gmmadrid-blog

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Reflection of Full Sail Business Intelligence Master of Science Program

The Full Sail Business Intelligence Master of Science Program met my expectations and goals by providing me with an excellent foundation to jump-start my career into the world of big data. I contemplated for over a year about how I wanted to utilize my Post 9/11 benefit. In the end I decided on Full Sail and it programs for a variety of reason including the ability to come back and revisit a specific class even years later. This made so much sense to me since being agile is my career is important in order to keep my skillset current. Big Data itself is a new field and information technology is always changing.

From beginning to end the program kept me engaged. The research tied into each specific course and each new course added value and built onto the previous course. The way it was presented made sense: Starting from granular data, data storage and architecture, to ETL process, conducting analysis and pattern recognition and then onto visualizations, reporting and communication.

I learned so much in this program that it is almost too much too list. The program definitely pushed me out of my comfort zone but in a very good way. Things that specifically standout in my mind were conducting statistical analysis with Bayes Theorem Being able to build websites, infographics and a capstone project was a perfect blend to apply towards my career. The program also helped me narrow down the specific direction for which I want to take my career.  

My favorite courses where those where I could apply my creativity such as creating data visualizations, infographics and dashboards. That being said, as I transition into a practitioner in the BI industry I want to continue developing my skill-set in those particular areas. I currently am a Senior Project Manager for a Fortune 500 company and I am looking forward to applying my knowledge in my current and future positions. Overall I am very pleased with the program, I am proud to join the Full Sail Alumni and I will continue to recommend this program to anyone who is interested in BI.

Greg Madrid

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REFLECTION OF BIN 660 CASE STUDIES COURSE

How has the Business Intelligence Case Studies Course met your Mastery Journal Timeline expectations and goals?

The Business Intelligence Case studies course surprised me a little. I really did not know what to expect coming into this course but after taking the course you begin to see the real world scenarios and how it applies towards your capstone project. I have an MBA degree but not having done a SWOT or PESTEL analysis in a few years challenged me to consider the concepts as it applies to BI solutions. This course was able to tap into my previous knowledge of business analysis and specifically gear it towards the applications of BI solutions.  

What have you learned from the Course content?

Reading and depicting information in the annual reports helped me unveil the mission and ideologies in the company I am currently employed with, which also happens to be my capstone project. The cost benefit analysis really made you dig for deeper meaning in order to present factual information. In doing so I learned how my company determines salvage value of software and shared these concepts with my classmates to help their own analysis. On that same subject I learned the importance of properly formatting color schemes in accounting in order to properly present financial data.

How might you apply what you learned as you transition into your role as a practitioner in the BI industry and pursue your professional career upon graduation?

Between the Gap analysis and the cost benefit analysis the course gave an excellent source for blending the two together to allow you to have a fundamental knowledge of how to present a BI solution proposal.   As a practitioner of BI anytime I am ready to propose an idea, I want to use my final capstone project as a template to follow. Since this is my own work, this solution plan will allow me to have a foundation from which to build future proposals.  In addition, by using it as a template I will have the added bonus of building upon this and other BI solutions 

Overall this was an excellent class!

Greg Madrid

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BIN 650

This course went over Leadership and communications. It was the perfect timing for this course because I was seeking a promotion at work and every lesson tied into what I needed. Some of the topics covered included: leadership and management styles, performing self assessments, researching leadership tools, and learning how to delivering more professional presentations.

The discussion posts were quite memorable as well including the first assignment where we got to watch a classic movie with Gregory Peck called Aces High, excellent video references and even a look into contract negotiations between George Lucas and Robert Iger at Disney.

I have to admit that even the final project in week 4 helped me learn more about the company I work for. By tracking revenue financials and investigating trends in order to deliver a final presentation. Overall this course helped me and my career the negotiations topics also helped build my confidence levels going into some of my upcoming interviews.

Thank you John Reneski for making this a GREAT CLASS!

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REFLECTION JOURNAL ENTRY of BIN 630 VCR (Visualization) Course

This course reflects the real reason why I got into the MBI program. The ability to take raw data and turn it into meaningful visualization that is easy to understand is what I want to specialize in. The creativity involved with selection of color and use of visuals for more meaningful impact was fun. Certainly more compelling than the mundane Excel charts and PPT that has become an industry standard for far too long now has lost its luster and when shown in meetings has a tendency to reflect that same level of interest.

