Southern New Hampshire University Decision Making Presentation

This is the final stage of the Capstone journey. Like in real-world projects, the ultimate destination of any project or work is generally an executive or decision-making meeting, where you are supposed to present your solution to the business problem, based on the project/work you have done. And you would generally get very little time to do that. The purpose of this presentation is to simulate that kind of experience, where you have to present your work to a panelist for 7.5 minutes and then answer the questions in the next 10 minutes.

The objective should be to draw your audience’s attention to the key points of your project – problem definition, solution design, key findings/insights, and business recommendations.

Structure the presentation well – It is good to start with key takeaways – overview of the problem, approach for the solution, key findings & insights, and recommendations & next steps. The inclusion of the potential benefits of implementing the solution will give you the edge.

Focus on explaining the takeaways in an easy-to-understand manner (a business leader wouldn’t know about the term ‘confusion matrix’ but might understand ‘% of events (1s) predicted correctly by the model’). SAMPLE LIVE PRESENTATION CARBON EMISSIONS FORECASTING
Global warming is a growing problem caused
by greenhouse gases
PROBLEM
DEFINITION
CO2 emissions are one of main contributors
to greenhouse gases
Energy production by humans heavily
contribute to CO2 emissions
PROBLEM TO SOLVE
 We need to figure out future CO2 emissions to see how fast
the problem is growing
 Can we use time series models to predict CO2 emissions
from the use of natural gas?
 What are some policies that can be adopted to reduce
natural gas carbon emissions?
SOLUTION
APPROACH
 Data exploration led to a few
key takeaways
 CO2 emission values
oscillate as the years
progress (seasonal)
 Natural gas emissions are
steadily increasing (red line)
 Coal emissions have
decreased in recent years
(blue line)
EMISSIONS
SPECIFICALLY DUE TO
NATURAL GAS
 The full data set of the
training data from January
1994 to July 2014.
 The data can be broken into
the following components:
 Trend: (Increasing,
decreasing, linear, and nonlinear)
 Seasonality: (repeated
pattern)
 Residuals: (randomness)
SARIMAX is a time series model chosen to forecast
emission values
PROPOSED
MODEL SOLUTION
Seasonal Auto Regressive Integrated Moving Average with
exogenous factors
SARIMAX takes seasonality and exogenous factors into
account along with previous values and noise for
calculating predictions
Provided data shows seasonality and other energy source
production may impact production
FINAL MODEL
SOLUTION
 AR time series model had
the best performance out
of all models tested
 The appropriate lag value
was able to address
seasonality
 Forecasted CO2 emissions
for natural gas follow the
same trend and value
magnitude
 Alternative energy sources are needed to replace coal and
natural gas electric power generation
 Invest in research and technology to develop clean energy
PROPOSED
BUSINESS
SOLUTION
sources
 Some examples include
 Nuclear
 Solar Power Energy
 Wind Turbine Energy
 Ethanol
EXECUTING BUSINESS SOLUTION
 Nuclear power is the least expensive so start there
 Invest in infrastructure for more power plant
constructions
 Promote support educational grants and programs for
students to study and infuse the workforce
 Risks like nuclear reactor malfunctions are possible but
unlikely
 Need to education the public and overcome political
games some governments play
EXECUTIVE SUMMARY
 Autoregressive model can predict CO2 emissions with
appropriate parameters
 CO2 emissions from natural gas is likely to increase over the
next 12 months
 Nuclear energy is a reliable source to replace other energy
sources of electricity
 Replacing coal and natural gas power with nuclear power will
drastically reduce carbon emissions
 Less CO2 emissions will reduce greenhouse gases
contributing to global warming
 Risks
CARBON
EMISSIONS –
RISKS &
CHALLENGES

There are clear high upfront costs associated with alternative methods to
energy generation, as in the case of wind and solar energy.

We are still in the early stage of recognizing these alternatives to be at
par with natural gas in terms of the amount of energy required to be
generated.

There will be a gap between the cost benefits of alternatives to cat5ch up
to the already existing natural gas.
 Challenges

There will strong opposition from the natural gas industry, including utility
providers and the natural gas pipelining industry

Maintenance of new equipment such as large solar fields or wind
turbines will bring a new challenges and the need for the rise of new
industry standards for the same.

