Greetings again, to all Porn Stars and Sex Workers on the Web, and thank you so much for continuing to learn on my page, designed for you, from the bottom of my heart! I am honored to be creating this content for you.
At this time in my studies, I have learned many tools to jump-start a project, and the following tool (called MAP 2.0) is the project management framework that I use when starting class assignments, and extracurricular projects.
Also, as I will explain later, this framework is MY version of an initial four-step framework called the Analytics Process. That version is a capable model, but it is too broad for our purposes.
MAP 2.0 (Modified Analytics Process) is more specific, yet general enough for
you to start your own analytics projects. The idea is that you will have
freedom to complete any project, combined with a strict frame for addressing
ambiguity within the data.
I am encouraging all of you to grind it up, consume it, abuse it, re-use it,
and even change MAP 2.0 to whatever project you are working on for data
analysis. You will find that different data sets have different questions that
need to be answer, and therefore, some of the steps in the framework might need
to be revisited, or even eliminated from your project management process.
For
instance, you might find that the first Phase, called “Plan,” might not be
achievable until you have seen the data. Other times, your instructor, client
or manager might have a set of questions for you to answer based on the data.
The point is that the steps in this framework are not static. They are in essence meant to be addressed both specifically and broadly, AND in a different order if need be. This framework will typically be used in combination with a coding language (Python) or statistical computing software (R programming), so if some of the content is confusing, no need to worry.
Come back to this page as often as you like to become familiar with the process... you will be using it quite a bit!
For now, here is what MAP 2.0 looks like:
Steps in Brief
1. Plan- “Define Goals”
Insights
2. Collect- “Lock, Load and Clean”
Insights
3. Explore- “Learn Your Data”
Insights
4. Exploit- “Use Statistical Approaches
for Problem Solving”
Insights
5. Report- “Persuade Management”
6. Managerial Implications- “What Actions
are Acceptable Given the Data”
7. Gratitude- “Thank Your Audience and
Sponsors”
Steps in More Detail
1. Plan: Define Goals
A. Objective: What is your business question? Or problem that you are trying to solve?
B. Complimentary questions/considerations: What are some restraints on your resources? Are there any key competitors to consider? Any other questions worth asking, that can collaborate with your key objective?
C. Sources: Be sure to gather appropriate literature and materials to help you plan and code more effectively. Be certain to cite these authors and contributors of these resources.
D. Insights: What are some of your hypotheses at this point? Use this space to determine them and how you might proceed when and if you encounter certain elements of the data.
Note: There is no coding in R, at this
point. The Plan Phase is for brainstorming and meant to stimulate questions to
help you prepare for the project. As you progress in your project, you might
need to revisit this stage so that you can refine your questions, goals, and create
new hypotheses.
2. Collect: Lock, Load, and Clean
A. Gather the appropriate data via web scrape techniques, or via the use of csv files (you will mostly use csv files).
B. Load data into your analytics environment (like R Studio, or Jupyter), and any packages that might be of use to you, like tidyverse.
C. Clean data, transform it, modify it, wrangle with it so that it is suitable for supervised and unsupervised machine learning analysis.
D. Insights: What data sets are important for your analysis? Which variables (columns) might you use, which ones might be redundant? Are there any additional columns that you might add to your data frames which will explain more of the ambiguity of your data later on (an increased R-Squared Score)?
Note: Coding in your analytics
environment begins to dominate your time and effort, from this phase onward.
3. Explore: Learn Your Data
A. Begin to derive insights from the data using functions in the environment like dim(), str(), head(), tail(), View(), and colnames().
B. Understand the data types of the variables that you will be working with.
i. Nominal: labels which have NO overlap (Gender).
ii. Ordinal: labels that are in a specific order (Likert Scale).
iii. Interval: numeric value that is order dependent as well (Temperature).
iv. Ratio: numeric value that has an absolute zero (Weight and Height).
C. Use functions that will uncover descriptive statistics of the data.
D. Dig deeper into the by use of visualizations. Start to visualize with ggplot2.
E: Insights: Is the data linear? Do you need to run any Log transformations that will make a linear model acceptable for analysis? Is the data informing you that a Clustering model might be more preferable than a linear model? Is Classification the way to go? Do you have any other hypotheses at this point that might make the next stage more expeditious?