The first week’s assignment with developing an info-graphic provided clarity and the assignments that followed you forced you to be more creative in approach. In the 2nd week we had to review a selection of movies and use that theme to tell the story of the movie using data visualizations. It required much more work, however the results were so much more compelling that even my teenage daughter and wife began to show interest in what I was doing for the first time in my program. This was even more evident in the 3rd week’s assignment when we had to select a superhero and build a dashboard. In fact hearing the words “What Dad is doing is so cool!” made me smile about the work effort I pushed myself with on the assignments as well as what the future holds for my career.  

 :)

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PTR BIN 610 - Summary

Excellent Class - I learned quite a bit in this course. More intense then I originally expected. The gathering of data was very much a hard part of this course that challenged me to utilize scarping data and transferring it to Excel to conduct analysis. Became much more comfortable with Bayes in thous course. The assignments challenged me each week (all week) to really analyze my own data. From stock markets, to restaurants, and even my own company that I currently work for. This helped me become more engaged with my work at work since I was able to look at the data in different ways. Overall great yet challenging course! It really made you learn!

[gmmadrid-blog]

BIN 610 PTR

GLOSSARY TERMS:

NRSROs

Definition - Stands for "nationally recognized rating organization". SEC uses this term to distinguish between investment grade and non-investment grade paper. Basically it is a credit rating agency that the creditworthiness of an entity with respects to the money market instruments.

Source - Credit Rating Agencies and Nationally Recognized Statistical Rating Organizations (NRSROs) (n.d.). In US Securities and Exchange Commision. Retrieved March 15, 2018, from https://www.sec.gov/fast-answers/answersnrsrohtm.html          

Sentence – The NRSRO issues credit ratings which the SEC uses to permit financial firms for regulatory purposes.

Correlation

Definition - Used to test relationships, a correlation coefficient is a measure of the degree to which two variables tend to move together. This is typically between quantitative variables or categorical variables

Source - Text Chapter Glossary of Statistical Terms (n.d.). In OCED Retrieved March 15, 2018 from https://stats.oecd.org/glossary/detail.asp?ID=456            

Sentence – There is a strong correlation between the two variables that happen to move in the same direction

Regression

Definition -A statistical measure used in finance, investing and other disciplines that attempts to determine the strength of the relationship between one dependent variable and a series of independent variables. Attempts to model the relationship between two variables by fitting a linear equation to observed data.

Source - Linear Regression (n.d.). In Yale. Retrieved March 15, 2018 from http://www.stat.yale.edu/Courses/1997-98/101/linreg.htm

Sentence – we use regression analysis forecast tornado activity with the wind and atmospheric pressure  

Qualitative vs Quantitative

Definition - Quantitative data are measures of values or counts and are expressed as numbers. Qualitative data are measures of 'types' and may be represented by a name, symbol, or a number code. Quantitative data are data about numeric variables (e.g. how many; how much; or how often). Qualitative data are data about categorical variables (e.g. what type).

Source - Quantitative and Qualitative Data (n.d.). In Australian Bureau of Statistics. Retrieved March 15, 2018 from http://www.abs.gov.au/websitedbs/a3121120.nsf/home/statistical+language+-+quantitative+and+qualitative+data

Sentence – Both qualitative and quantitative analysis is conducted when we determining risk and establishing a risk matrix

Bias

Definition - Refers to the tendency of a measurement process to over or under estimate the value of a population parameter

Source - Stattrek.com (2017). Statistics Dictionary. Retrieved March 18, 2018 from http://stattrek.com/statistics/dictionary.aspx?definition=Bias

Sentence – The difference in the model was bias due to the person’s expected value and the true value of the population parameter

Univariate vs Multivariate Analysis

Definition – (1) Univariate is the simplest form of analysis based on one variable.  Univariate looks a one variable at a time and the objective is to describe the variable. (2) Multivariate looks at more than two variables together for any possible association or interactions.

Source - Afsar, J. (2012 Sep 21). Univariate, Bicvariate, And Multivariate Data. Retrieved March 18, 2018 from http://www.engineeringintro.com/statistics/introduction-statistics/univariate-bivariate-and-multivariate-data/

Sentence - A univariate analysis will help us determine how many people are infected while a multivariate analysis will determine the correlation between gender, race a geographic location.

 Heisenberg's Uncertainty Principle

Definition -Werner Heisenberg coined this principle to state that two properties can be measured simultaneously with infinite precision.

Source - Uncertainty principle (29 December 2017). In Britannica. Retrieved March 16, 2018 from https://www.britannica.com/science/uncertainty-principle Uncertainty principle, also called Heisenberg uncertainty principle or indeterminacy principle, statement, articulated (1927) by the German physicist Werner Heisenberg.

