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So, What is the best Python Integrated Development Environment (IDE)? This question gets asked all the time. The quick answer is... “It depends”. What problem are you trying to solve and where in the CRISP-DM methodology are we operating? Figure 2. CRISP-DM Methodology Some IDEs are better for the Data Understanding and Data Preparation piece while some IDEs are better in the Modeling, Deployment and sharing analysis piece. We actually have three architecture options for Python development – command line, IDE, or Notebook. For tool selection, we need to look at which part of the data science process we are in and how well the tool meets our trade-offs between cost, quality, and time to market. For example, in the data cleansing phase of a project you may just need to use the command line. There are many benefits to this. One great use case for using the command line is maximizing your memory resources with parallel processing for large data sets (see Article by Adam Drake). Python shell scripts work as a great lightweight tool to parallelize existing memory resources. However, if we want to integrate these tools into the data exploration and model-building phase of the projects as well as reuse these tools in other applications – we are going to need an Integrated Development Environment (IDE) for development. IDE’s provide the features for authoring, modifying, compiling, deploying and debugging software. There are a multiple number of IDEs out there and I have experimented with several. I’ve tried Yhat’s Rodeo platform (released after the stackoverflow spreadsheet (Figure 1) was put together), Spyder, PyCharm, Jupyter, and RStudio. I have also done extensive research on stack overflow and various data science blog reviews. My best source however was the Operation Code slack channel. Operation Code is the largest community dedicated to helping military veterans and families launch software development careers. Great content and collaboration for any military veterans transitioning to software development careers. (https://operationcode.org) Here are my thoughts: For Python development and initial code syntax training, you want PyCharm or a similar IDE with Intellisense. PyCharm and Intellisense help new developers with syntax and proper formatting techniques. Intellisense is intelligent code completion and a few IDEs offer this. I was fond of the four Python IDEs that I directly worked with and tested. I thought they were all very easy to use with Yhat’s Rodeo and PyCharm my overall favorites. Yhat has a great data science blog (http://blog.yhat.com) that initially brought me to Rodeo. Ultimately, I had to use PyCharm for a class and stuck with it due to its overall functionality, nice layout, and ease of use. Figure 3: PyCharm Example In Figure 3, our PyCharm example, we see an example of Python code with the yellow highlights indicating Python best practices for syntax. The lines on the right margin indicate severity of the issue by color-coding and where there are conflicts. Yellow indicates a best practice for format tip. If lines to the right were red, we would have a syntax or logic issue causing our code not to run. For data understanding and data preparation, we are going to want something similar to RStudio, Spyder, or Rodeo. The positives with these IDEs are having a variable explorer view so you can see what variables are stored and can double click to view the underlying data and Rodeo automates or at least makes saving the images from graphs very easy. I like RStudio the best due to the ease of use switching between Python, R, and SQL. The ability to move seamlessly between the R and Python in a single environment is particularly useful for cleaning and manipulating large datasets; some tasks are simply better suited to Python, and others to R. One additional benefit to RStudio and Jupyter notebooks is how the code executes in memory. PyCharm, Rodeo, and Spyder have to import packages each time you execute code and some dataframes can take a while to load. With RStudio and Jupyter notebooks it is all in memory so minimal lag time. It is also very easy to share analysis and demonstrate findings. Another great feature of RStudio is the ability to convert notebook and analysis to slides with a simple declaration in the output line: • beamer_presentation - PDF presentations with beamer • ioslides_presentation - HTML presentations with ioslides • slidy_presentation - HTML presentations with slidy • revealjs::revealjs_presentation - HTML presentations with reveal.js Figure 4: RStudio Notebook IDE With ‘reveal js_presentation’ Slide Output My preferred method for new functionality is to develop and test large functions in PyCharm and then move to RStudio notebook for data exploration and building analytics pipelines. You can actually cut and paste Python code directly into R Markdown. All you have to do is tell R Markdown what type of ‘chunk’ to run. For Python: ```{python} … For SQL: ```{r} library(DBI) db <- dbConnect(RSQLite::SQLite(), dbname = "chinook.db") query <- "SELECT * FROM tracks" ``` ```{sql, connection=db, code = query} ``` Note: A future blog post will talk about the convergence in functionality on large datasets between Structured Query Language (SQL) and the R package ‘dplyr’. Figure 5: An example of Python running in an R Markdown document inside the RStudio Notebook IDE For model development and final deployment – here it depends on the size of the dataset and whether or not we will need to use distributed processing with Spark. If we have a large amount of images or any other type of large dataset, we should use Spark’s Databricks platform. Databricks works interactively with Amazon Web Services (AWS) to quickly set up and terminate server clusters for distributed processing. Figure 6. Databricks Notebook Workspace Databricks also automates the install of software packages and libraries to the Amazon cluster greatly decreasing environment setup and configuration time. Figure 7. Databricks Spark Deep Learning Package With the Databricks Community Edition, users will have access to 6GB clusters as well as a cluster manager and the notebook environment to prototype simple applications. The Databricks Community Edition access is not time-limited and users will not incur AWS costs for their cluster usage. The full Databricks platform offers production-grade functionality, such as an unlimited number of clusters that can easily scale up or down, a job launcher, collaboration, advanced security controls, JDBC/ODBC integrations, and expert support. Users can process data at scale, or build Apache Spark applications in a team setting. Additional pricing on top of AWS charges is based on Databricks processing units (DBUs). Figure 8. Databricks Pricing Model (https://databricks.com/product/pricing) Figure 9: Databricks Pricing Example for Production Edition You will need to balance the time saved with Databricks versus the cost of analysts setting up the same environment with other tools but the automated Spark and AWS cluster integration make this a wonderful IDE to work with. Conclusion My top picks... If going to develop a custom algorithm or a custom package in Python – PyCharm If performing data exploration, building analytics pipelines, and sharing results – RStudio If you have a large dataset for Spark distributed processing - Databricks Please comment with your command line/IDE/Notebook best practices and tips. *Jerome Dixon is a valued Senior Operation Research Analyst at CANA Advisors to read more Python articles by him and other members of the CANA Team visit the CANA Blog. #Databricks #RStudio #PyCharm #Spark #DeepLearning #codeexample #Python #IDE #Spyder #stackoverflow #YhatRodeo #CRISPDM

"Can I view that in Excel?" The capabilities of R programming are expanding. Fast. From publication-quality graphics with ggplot2 to the capability to handle large scale computing with Apache Spark, the analytics community embraces R as a core environment. At CANA Advisors, we use the latest developments in order to deliver the fastest, most adaptable solutions. For clients, results need to be in a form that is easy to process by any member of their team-- with little to no learning curve. As analytics professionals, how can we ensure the best of both worlds? That is, state of the art solutions that produce results in the familiar form clients seek. In this post, I'll go over one such method: using R programming to export the results of a Shiny analysis to Microsoft Excel. For those not familiar with Shiny, it is a package to create interactive, aesthetically pleasing web apps with all the statistical capability of the R programming language. This brief tutorial will utilize the Shiny and XLConnect packages in R. The Method In this example, we'll be working with the iris data set [1], which contains information about the dimensions of different instances of various iris flower species. For the purpose of this tutorial, we'll assume we already have a functioning Shiny app and the data structures we are interested in saving. In this case, the data we'd like to store is reactive in nature. This means, it will change with user inputs. You can recognize calls to reactive expressions in the code below by their distinctive form expression(). To export a worksheet: 1. Lay the groundwork: Create the download button, workbook, and worksheets. 2. Assign the data frames to the worksheets. 3. Save and download. The Result The above process will take us from a shiny app like this: To an excel file like this: The Implementation # Load the shiny and XLConnect packages library(shiny); library(XLConnect) # Create and label the download button that will appear in the shiny app renderUI({ downloadButton("downloadExcel", "Download") }) output$downloadFile <- downloadHandler(filename = "Iris_data.xlsx", content = function(file) # Name the file fname <- paste(file, "xlsx", sep = ".") # Create and assign names to the blank workbook and worksheets wb <- loadWorkbook(fname, create = TRUE) createSheet(wb, name = "Sepal Data") createSheet(wb, name = "Petal Data") # Write the reactive datasets to the appropriate worksheets writeWorksheet(wb, sepal(), sheet = "Sepal Data") writeWorksheet(wb, petal(), sheet = "Petal Data") # Save and prepare for download saveWorkbook(wb) file.rename(fname, file) }) To learn more about any of the features discussed above, use the ?topic feature in R. A more comprehensive overview of shiny is provided by RStudio here. Lucia Darrow is an valued Operation Research Analyst at CANA Advisors to read more R articles by her and other members of the CANA Team visit the CANA Blog. [1] In R, type ?iris to learn more than you would ever want to know about it. #R #Rstudio #shiny #XLConnect #ShinnyApps #codeexample #RStudio #programming #graphics #ggplot2 #LuciaDarrow