Note: The Analyze Phase will enable
you to derive certain “ground truths” about this data and will provide
inspiration for making hypotheses that will be tested in the next phase.
4. Exploit: Use Statistical Approaches for Problem Solving
A. Now that you have many hypotheses to drive your learning and insights, you can now build supervised and unsupervised machine learning models to make predictions about the future. You can use techniques such as classification, regression, and clustering.
B. Choose an appropriate model that addresses your business question (or problem you are trying to solve) from the Plan Phase.
C. Once you have chosen a suitable model that fits your data, use optimization techniques like "ensembling" to optimize the predictive power of your model (if necessary).
D. Insights: Which models best describe the data set that you are using? Are these models difficult to interpret, and thus might be difficult to explain to management? Might you lean on one model more than another (linear regression vs. classification)?
Note: Be sure that you are NOT over
fitting or under fitting your model, and that you partition the data into
training and testing sets, using a 75/25 split, or other variations like 80/20
or 70/30.
5. Report: Persuade Management
A. Use visual aids to show the management team the results of your findings, ggplot2 and R Markdown would be appropriate.
B. Tell a “story” that COMBINES the business problem with analysis of the data. (Spend time on this as it is a difficult task to accomplish)
C. Remember to be clear, and persuasive, as the ultimate goal is for the management team to ACT and MAKE A DECISION on your analysis.
Note: Your report should be
simplified, as if you were communicating with Laypersons. What good is doing a
multi-month analysis if management does not understand the results, and is not
persuaded to impact the business in a meaningful way???
“No one cares about how amazing your
R-Squared Score is… explain how the model answers your business question. What
impact does it have on the business problem?” – A revised Thomas Davenport
quote
6. Managerial Implications: What Actions are Acceptable Given the Data
A. This section should be concise and EXTREMELY clear, 4 sentences at most. It is meant to alleviate any confusion about WHAT to do next.
B. Actions SHOULD be informed by close-ended questions that require “yay” or “nay” in response, like “The marketing team should NOT spend $45,000 for the next Super Bowl campaign.”
C. A leader should be able to use this section of your project to be CONFIDENT about making an impactful yet ethical decision. There should be NO confusion here.
Note: If this project presentation is
given in a meeting at office setting, management should NOT be confused when
they leave the room. They should have a clear path on which actions to take.
7. Gratitude: Thank Your Audience and Sponsors
A. This section is short, although it can be greater in length than Managerial Implications.
B. Thank your AUDIENCE for their attention.
C. Thank your SPONSORS, MENTORS, and THOUGHT LEADERS who helped you on the project.
D. Cite any additional sources so that external parties are given due credit and recognition.
E. Finally, if you are using this framework for extracurricular projects for self-growth or competitions, be sure to praise and consider donating to open source software like R Studio, if you feel that such sources are helpful in your professional and academic journeys.
Note: Be humble, be gracious, and give
abundant credit to those who have helped you.
Sources:
The original 4-step “Analytics Process
(AP)” was initially introduced to me by a Professor of the
Kellstadt Graduate School of Business at DePaul University. My professor presented this information to me and her students in a class called “Fundamentals of
Business Analytics,” in Chicago, IL, 2022. I would like to give her credit for
the “Managerial Implications” section of MAP 2.0.
I would like to credit the “Insights”
portion of each of the first four steps to the Director of the Program of the Kellstadt
Graduate School of Business at DePaul University. He is an educator and thought
leader on Data Visualization and Data Communication. His insight is always
appreciated.
Additional insights were provided from
other resources by other “Big Data” thought leaders like Thomas Davenport
(books, and articles), and Machine Learning Techniques literature (Wiley, HBR,
Packt>).
THANK YOU!
I hope this template is useful to you in your journey to becoming data analysts and data entrepreneurs. Continue to show courage in the face of the toxic Sex Work Industry. Your hard work and dedication WILL pay off. Thank you for visiting my blog!