Sentence –Heisenberg's Uncertainty Principle can be applied to identical objects

 Chaos Theory  

Definition -Chaos Theory deals with nonlinear things that are effectively impossible to predict or control, like turbulence, weather, the stock market, our brain states, and so on.              

Source - What is Chaos Theory? (n.d.). In Fractal Foundation. Retrieved March 16, 2018 from http://fractalfoundation.org/resources/what-is-chaos-theory/ There are many variables and items in nature such as clouds that follow Chaos Theory.

Sentence – The stock market randomness can be defined as chaos theory

 Dynamic Systems

Definition -A dynamical system is a system whose state evolves with time over a state space according to a fixed rule.              

Source - Dynamical system definition (n.d.). In Math Insight. Retrieved March 16, 2018 from http://mathinsight.org/definition/dynamical_system  

Sentence - Dynamic Systems formulates differential equations

 Nonlinear Systems

Definition -Nonlinear systems cannot be decomposed into parts and reassembled into the same thing, and do not change in proportion to a change in an input. A nonlinear system of equations, which is a set of simultaneous equations in which the unknowns (or the unknown functions in the case of differential equations) appear as variables of a polynomial of degree.              

Source - Nonlinear systems (n.d.). In BusinessDictionary. Retrieved March 16, 2018 from http://www.businessdictionary.com/definition/nonlinear-system.html

Sentence - Nonlinear systems have multiple operating points

 Prediction

Definition -the process of determining the magnitude of statistical variates at some future point of time. To use regression analysis for prediction, data are collected on the variable that is to be predicted, called the dependent variable or response variable, and on one or more variables whose values are hypothesized to influence it, called independent variables or explanatory variables.

Source - Glossary of Statistical Terms (n.d.). In OCED Retrieved March 16, 2018 from https://stats.oecd.org/glossary/detail.asp?ID=3792

Sentence – We made a prediction based on some analysis using Bayes Theorem

 Forecast

Definition - A planning tool that helps management in its attempts to cope with the uncertainty of the future, relying mainly on data from the past and present analysis of trends.

Source - businessdirectory.com (2017). Forecasting. Retrieved March 16, 2018 from http://www.businessdictionary.com/definition/forecasting.html

Sentence - We were able to forecast the hurricane path based on the spaghetti patterns models

 Brownian Noise/Motion

Definition – Named after the botanist Robert Brown (discovered Brownian motion - random particle motion) it is a change in sound signal from one moment to the next is random.

Source - What Is Brown Noise? (30 July 2013). In Live Science. Retrieved March 16, 2018 from https://www.livescience.com/38547-what-is-brown-noise.html        

Sentence – The random Brownian Noise had less energy at higher frequencies.

 Overfitting

Definition - A modeling error which occurs when a function is too closely fit to a limited set of data points.

Source - Overfitting (n.d.). In Investopedia. Retrieved March 16, 2018 from https://www.investopedia.com/terms/o/overfitting.asp

Sentence - Overfitting the model can become too complex to determine patterns

 Extrapolation

Definition - An estimation of a value based on extending a known sequence of values or facts beyond the area that is certainly known.

Source - Rouse, M. (n.d.). Extrapolation and Interpolation. In Whatis.com. Retrieved March 16, 2018 from http://whatis.techtarget.com/definition/extrapolation-and-interpolation

Sentence - Extrapolation is useful when estimating values that go beyond a set of given data

 Bayes Theorem    Bayes' theorem

Definition - named after 18th-century British mathematician Thomas Bayes, is a mathematical formula for determining conditional probability. In finance, Bayes' theorem can be used to rate the risk of lending money to potential borrowers. In finance, Bayes' theorem can be used to rate the risk of lending money to potential borrowers.

Source - Bayes' Theorem (n.d.). In Investopedia. Retrieved March 16, 2018 from https://www.investopedia.com/terms/b/bayes-theorem.asp

Sentence – Bayes Theorem can be applied when you have conditional probabilities

 Prior Probability

Definition - Prior probabilities represent what we originally believed before new evidence is uncovered. New information is used to produce updated probabilities and is a more accurate measure of a potential outcome.

Source - Prior Probability (n.d.). In Investopedia. Retrieved March 16, 2018 from https://www.investopedia.com/terms/p/prior_probability.asp            

Sentence - Prior probabilities are the original probabilities of an outcome, which be will updated with new information to create posterior probabilities.

 Posterior Probability

Definition - In statistical terms, the posterior probability is the probability of event A occurring given that event B has occurred. Simply put the probability of an event occurring after taking into consideration new information.