Policies will need to e put n place offering tax benefits to firms and end
users of alternative sources of energy, to get more customers to switch.
APPENDIX
MODEL COMPARISON
Model
AR
MA
ARMA
ARIMA
SARIMAX
AIC
-4.86
-229.00
-291.16
-306.28
-308.35
RMSE (test data)
14.17
43.95
89.28
114.15
31.30
PREDICTIONS ON TEST AND TRAIN DATA
SOLUTION CODE HIGHLIGHTS
SOLUTION
CODE
HIGHLIGHTS
Milestone 1
Problem Definition, Data Exploration, Proposed Approach
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Problem Definition
●
●
Context – Why is this problem important to solve?
○
Brief Introduction to the problem
○
Advantages of solving the problem
○
Good to add some facts and numbers to support your argument
Objectives – What is the intended goal?
○
The goals you are trying to achieve.
○
Example – Reducing the attrition rate, Improving the lead conversion rate
○
There can be multiple goals
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Problem Definition
●
●
The key questions – What are the key questions that need to be answered?
○
Curating questions related to the problem that need to be answered
○
The burning questions or important insights you are planning to draw while solving the problem
The problem formulation – What is it that we are trying to solve using data science?
○
Already explained the general form of the problem. Now, formulate the problem as a data scientist
○
How data science fits into the spectrum of solving the problem
○
The nature of the data science problem
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Data Exploration
●
●
Data Description
○
Background of the data and what is it about?
○
Information about the variables included in the data
Observations & Insights
○
What are some key patterns observed in the data during EDA?
○
How do the key patterns affect/relate to the problem?
○
What are the data treatments or pre-processing steps required, if any?
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Proposed Approach
●
●
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Potential techniques
○
What are the potential techniques/models that should be explored in the next step?
○
Why the techniques suggested are the best to explore for the data and problem at hand?
Overall solution design
○
What is the potential solution design?
○
The steps (and substeps) that will be followed to solve the problem
Measures of success
○
The key measures of success that will be used to compare potential techniques/models
○
Why the metric chosen is the best for the problem at hand?
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Milestone 2
Refined Insights, Techniques’ Comparison, Final Solution Design
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Refined Insights
●
List the most meaningful insights from the data relevant to the problem
●
A meaningful insight has three components:
●
○
Good interpretation of the output from the data
○
Potential reason for that output
○
What it means for the problem/business?
Not more than 1 page or slide
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Comparison of Techniques and their Performances
●
Try different techniques to solve the problem
●
Compare the performance of different techniques based on the metric chosen for the problem
●
○
Which technique is performing relatively better?
○
Pros and cons of different techniques
○
Good to include a comparison table
Is there scope to improve the performance further? If yes, how?
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Proposal for the Final Solution Design
●
●
What model do you propose to be adopted?
○
Based on the comparison, which is the best model for the problem?
○
Think of the tradeoff between model performance and model interpretability
Why is this the best solution to adopt?
○
Reason for choosing the best model
○
How that solves the problem?
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Final Submission
Executive Summary, Problem and Solution Summary, Recommendations
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Executive Summary
●
●
What are the key takeaways?
○
Identify and focus on the big picture first and all of its components
○
These components are usually the driving force for the end goal
○
Summarize the most important findings and takeaways in the beginning
What are the key next steps?
○
Steps that can be taken to improve the solution
○
How to make the best of the solution?
○
What are the steps to be followed by the stakeholders?
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Problem and Solution Summary
●
What problem was being solved?
○
●
Final proposed solution design
○
●
Summary of the problem
What are the key points that describe the final proposed solution design?
Why is this a ‘valid’ solution that is likely to solve the problem?
○
The reason for the proposed solution design
○
How it would affect the problem/business?
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Recommendations for Implementation
●
What are some key recommendations to implement the solution?
●
What are the key actionables for stakeholders?
●
What is the expected benefit and/or costs?
●
○
List the benefits of the solution
○
Take some rational assumptions to put forward some numbers on costs/benefits for stakeholders
What are the key risks and challenges?
○
●
What are the potential risks or challenges of the proposed solution design
What further analysis needs to be done or what other associated problems need to be
solved?
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General Tips
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Do’s and Don’ts for a Good Project Report
Do’s
Don’ts
✅
Focus must be on the business problem and solving
the same by analyzing the data
❌
Following this template word to word. This template is
just to help you get started
✅
Follow the guidelines provided on LMS and by the
Program Office
❌
Presenting numbers and figures without the business
interpretation and what it means for the problem
✅
Include only the important material in the main body.
Appendix can contain codes and all less important
tables, figures, etc.
❌
Using any non-standard abbreviation in your report
✅
Adding codes and reference in the Appendix
❌
Filling the main body of the report with codes
✅
Easily readable tables, figures, and graphs. Work on
the axis labels and legends
❌
Screenshots of tables/charts from Python output
✅
Present all numbers up to 2 places of decimals only,
unless required otherwise
❌
Explaining theory of the techniques in the project report
❌
Using very large fonts and/or adding unnecessary visuals
❌
Including too much content on a single slide
✅
Highlight the innovations of the project and why the
methods suggested there ought to be utilized by the
industry
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Project Report VS Live Presentation
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Graded by evaluator
based on files submitted
Includes all the analysis
Can be a bit elaborate
Convey the methodology
to the evaluator
Follow the rubric
To be created for each
milestone
●
●
●
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Project
Report
Graded by faculty based
on live presentation
Good Structure and Flow
Crisp and Neat Slides
Include only bullet points
Take your audience
through the logical steps
of your full project work
Refer here for guidelines
on creating presentation
Live
Presentation
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