Now that we are halfway through 2017, we thought it would be a good time to provide an update on what the CANA Foundation has been up to this year. We started off with a bang – officially launching CANA Foundation January 1, 2017. What began as a key component of the founding of CANA Advisors has grown to a fully functioning element of the company, focused on giving back to the communities that the CANA Team lives and works in each day. Gathering for Women CANA Foundation has taken on two initiatives so far, this year. The first was spear-headed by Harrison Schramm, one of our Principal Operations Research Analysts, who saw an opportunity to give back to Gathering for Women, a Monterey, California-based non-profit organization. Gathering for Women’s mission is to serve the needs of homeless women and help them transition out of homelessness. They had a need for better managing the records of their clients to support grant writing, responsibility to donors, and ensure fairness in distributing resources. Harrison jumped at the chance to use his skills and develop a solution to their existing spreadsheet method of record-keeping, which was complex, contained redundant information, and was prone to inaccuracy. Harrison created an application that solved all those problems. In the application, the information can now easily be edited by most computer users, contains accurate records of their clients, and significantly reduces the chance of inadvertently changing existing information. This initiative is truly a win-win scenario, where a CANA team member was able to use valuable skills to help Gathering for Women take care of some of their administrative tasks accurately and efficiently; freeing them to focus on taking care of and helping the homeless women in the Monterey area! Camp Schreiber We are thrilled to tell you about Camp Schreiber Foundation, a non-profit based in Wilmington, NC, that CANA Foundation has recently begun to support! Focused on growing and mentoring our young men, Camp Schreiber is centered around a one-week camp in July each year where campers focus on team work, character building, educational goals and leadership through a variety of activities. During the remaining 51 weeks of the year, Camp Schreiber provides tutoring, mentorship and extracurricular activities to the campers. Campers are accepted into the Camp Schreiber program through a competitive process, beginning in middle school, with the ultimate goal of preparing them to successfully attend and graduate from a four-year for college and to become future leaders in their communities. What is so exciting about partnering with Camp Schreiber is the opportunity to invest in the development and mentorship of young men who will one day be leaders in the military, business, civic, and political organizations in their communities! We specifically contributed financially to Camp Schreiber’s incredible tutoring program, which is vital to helping these boys stay on track scholastically through their middle and high school years. In addition, our own Kenny McRostie has become personally involved with some of the “51-week” extracurricular activities. He has begun to mentor some of the campers and is already providing a positive, male role model to the group. We look forward to establishing a long-term relationship with Camp Schreiber and seeing the positive results of this wonderful organization’s work. Who knows, maybe one of these bright, young men will be a future CANA Advisors team member! If you have any questions about the CANA Foundation, its initiatives and partnerships, please reach out to Kenny McRostie, CANA Foundation Manager, at kmcrostie@canallc.com or visit our website at http://www.canallc.com/giving-back. #CANAFoundation #GatheringforWomen #CampSchreiber #mentorship #mission #outreach #givingback #51week

What do Analytics Professionals do? In 2017, the website Glassdoor.com ranked the following analytical career fields: Data Scientist #1, Data Engineer #3, and Analytics Project Manager #6. Do college students know what these professionals do on a typical day? There are TV shows with nurses, doctors, police, firefighters and lawyers but there are not any shows that focus on analytics professionals. My challenge over the past few months was to create and deliver a presentation to inform potential future analytics professionals. My presentation title was "A Day in the Life of an Analytics Professional." Over two months, I delivered the presentation to the NC State Sports Analytics and the Math Club, UNC Math Department and finally, during the UNC-Wilmington Cameron Business