Source - Posterior Probability (n.d.). In Investopedia. Retrieved March 16, 2018 from https://www.investopedia.com/terms/p/posterior-probability.asp

Sentence - Posterior probability is normally calculated by updating the prior probability by using Bayes' theorem.

 Heuristics

Definition – A shortcut approach to solving problems. Heuristics refers to the random trial and error methods or the experience based techniques used for solving of problems.

Source - Heuristics (n.d.). In BMASkool.com Retrieved March 16, 2018 from https://www.mbaskool.com/business-concepts/statistics/7460-heuristics.html              

Sentence - The heuristics method of solving problems starts with a collection of unknown factors, and then proceeds with the independent experimental exploration for each of the factors.          

 Algorithms

Definition - An algorithm is a procedure or formula for solving a problem, based on conducting a sequence of specified actions.

Source - Rouse, M. (n.d.). algorithm. In Whatis.com. Retrieved March 16, 2018 from http://whatis.techtarget.com/definition/algorithm              

Sentence - In math and technology , an algorithm usually means a small procedure that solves a recurrent problem.

 Efficient-Market Hypothesis

Definition - The efficient market hypothesis (EMH) is an investment theory that states it is impossible to "beat the market" because stock market efficiency causes existing share prices to always incorporate and reflect all relevant information.

Source - Efficient market hypothesis (EMH) (n.d.). In Investopedia. Retrieved March 16, 2018 from https://www.investopedia.com/terms/e/efficientmarkethypothesis.asp            

Sentence – day traders blamed the efficient market hypothesis was the rationale for the poor investment choices they had made to their portfolio.

 Healthy Skepticism

Definition - Healthy skepticism is the willingness to logically test anything that one considers as "true" and being inclined to test new information before one accepts it as "true".

Source - Healthy skepticism (EMH) (n.d.). In Faith Principles. Retrieved March 16, 2018 from http://www.faithprinciples.com/intelligence.htm  

Sentence - Someone with healthy skepticism will be doubtful if it is not backed by logical facts.

 Initial Condition Uncertainty

Definition - Arises from the properties of chaotic systems.

Source - Werndl, C. (2013, March 13). Initial conditions dependence and initial conditions uncertainty in climate science. In University of Pittsburgh. Retrieved March 16, 2018, from http://philsci-archive.pitt.edu/14133/1/Revision5.pdf

Sentence – Tornado forecasting uses Initial Condition Uncertainties when creating predictive models

 Scenario Uncertainty

Definition - It is uncertainty due to descriptive errors, aggregation errors, errors in professional judgment, or incomplete analysis.

Source - 2015 NAL Glossary (2015). In United States Department of Agriculture, National Agricultural Library. Retrieved March 16, 2018 from https://definedterm.com/scenario_uncertainty

Sentence - The use of scenarios uncertainty is one approach used in policy analysis to deal with uncertainty related to the external environment of a system

 Structural Uncertainty

Definition - Probability of the occurrence of an unanticipated event due to the particular configuration of a system such as an economy, market, or organization.

Source - structural uncertainty (n.d.). In Business Dictionary. Retrieved March 18, 2018 from http://www.businessdictionary.com/definition/structural-uncertainty.html

Sentence - The term “structural uncertainty” is used here, as elsewhere, to describe those other sources of uncertainty not characterized in other ways (parameter or methodological).

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PMA - BI Glossary

New Terms

Business Process Modeling

The analytical representation or illustration of an organization’s business processes.        

http://whatis.techtarget.com/definition/business-process-modeling

Process Simulation Modeling (PSIM)  

Process Modeling and Design involves visual models of activities, resources, inputs, outputs, and business rules. The outcome of the as-is process model is a shared understanding of how the current business process works. This reveals information that is otherwise difficult to document and comprehend. As a result of this step, it is not uncommon for process owners, suppliers, and customers to identify process improvement ideas. Process models proposed to-be processes  provide visualizations of future-state alternatives.

http://www.technologymultipliers.com/process-simulation-modeling-and-analysis

 Decision Support Systems (DSS)

A specific class of computerized information system that supports business and organizational decision-making activities.            

https://www.informationbuilders.com/decision-support-systems-dss

 Performance Indicators (KPIs)

A measurable value that demonstrates how effectively a company is achieving key business objectives.  

https://www.klipfolio.com/resources/articles/what-is-a-key-performance-indicator

 AB Testing         

Is comparing two versions of a web page to see which one performs better. You compare two web pages by showing the two variants (let's call them A and B) to similar visitors at the same time.      

https://vwo.com/ab-testing/

 Balanced Scorecards

A management system aimed at translating an organization's strategic goals into a set of performance objectives that, in turn, are measured, monitored and changed if necessary to ensure that the organization's strategic goals are met.

http://searchcio.techtarget.com/definition/balanced-scorecard-methodology

  Stand-Alone Simulation vs Integrated Simulation            

Stand-Alone Simulation agrees with the notion that you train as you learn where Integrated Simulation is based more on experience and observation (experiential learning) used to enrich and support real world systems

Sokolowski J., Banks C. (2012) Real World Applications in Modeling and Simulation, John Wily & Sons, Inc.

