you 30 minute presentation gets cut to 10
minutes?
Population Data
In simple terms, population means the complete set of data. This also means that all the possible values are taken into consideration. When we consider the entire possible set of values, we say that we are considering the “population.”
* The following are examples of population:
– the population of all the employees of the entire information technology (IT) industry
– population of all the employees of a company
– population of all the transaction data of an application
– population of all the people in a country (census)
– population of all the people in a state
– population of all the Internet users
– population of all the users of e-commerce sites.
* The list of examples is unlimited.
Sample Data
In simple terms, sample means a section or subset of the population selected for analysis. Examples of samples are the following:
– randomly selected 100, 000 employees from the entire IT industry
– randomly selected 1, 000 employees of a company
– randomly selected 1,000,000 transactions of an application
– randomly selected 10,000,000 Internet users
– randomly selected 5,000 users each from each ecommerce site, and so on.
* Sample can also be selected using stratification (i.e., based on some rules of interest). For example:
– all the employees of the IT industry whose income is greater than $100,000
– all the employees of a company whose salary is greater than $50,000
– the top 100,000 transactions by amount per transaction (e.g., minimum $1,000 per
transaction
– all Internet users who spend more than two hours per day, etc.).
Qualitative or Categorical Data
Qualitative data is not numerical. Data is collected through observations,
conversations, surveys, discussion or may just be demographic data
-type of car
-favorite color
-favorite food
Nominal Data
The order of the data is arbitrary, or no order is associated with the data. For
example, eye color: Blue, Brown, Green, and so forth; no order is associated with the
data.
Bar and Pie charts are most commonly used.
-Status of an application (pending, not pending)
-Gender (Male, Female)
Ordinal Data
This data is in a particular defined order. Examples include Olympic medals, such as
Gold, Silver, and Bronze, and Likert scale surveys, such as disagree, agree, strongly agree. With ordinal data, you cannot state, with certainty, whether the intervals
between values are equal.
The ordinal data only shows the sequences and cannot use for statistical analysis.
Compared to nominal data, ordinal data have some kind of order that is not present in nominal data
Values of Education level - none, primary education, secondary education, higher education
Satisfaction with a product - unsatisfied, satisfied, very satisfied
Quantitative Data
- Quantitative data is numeric. Additionally, quantitative data
can be divided into categories of discrete or continuous data - Quantitative data is often referred to as measurable data.
This type of data allows statisticians to perform various
arithmetic operations, such as addition and multiplication,
and to find population parameters, such as mean or variance. The observations represent counts or
measurements, and thus all values are numerical. Each
observation represents a characteristic of the individual data
points in a population or a sample. - Quantitative data can be used for statistical manipulation.
These data can be represented on a wide variety of
graphs and charts, such as bar graphs, histograms,
scatter plots, boxplots, pie charts, line graphs, etc.
Discrete Data
A variable can take a specific value that is separate and distinct. Each value is not
related to any other value. Some examples of discrete data types include the number of cars per family, the number of times a person drinks water during a day, or the
number of defective products on a production line.
* The discrete data are countable and have finite values; their subdivision is not possible. These data are represented mainly by a bar graph, number line, or frequency table.
-shoe sizes
-number of semesters completed
Continuous Data
A variable can take numeric values within a specific range or interval. Continuous data
can take any possible value that the observations in a set can take. For example, with
temperature readings, each reading can take on any real number value on a thermometer
* The key difference between discrete and continuous data is that discrete data contains the integer or whole number. Still, continuous data stores the fractional numbers to record
different types of data such as temperature, height, width, time, speed, etc. Bar, Line and
Histograms are often use for Continuous data.
-Time it takes to travel to work
-Distance between two planets
First step of charting data
Start with the function (the trend, pattern, or vital piece of information you’re trying to impart at a glance), then consider the user (how they navigate and interact with the data), and only then do we reach the final step: making it as clean and beautiful
as possible
Trend
A trend is usually the result of long-term factors such as population increases or decreases, shifting demographic characteristics of the population, improving technology, changes in the competitive landscape, and/or changes in consumer
preferences.
* A trend shows the general direction in which something is changing.
* Uptrends are marked by rising data points, such as higher swing highs and
higher swing lows.
* Downtrends are marked by falling data points, such as lower swing lows and
lower swing highs.
Pattern
A business pattern is a set of recurring and/or related elements (business
activities, events, weak or strong signals) that indicates a business opportunity or threat.
* A pattern is a repeated occurrence or sequence.
– Center, Spread, Shape and Unusual features
– Symmetric, Bell-Shaped and Skewed
* We often collect data so that we can find patterns in the data, like numbers
trending upwards or correlations between two sets of numbers.
* Depending on the data and the patterns, sometimes we can see that pattern
in a simple tabular presentation of the data. Other times, it helps to visualize
the data in a chart, like a time series, line graph, or scatter plot.
Pattern Options
Symmetric, unimodal
Skewed right
Skewed left
Symmetric, bimodal
Non-symmetric, bimodal
Uniform
Symmetric, unimodal - bell curve
Skewed right - tail to the right
Skewed left - tail to the left
Symmetric, bimodal - two equal peaks
Non-symmetric, bimodal - two, unequal peaks
Uniform - flat uniform amount
Leadership topics
Communication
Effectiveness
Remember their good days
Influence
Priorities
Process & Navigation
Passion
Momentum
Motivation
Vision
Why business?
Pattern Types:
Gaps
Outlier
Gaps - no data in between periods of data
Outlier - a piece of data that is far away from the rest
Relationships in Data
- A relationship shows connections or associations between concepts or ideas.
- The relationship between two data
columns shows you what learning about
one variable tells you about the other. - Most commonly used Chart is a
Scatterplot and works best with
quantitative data.
SWOT Analysis
Strengths, Weaknesses, Opportunities & Threats
A SWOT analysis is designed to facilitate a realistic, fact-based, data-driven look
at the strengths and weaknesses of an organization, initiatives, or within its
industry. The organization needs to keep the analysis accurate by avoiding pre-
conceived beliefs or gray areas and instead focusing on real-life contexts.
Companies should use it as a guide and not necessarily as a prescription.
* A SWOT analysis pulls internal information (strengths of weaknesses of the
specific company) as well as external forces that may have uncontrollable
impacts to decisions (opportunities and threats).
* SWOT analysis works best when diverse groups or voices within an
organization are free to provide realistic data points rather than prescribed
messaging.
* Findings of a SWOT analysis are often synthesized to support a single
objective or decision that a company is facing.
The Analytics Process
- Identify business problem: preprocessing
- Identify data sources: preprocessing
- Select the data: preprocessing
- Clean the data: preprocessing
- Transform the data: preprocessing
- Analyze the data: analytics
- Interpret, evaluate, and deploy the model: post-processing
First steps in the analytics process
- Initial Contact with the Client
- Business Request
- Convert to a Business Problem
- Frame the Problem
– Objectives/Goals
– Data
– Models