School Business Week Event. Giving the brief multiple times allowed for refinement and adjustments based on student questions. Walt DeGrange giving the presentation So what does a typical day look like? Research - 10% Keeping up with the art of the possible is a required daily chore. New methods and technologies are being introduced daily. Assuming a software math solution implemented six months ago is still state-of-the-art is risking irrelevance. Coding - 10% This is the basic skill required for all analytics professionals. As important as the carpenter's tools, coding in various languages such as R, Python, C++, and SAS allows the analytics professional to manipulate and gain insight from data sets. Communication - 25% Communicating with collaborators, clients, project leads, and technical experts is critical to ensure that deliverables are on time and fulfill the requirement. Marketing - 15% Everyone needs to sell. Even the coder that never presents to a client must convince their project lead that their methodology works. This is a very important skill for analytics professionals since many models use math that is not easily understood or explained. These "black box" solutions require a higher level of convincing. Project Management - 30% Keeping analytics projects on track is not like managing a construction project. There are many analysis areas that require familiarization with the data before building a model. Many aspects of model building are more of an art form then a science and thus the time to complete may have a large variation from project to project. One must consider this in the planning and execution of these projects. Breaks - 10% Everyone needs a break, and this is especially true if your job keeps you in front of a computer screen. Walking, running, and cycling give me time and space to think about challenges. Sometimes your unconscious mind needs this distraction to develop solutions. Plus the physical exercise is good for you. Of course, this is just a sample day. Why I love analytics is that I can apply the techniques across many industries and solve a multitude of challenges. This results in schedule variation every day. Also, my role these days falls more on the project management side. I would guess a technical analytics professional would spend 30% or more of their time coding and less on project management. Overall, the feedback from the students was positive. Many students were glad to learn about what to expect if they choose an exciting career in analytics. If you would like to take a look at the brief, it is available at https://www.slideshare.net/ltwalt/day-in-the-life-analytics-professional #analyst #analytics #datatype #researchanalyst #bigdata #workenvironment #workload #coding #communication #marketing #project #management #dayinthelife

The inaugural Military Operations Research Society (MORS) Emerging Techniques Special Meeting (METSM) was a new event for the National Security professional association and was held at the Hilton Mark Center in Alexandria, Virginia on December 6th and 7th. It was exceptionally well-attended with 128 total registrants. It highlighted Plenary Sessions from luminaries such as Les Servi, Jiawai Han, and Karla Hoffman as well as in-depth technical sessions. A summary of the meeting and selected sessions will appear in the March issue of Phalanx. The CANA Advisors team at METSM from left to right; Norm Reitter, Harrison Schramm, Carol DeZwarte and Walt DeGrange. #MORS #METSM #operationsresearch #emergingtechniques #CANAAdvisors #workshop

There are numerous supply chain disruptions that can occur on any day, at any time. Have you sat down and thought about all of the possibilities - natural disasters, weather, terror attacks, politics, foreign government policy changes, labor strikes, infrastructure disaster, contaminated food, and more. If not, you should get started. If yes, do you have a plan to manage these risks? While most of these types of disruptions cannot be prevented or stopped, having a plan in place in the event that they occur will help to minimize a disruption in your supply chain. Companies that have these plans in place have successfully made it through major disrupting events while others suffered the consequences. There are three overarching steps in risk management for supply chain disruptions. First, the company must sit down and think of all of the potential disruptions, from internal to external, from the beginning of the supply chain to the end, and from organizational to operational. Second, the risk has to be quantified somehow. It is not possible to predict the likelihood of most of these events, but you can determine a good ballpark estimate on likelihood for each event. Also quantify it by applying a dollar value to the potential loss. Or, maybe it is not a dollar value, but it is product quantity or the number of customers lost due to the event. For instance, if you sell food with salmonella, and that gets into the news, you may lose customers due to trust. What is the overall impact? The third step is to make a mitigation plan for each of the events. To get started, begin with the highest impact