 Complicated and Complex Systems        

Complicated and Complex Systems the main difference between complicated and complex systems is that with the former, one can usually predict outcomes by knowing the starting conditions. In a complex system, the same starting conditions can produce different outcomes, depending on interactions of the elements in the system.”

http://www.businessofgovernment.org/article/managing-complicated-vs-complex

  Fidelity and Validity      

Validity generally means how closely the simulated results match the data collected from real life case. Fidelity generally means how closely the simulation replicates the environment, responses, and controls.               

https://www.quora.com/The-difference-between-validity-and-fidelity-in-simulation

 Discrete-Event Simulation (DES)              

The process of codifying the behavior of a complex system as an ordered sequence of well-defined events. In this context, an event comprises a specific change in the system's state at a specific point in time.             

http://whatis.techtarget.com/definition/discrete-event-simulation-DES

  The System Dynamics (SD)         

System Dynamics is a computer-aided approach to policy analysis and design.  It applies to dynamic problems arising in complex social, managerial, economic, or ecological systems–literally any dynamic systems characterized by interdependence, mutual interaction, information feedback, and circular causality.         

http://lm.systemdynamics.org/what-is-s/

  Discrete vs Continuous Simulation Paradigms   

Discrete event simulation is appropriate for systems whose state is discrete and changes at particular time point and then remains in that state for some time. Continuous simulation is appropriate for systems with a continuous state that changes continuously over time.

https://www.researchgate.net/post/what_is_the_exact_difference_between_Continuous_discrete_event_and_discrete_rate_simulation

  Deterministic vs Stochastic Simulation Paradigms

"In deterministic models, the output of the model is fully determined by the parameter values and the initial conditions initial conditions. Stochasticmodels possess some inherent randomness.  The same set of parameter values and initial conditions will lead to an ensemble of   different outputs."

https://www4.stat.ncsu.edu/~gross/BIO560%20webpage/slides/Jan102013.pdf

  Static vs Dynamic Simulation Paradigms

A static model is one which contains no internal history of either input values previously applied, values of internal variables, or output values. The defining feature of a dynamic model is that unlike the static model, it does maintain an internal 'memory' of some combination of prior inputs, internal variables, and outputs.                 

http://www.edscave.com/static-vs.-dynamic-models.html

  Monte Carlo Simulation              

Also known as “probability simulation” is a technique used to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models."

https://www.riskamp.com/files/RiskAMP%20-%20Monte%20Carlo%20Simulation.pdf

  Recognition-Primed Decision Model (RPD)         

The RPD Process highlights the three simple steps that we go through, often subconsciously, when we need to make a quick decision. This is based on “pattern recognition,” and on how we can use our past experiences of similar situations to make decisions. The three steps are:  Experiencing the situation, Analyzing the situation, Implementing the decision.

https://www.mindtools.com/blog/corporate/wp-content/uploads/sites/2/2015/03/Recognition-Primed-Decision-Process1.pdf

  Finite State Machine

Online   a computation model that can be implemented with hardware or software and can be used to simulate sequential logic and some computer programs. Finite state automata generate regular languages. Finite state machines can be used to model problems in many fields including mathematics, artificial intelligence, games, and linguistics.         

https://brilliant.org/wiki/finite-state-machines/

  Neural Networks            

An information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems.

https://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html#What is a Neural Network

 Fuzzy Logic and Fuzzy Inference

Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made, or patterns discerned.  Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based.

http://whatis.techtarget.com/definition/fuzzy-logic

https://www.mathworks.com/help/fuzzy/fuzzy-inference-process.html?requestedDomain=true

  Agent-Based Modeling

In agent-based modeling (ABM), a system is modeled as a collection of autonomous decision-making entities called agents. Each agent individually assesses its situation and makes decisions on the basis of a set of rules. Agents may execute various behaviors appropriate for the system they represent—for example, producing, consuming, or selling. Repetitive competitive interactions between agents are a feature of agent-based modeling, which relies on the power of computers to explore dynamics out of the reach of pure mathematical methods (1, 2).