event from the previous step. Which one will hurt the most? Make a detailed plan and move on to the next one and so on. Now that you have the plans, communicate them. A plan will do no good if it is written, sits on a shelf and is never looked at. Communication is key when a major disruption occurs. First, all concerned or those who have a role in the plan must be aware of their role so that they can act right away when it happens. Second, communication during the disrupting event is crucial. As soon as it happens, everyone along the supply chain should be aware so that they can act on their part of the plan. This applies to all levels of the organization as key personnel will have to make decisions, and management and other personnel will have to carry out the plans at the operational level. While this is just a high level overview, there are many resources out there to help with risk mitigation/management and deriving these plans. Some sources include APICS Operations Management Body of Knowledge, the Supply Chain Operations Reference Model (SCOR), and NC State's Supply Chain Resource Cooperative. There are many, many articles out there aside from these sources. Bottom line, the resources are available, and if you have not prepared your company from a major supply chain disruption yet, you should. You do not want to lose your success or suffer a major setback in a matter of moments if an event should occur. If you have plans in place already, have you communicated the plans thoroughly? Do all involved know what to do the moment a disruption occurs? Have you considered any new threats since the plans were made? It is a good idea to update these plans every few years for new threats, remove old ones, update for changes in processes, update impact value, etc. You cannot prevent or even predict most of these major supply chain disruption events, but you can be prepared for them! #supplychain #disruptions #risks #operations #plan #KimMamula

It was a beautiful sunny September day in New Bern, NC. I trained all summer for this ride. I was on the starting line with 2,000 other cyclists waiting to begin the first day of a two day BikeMS charity ride. The goal of the event was to raise money to fund research to find a cure for multiple sclerosis. My goal over the next two days was to survive a 50 mile ride on Saturday and followed by a 75 mile ride on Sunday. As I leaned against my bicycle waiting for the start of the event, I realized that the event organizers were sending out riders in waves approximately every five minutes. This stimulated my OR brain into thinking about why organizers would do this. I quickly decided to use simulation to answer my question; formulating this model kept my mind off the 125 miles ahead. After successfully completing the ride (thanks to everyone who supported me, especially the CANA Foundation!) I built a simulation in ExtendSim. The actual New Bern BikeMS event had four riding distances a day (30, 50, 75, and 100 miles) and multiple rest stops. I simplified the model to demonstrate the effects of the wave start. A diagram of the model is in Figure 1 below. Figure 1. ExtendSim simulation model with seven ten mile cycling segments and six rest stop queues Simulation parameters One day event Two scenarios 2,000 riders mass start 10 waves of 200 riders released in 5 minute intervals 6 rest stops 70 miles (7x10 miles segments) 1,000 simulation runs for each scenario Simulation Variables Departure times for the cyclists. There is a 0.9 second separation between cyclists crossing the start line. Given this gap it will take 30 minutes for 2,000 cyclist to start in one mass wave and approximately 50 minutes for 10 waves of 200 cyclist departing at 5 minute intervals (still individually at 0.9 seconds). Rest stops have a constant service rate of one rider every 3.6 seconds. This equates to 500 cyclists over a 30 minute time frame. Some riders stop to fill a water bottle, others for food and many to use the bathroom. Bicycling time distribution for each 10 mile segment is a triangle distribution with a minimum of 30 minutes (20 mph average), maximum of 60 minutes (10 mph average) and a most likely of 40 minutes (15 mph average). Remember this is a charity ride and not the Tour de France. There is a probability of 0.5 for a cyclist to stop at a rest stop. Therefore each rest stop should expect approximately 1,000 cyclists during one day. For those lucky few that ride in charity cycling events, you know that the first rest stop is usually overwhelmed (especially the bathrooms). So what were the results of the two scenarios? The results are very convincing and displayed in Table 1 below. Starting in waves reduces the average queue wait time from 10 minutes to under a minute. The max line for Rest Stop 1 with the 2,000 cyclist start was 335 cyclists. With the 200 wave start, it was 11 cyclists. Table 1. Results for Rest Stop 1 for the two starting wave scenarios. The Wait times are in minutes. By the time cyclists reached Rest Stop 2, the riders had spread out enough to eliminate any queuing at Rest Stops 2 through 6. Given that the wave starts take slightly longer to implement at the beginning of the day (40 extra minutes for all cyclists to start), the overall performance of the wave start strategy is impressive. This strategy reduces the workload on Rest Stop 1 and decreases the time cyclists spend on the course as displayed in Table 2 below. And, for us cyclists, we are always infinitely happier pedaling our bicycles than standing in line for the bathroom. Table 2. Results for all cyclists completing the 70 mile ride with six rest stops. I did stop both days at Rest Stop 1 and was impressed with the short lines. Perhaps the BikeMS event organizers had figured out the wave start method through years of experience or maybe some OR designed a simulation for them…. #BikeMS #charity #CANAFoundation #NewBern #cyclisit #event #ExtendSim #simulation #OR #analyst #operationsresearch #WaltDeGrange

The International Organization for Standards (ISO) was founded in 1947. The organization promotes worldwide propriety, industrial and commercial standards. [https://en.wikipedia.org/wiki/International_Organization_for_Standardization]. In 1987, the ISO 9001 standards for quality were established. These were a set of standards for quality assurance in design, development, production, installation and servicing that is internationally agreed upon. Many major purchasers require suppliers to hold this certification. Also, when implemented, these standards can increase efficiency and reduce re-work within a company positively affecting the bottom line. I recently attended the ISO 9001:2015 Requirements and Lead Auditor Course, and it was presented in three (3) distinct parts: Quality Management Systems - ISO 9001:2015 Requirements which included ISO 9000:2015 - Management Systems Fundamentals and Vocabulary Managements Systems Auditing - ISO 19001:2011 Leading Management Systems Audit Teams - ISO 19001:2011 The class consisted of various companies and manufacturers - there were representatives from Mass Transit from Champagne/Urbana, S&S Cycles from Wisconsin, Fincantieri Marinette Marine, a shipbuilding company from Michigan (U.S. Navy shipbuilder), a food grade chemical company, and CANA Advisors. The corporate knowledge of the ISO standard ranged as wide as the various companies who were represented; some had no knowledge or experience, a few had a minimal knowledge with the standard as it applied to their workplace, and others, such as myself and the quality director for Fincantieri, were those who had knowledge from the 1987, 2000 and 2008 standards. The lead instructor was one of the most knowledgeable instructors I had come in contact with. He has been in "Quality" for more than 30 years and in fact, is on the committee for appeals and was part of the group responsible for the current version of the standard. It was a excellent first two days of the course going through all the new clauses and changes to the standard. This overview was followed up by practical applications to further ones knowledge of the standard. The third day of the course was focused on the review of the ISO 19011 Auditing fundamentals for the people who were going to be involved in internal auditing of their companies. This was a down and dirty overview with many activities pertaining to the auditing process. The fourth day of the course was specifically for lead auditors who are going to be involved in internal, supplier and registrar audits. This part of the course was a more deep dive into auditing techniques, team lead responsibilities, and managing the audit program. Quality training is constant and continuous! This course provided the necessary tools for CANA to self-assess and improve its internal processes and also to provide guidance to other companies that are interested in learning more about how ISO quality standards can help their processes. If you have any questions about the ISO 9001 standards or CANA Advisors, feel free to reach out to me at keades@canaadvisors.com. #ISO #ISO9001 #standards #quality #requirements #auditing #learning #CANAAdvisors

It is somewhat difficult to convey the spirit of Camp PARC to someone unfamiliar with it. When describing the communal shower house and lack of air conditioning, the squeaky beds and sharing a cabin with up to 35 other people, plus whatever wildlife critters invite themselves in for the night, most people don't match my enthusiasm. It’s hard to put into words an experience so fulfilling, self-validating, yet humbling. A person so innocent, one who is open to your kindness, seeks nothing, yet is overwhelmed by your compassion and companionship, is someone truly worth meeting and making smile. Through Camp PARC we have 100+ of those people. One of the core values of CANA Advisors is to balance work and life through flexible working hours, encouraging each of us to pursue passions and focus on our personal goals. In July I chose to invest my time at Laurel Hill State Park in Somerset, Pennsylvania and volunteer for a local non-profit organization - Camp PARC. 2016 was my tenth year of service with the organization. Camp PARC is a privately funded service organization "[that] creates recreational and social opportunities for children and adults with mental and developmental disabilities (Down Syndrome, Fetal Alcohol Syndrome, severe autism, etc.) through its residential summer camp." With a mission to provide summer camp experiences to these special needs "campers," Camp PARC hosts three (3) unique weeks of camp: 7-day Main Camp is for anyone over 18 and has been the traditional format of the camp for over 50 years. 