http://www.pnas.org/content/99/suppl_3/7280

  Object-Oriented Programming

Object-oriented programming (OOP) is a programming language model organized around objects rather than "actions" and data rather than logic. Historically, a program has been viewed as a logical procedure that takes input data, processes it, and produces output data.         

http://searchmicroservices.techtarget.com/definition/object-oriented-programming-OOP

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Process Modeling & Analysis Course Reflection:

“As I progressed through the lesson plans in this course each week seemed to require more and more research. From data modeling to uncovering crime statistics and offer solutions to a city all the way to revamping the McCormick Spice company’s tired old HR department in order to grow a consumer base.  

Nothing however challenged me more than the primary homework assignments which involved me building my first website. Event Process Flows, Causal loop diagrams, 5 year plans and detailed descriptions for stock and flow made the research seem like a real world consulting project rather than an assignment.

Although it was a mountain of work including new terminology in the BI Glossary, the satisfaction of a finished project, new knowledge of modeling and analysis and website to show as a portfolio was very gratifying. “

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PTR Terms

Attached Glossary:

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PTR Course

The course exceeded my expectations. The course allowed me to dive a littel deeper in putting together the analysis with the data to discover patterns. in fact I was able to find a new discovery related to my own career while doing the week 4 discussion post. This may be a direction I want to elaborate more on for a final capstone project. The concept was on building better connections with better nodes (end points) to hierarchical structured organizations. this in itself reduces inefficiencies in communication which can reduce the amount of silos. This is a huge problem in my own organization due to a recent string of acquisitions and mergers. Overall a great class that will help me build on for data analysis.

[gmmadrid-blog]

Data Mining Course

How has the Data Mining course met your Mastery Journal Timeline monthly expectations and goals for this course?

This course far exceeded my expectations and I mean this with sincerity. The best way to describe the course is was the first time in the program where you were in the driver’s seat of the data collection.

When I first read the assignment I was not overly thrilled about it. However I have to admit that I really got into it. Although it wasn’t required I actually spent approximately 45 hours in researching data. I collected information from all 25 seasons based on the top 3 finalist of each season which totaled 75 individual positions and thoroughly read and applied it to the spreadsheet. The information gathering was a bit addicting and I wanted to find more correlations. After I read each Season I looked up each contestant and sometimes went further to find out if they were married or what their height was. I went on to include which types of dances (based on 17 unique styles) and which ones  were wining performances and which were scored in the bottom three loosing performances. From that information I was able to gather further analysis.

  What have you learned from the course content?

While gathering all of this information I learned the importance of cleaning my data. For example I would gather age of celebrity sex, height, married etc.  Then when I went to sort this information I noticed that I had two of everything because there was both a celebrity and a professional dancer who was partnered with that celebrity. Therefore it was important for me to put PD-Age and C-Age for Professional Dancer Age and Celebrity Age. Once this was done then I could move forward and filter or sort my data.

 How might you apply what you learned as you proceed through the Business Intelligence program and in your professional career upon graduation?

To save time and if given the opportunity I would spend some extra time thinking about the question “What is it I would like to prove?” I say this because I believe if I would have spent more time on that question I would have focused more on relevant data collection.

Once I knew what to collect I would spend some time on how I want to gather the data so that I collect and organize it correctly the first time.

I would also like to invest more time in learning how I could provide more effective visualizations then simple spreadsheet tables. I certainly spent the majority of time collecting good quality data, which I was able to interpret into a solid analysis, but I like the creative aspect of BI and I hope to project that creativity more in my selection of visuals.  

[gmmadrid-blog]

BIA

BIA was a great refresher for my statistics learning, since it had been several years since I had taken a statistics course in my undergraduate BSBA and MBA degrees. The course proved to be challenging in a positive way that made you really think twice about the depth of your research and how it could be applied to the project. From the very first project focused on new business venture in the country India to revising your plans, the course gave a real world perspective that I will certainly apply in my current position. This course will be one that I reference material from to generate analytics. Overall a solid class!