5-day Adult Mini Camp for those over 18 who cannot, or may not want to, keep up with the faster paced week-long camp 4-day Youth Mini Week for the younger crowd to experience their first time away from home overnight and start building new relationships. This year, a total of 130 campers attended our camp season. To add additional awe to the very special mission of Camp PARC, the camp is run ENTIRELY by volunteers and donations. From the cooks to the Directors, dishwashers to the very important nurses, and everyone in between, hundreds of people flock to this camp annually to lend a hand. The most important people, though, are the junior staff. These positions are filled by local young adults (ages 13-21) who volunteer a week (or three!!) of their summer to be paired with 1 - 2 campers. They provide 24-hour care for the duration of the camp session. Junior staff are responsible for serving campers food, keeping them safe, and ensuring all their needs are met, while also encouraging them to participate in activities, go outside of their comfort zone, and have fun. For a teenager who is just coming into their own as well, the interactions between junior counselor and camper is something very special. Day-to-day camp activities are organized and executed by the Senior Staff (this where I fit in!). We are adults over 21 and most of us have previously served as a junior counselor during our high school years. Even as jobs and families force us away from Western PA, several of our staff make the journey annually, traveling from Texas, Missouri, Alabama, Virginia, and all across Pennsylvania. In 2014 & 2015, I traveled in from Afghanistan because this camp is that special. Koa Beam, another CANA Advisors team member, was gracious enough to volunteer one morning and experienced first-hand the spirit of Camp PARC. "I just thought it was a great thing and seeing the kids you guys help out with is a daunting task, but so needed. My hat goes off to the people who volunteer a portion of their summer to go help-out. I just hope that my small contribution of time put a smile on at least one or two kids' faces and that they enjoyed my time there as well. It couldn't have been a nicer day." How many of us miss out on these kinds of people, opportunities, and smiles? It is amazing what happens when we put down our phones, walk away from our computers, and live in life’s beautiful and inspiring moments. Somehow, our ships sail straighter when we invest in others and balance ourselves. For more information about Camp PARC, please visit their website: http://campparc.org/ #camp #PARC #CANAAdvisors #volunteers #community #kindness #team #summer #connection

Mix sports analytics, sunny weather, a beautiful campus, interested students, and great speakers and what do you get? The 2016 Carolina Sports Analytics Meeting (CSAM) of course! CANA Advisors, along with ESPN, Furman, and National Amateur Sports, sponsored this event. The plenary speakers were David Kaplan, Director of Analytics for the Charlotte Hornets, and Peter Keating, writer for ESPN the Magazine. Other speakers and attendees ranged from high school and college students to directors of analytics for professional teams. CANA presented a NHL sports analytics project and how technology was overwhelming sports with a tidal wave of data. A lot to pack into a one day conference. David Kaplan’s talk kicked off the conference. His presentation centered on how to make analytics actionable. He told a story that occurred during his time as an intern with the Portland Trailblazers. He told an assistant coach that his analysis indicated that a certain player was shooting poorly in the third quarter. The coach turned to David and told him "Well what do you want me to do, tell the player to shoot better?". Analysis must support a decision to change behavior at either the management, coaching or player level. In the NBA, analytics is applied to defense by predicting what side of a defender the ball handler prefers and is more successful at scoring. With this knowledge the defender can attempt to force the ball handler to cut to the non-preferred side. David also stressed the fact that basketball is not a sport that major adjustments can be made by either coaches or players during the game. The pace of play and the short break lengths (timeouts, breaks between quarters, etc.) force small changes. This means the analytics products are delivered before the game and are rarely updated during the game. Another interesting point was that David was surprised how much writing is required by his position. He communicates almost all of his analysis by writing reports and providing them to coaches and players. This consumes a large portion of his day. A popular event during the lunch break was the student poster presentations. This