[gmmadrid-blog]

Glossary

Nominal Ordinal Interval Ratio Random Sampling Sampling Errors Mean Mode Range Variance Standard Deviation Empirical Rule Chebyshev’s Theorem Bayes' Rule Six Sigma  Lean Manufacturing Decision Tree Hypothesis Testing Type 1 and Type 2 Errors Regression Analysis Statistic versus Probability

Key TermText Chapter , or onlineDefinitionSourceNominalOnlineNominal basically refers to categorically discrete data such as name of your school, type of car you drive or name of a book. This one is easy to remember because nominal sounds like name (they have the same Latin root). http://www.usablestats.com/lessons/noirOrdinalOnlineOrdinal refers to quantities that have a natural ordering. The ranking of favorite sports, the order of people's place in a line, the order of runners finishing a race or more often the choice on a rating scale from 1 to 5. With ordinal data you cannot state with certainty whether the intervals between each value are equal. For example, we often using rating scales (Likert questions). On a 10 point scale, the difference between a 9 and a 10 is not necessarily the same difference as the difference between a 6 and a 7. This is also an easy one to remember, ordinal sounds like orderhttp://www.usablestats.com/lessons/noirIntervalOnlineInterval data is like ordinal except we can say the intervals between each value are equally split. The most common example is temperature in degrees Fahrenheit. The difference between 29 and 30 degrees is the same magnitude as the difference between 78 and 79 (although I know I prefer the latter). With attitudinal scales and the Likert questions you usually see on a survey, these are rarely interval, although many points on the scale likely are of equal intervals.http://www.usablestats.com/lessons/noirRatioOnlineRatio data is interval data with a natural zero point. For example, time is ratio since 0 time is meaningful. Degrees Kelvin has a 0 point (absolute 0) and the steps in both these scales have the same degree of magnitude.http://www.usablestats.com/lessons/noirRandom SamplingOnlinea random sample is a set of items that have been drawn from a population in such a way that each time an item was selected, every item in the population had an equal opportunity to appear in the sample. In practical terms, it is not so easy to draw a random sample.http://www.animatedsoftware.com/statglos/sgrandsa.htmSamping ErrorsOnlineThat part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a sample of values is observed; as distinct from errors due to imperfect selection, bias in response or estimation, errors of observation and recording, etc. http://stats.oecd.org/glossary/detail.asp?ID=2377MeanOnlinean average; a number that in some sense represents the central value of a set of numbers.https://medical-dictionary.thefreedictionary.com/Mean+(statistics)ModeOnlineThe mode represents the most frequent value in a set of data. For example in the set of data: 3,5,6,7,7,9,8,7,5,6,4,5,3,1 the number 7 is the mode. The mode doesn't have to be the center of a set of data and there can be more than one mode.http://www.usablestats.com/lessons/datacenterRangeOnlinerange is defined simply as the difference between the maximum and minimum observationshttps://explorable.com/range-in-statisticsVarianceOnlineThe variance is a numerical value used to indicate how widely individuals in a group vary. If individual observations vary greatly from the group mean, the variance is big; and vice versa.http://stattrek.com/statistics/dictionary.aspx?definition=VarianceStandard DeviationOnlinea measure of the dispersion of a frequency distribution that is the square root of the arithmetic mean of the squares of the deviation of each of the class frequencies from the arithmetic mean of the frequency distribution; also : a similar quantity found by dividing by one less than the number of squares in the sum of squares instead of taking the arithmetic mean https://www.merriam-webster.com/dictionary/standard%20deviationEmpirical RuleOnlineThe so called empirical rule states that the bulk of the data cluster around the mean in a normal distribution. In fact: 68% of values fall within 1 standard deviation of the mean 95% fall within 2 standard deviations of the mean 99% fall within 3 standard deviations of the meanhttp://www.usablestats.com/lessons/empiricalChebyshev's TheoremOnlineChebyshev’s Theorem tells us that no matter what the distribution looks like, the probability that a randomly selected values is in the intervalhttp://academic.regis.edu/jseibert/SumStat08/Descriptive/DC1-Chebyshev.pdfBay's RuleOnlinea formula that describes how to update the probabilities of hypotheses when given evidence. It follows simply from the axioms of conditional probability, but can be used to powerfully reason about a wide range of problems involving belief updates.https://brilliant.org/wiki/bayes-theorem/Six SigmaOnlineSix Sigma at many organizations simply means a measure of quality that strives for near perfection. Six Sigma is a disciplined, data-driven approach and methodology for eliminating defects (driving toward six standard deviations between the mean and the nearest specification limit) in any process – from manufacturing to transactional and from product to service.https://www.isixsigma.com/new-to-six-sigma/getting-started/what-six-sigma/Lean ManufacturingOnlineThe core idea behind lean manufacturing is maximizing customer value while minimizing waste, thereby achieving manufacturing excellence through the creation of more value with fewer resources.https://www.qualitydigest.com/inside/twitter-ed/introduction-lean-manufacturing.html#Decision TreeOnlineDecision trees are produced by algorithms that identify various ways of splitting a data set into branch-like segments. These segments form an inverted decision tree that originates with a root node at the top of the tree. http://support.sas.com/publishing/pubcat/chaps/57587.pdfHypothesis TestingOnlineProcedure for deciding if a null hypothesis should be accepted or rejected in favor of an alternate hypothesis. A statistic is computed from a survey or test result and is analyzed to determine if it falls within a preset acceptance region. If it does, the null hypothesis is accepted otherwise rejected.http://www.businessdictionary.com/definition/hypothesis-testing.htmlType 1 and Type 2 ErrorsOnlineA type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2996198/Regression AnalysisOnlineRegression is a statistical technique to determine the linear relationship between two or more variables. Regression is primarily used for prediction and causal inference.http://statlab.stat.yale.edu/workshops/IntroRegression/StatLab-IntroRegressionFa08.pdfStatistic versus ProbabilityOnlineProbability and statistics are related areas of mathematics which concern themselves with analyzing the relative frequency of events. Still, there are fundamental differences in the way they see the world: • Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events.    • Probability is primarily a theoretical branch of mathematics, which studies the consequences of mathematical definitions. Statistics is primarily an applied branch of mathematics, which tries to make sense of observations in the real world. https://www3.cs.stonybrook.edu/~skiena/jaialai/excerpts/node12.html

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Reference

Chronic condition. (2017, November 07). Retrieved November 19, 2017, from https://en.wikipedia.org/wiki/Chronic_condition

Schimpff, S. MD (2017, March 29). How Many Patients Should A Primary Care Physician Care For? Retrieved November 19, 2017, from https://medcitynews.com/2014/02/many-patients-primary-care-physician-care/?rf=1

Tinker, A. (2017, October 25). How to Improve Chronic Diseases and Comorbidities. Retrieved November 19, 2017, from https://www.healthcatalyst.com/how-to-improve-chronic-diseases-comorbidities

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Conclusion

Optum, the world’s largest healthcare company, has partnered with the Centralized government of India to tackle the biggest chronic condition intervention in the company’s history. The scope of the project was to reduce chronic disease using BI tools to providers and provide recommendations based on quality data that has been gathered and submitted from medical claims to the government. To initiate such an undertaking studies were conducted to determine factors of influence such as population, most prevalent chronic diseases, resources needed, and communication. Once these conditions were analyzed a pilot project was initiated in Uttar Pradesh which held a population of approximately 2 million people. The study provided operational data and analytics that could be turned into information that allowed Optum to restructure its resources, staffing hours of operation and quality measures to better serve the needs of the patients.   

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Process Analysis

Process Flow

In the Optum India project data is collected from 5 sources of documentation. This documentation includes: (1) patient history and health questionnaire, (2) providers history and exam SOAP notes, (3), physician orders (lab work, x-rays referrals etc.) and referrals (4) test results or findings from the orders or referrals. While these sources provide the documentation needed to gather information, the process itself is not complete until the follow up visit happens. This is where the BI data helps to reveal what is going on in the plan of care and health outcomes. In the follow up visit the provider can review trend analysis for vital signs such blood pressure and weight monitoring, or review the labs to gauge the patients cholesterol and sugar levels which may lead to further diagnosis of chronic conditions and new treatment plans to be carried out for possible dietary and lifestyle changes. If the patients were in fact diagnosed with chronic conditions or comorbidities they would need to return to the clinic in less than six months and the very least within one year in order to monitor the clinical outcomes.  

The current process flow is inefficient, we have determined from the Centralized government the board of Nursing has agreed to put the clinical rotations as part of the practicum. In addition the government has put into place initiatives to outsource Nurses and Nurse Practitioners into the staffing model for each clinic. The following illustrations reflect these changes from current 2017 to proposed 2018.   

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Six Sigma

Staffing hours

Determining the right staff to have was not the only solution. The Optum India project also had to analyze when the highest amount of staff should be available in the clinic. To determine these results we evaluated 12,000 patients over the course of 4 weeks. We looked at the hours which saw the highest amount of patient volume beginning in the morning from 8:00 AM till close which was 4:00 PM.

  From this data we determined that the highest volume of patients was surprisingly around the time we had previously taken for lunch (Mean 12:00 PM). Schedule changes were made to accommodate the highest impact. Standard deviation for having the highest volume of patients (+3 or -3) was as early as 9:00 AM and as late as 3:00 PM. additionally we determined that Mondays and Thursdays were our busiest days of the week.