event gave students an opportunity to present and discuss their research. Subjects ranged from the effectiveness of randomizing softball pitches to ranking NBA players to a new method to find the overall best lifter at a power-lifting meet. Everyone learned something new, and the students got valuable feedback on their research. Peter Keating closed the conference with an interesting talk on predicting NCAA Tournament upsets. He has worked the past few years with professors from both Furman and Davidson Universities on a model to predict when these upsets will occur. The model uses logistic regression techniques, and he reviewed the advantages and disadvantages of using this method. He recommended exploring other methods to compliment predicting these unlikely events such as common opponent neighborhood network analysis. Since all teams in the NCAA don't play all other teams, this method would leverage graph theory to tease out the probabilities using a common opponent approach (Team A plays Team B and Team B plays Team C, therefore there is a connection between Team A and Team C). He also stressed the importance of mathematical methods to find similar games. This would include parameters such as seeding of the two teams and the classification of what type of giant (favorite) and giant-killer (underdog). The presentation highlighted some important points in identifying rare events that could be applied to other areas outside of sports such as risk analytics. Overall the conference was very successful. The smaller size of the conference ensures that everyone gets to interact with speakers and with each other. Also, April in Greenville, SC is beautiful. There were no less than two weddings, one 5K run and three other events occurring at the same time on the Furman campus. Already looking forward to next year! #CSAM #analytics #sports #informs #CANA #college #professional #ESPN #NCAA #WaltDeGrange

Please Welcome our Newest CANA Team Members! We pride ourselves on building and retaining a team with both deep technical expertise and multi-faceted experiences. Adding to our current team of outstanding professionals in both operations logistics & analytics, we are proud to announce our newest CANA team members. They bring immediate value and expertise to our clients, and quite simply they are a joy to work with! Please welcome Kurt Eades, Jason Brown, and Ashlee Knapp to CANA Advisors. Kurt Eades is a seasoned former United States Marine with career experience in logistics operations, explosive ordnance disposal (EOD), systems planning, quality assurance, inventory control and auditing. He joins CANA as a Senior Logistics Analyst. Kurt's LinkedIN profile Jason Brown is a long standing contributor to the Marine Corps family providing technical expertise and training in logistics, material management, warehousing and material handling equipment operation, and logistics automated information systems to the Marine Corps Logistics Command in Albany, GA. He joins CANA as a Software Developer. LinkedIN profile Ashlee Knapp has recent international experience working in Afghanistan, and she brings hands-on operations research analysis, information technologies, and logistics expertise to our growing team. Ashlee joins CANA as an Operations Research Analyst. Ashlee's LinkedIN profile Welcome to CANA! #logistics #operationsresearch #softwaredeveloper #CANAAdvisors #Kurt #Ashlee #Jason #team #analytics

The word analytics is floating everywhere these days. It's unavoidable if you work in an industry that uses a lot of mathematics, simulation, modeling, statistics, numerical analysis, and a broad range of information technology-based solutions. But what if you don't? What if you're a law firm? A plumbing supply store? A small manufacturer? If you're not surrounded by numbers all day every day, you might think you have no need for analytics and the need to harness and analyze your data. These words may mean nothing to you by themselves. But it's surprising how new insights can arise from applying advanced techniques in places of your business where they haven't been applied before. A law firm might use data mining to predict what times of year different types of cases come in to assist in planning workloads for staff with different skills. For example, they might find patterns in their customer data indicating more divorce cases come in the several months after taxes are due, requiring a surge in the associated legal skill set. A supply store can use predictive modeling to fit a broad range of forecasting algorithms to their demands. The better they predict demand, the smarter decisions they can make about inventory and ordering—saving time, saving money, and potentially reducing lost money on stock that doesn't move. A small manufacturer can do statistical analysis of equipment breakdowns vs. line speed. They might find that increasing line speed to try to increase output actually decreases output because it causes more machine breakdowns and maintenance requirements. In a recent case, CANA had the opportunity to investigate intellectual property protection for a manufacturer of specialty products. Intellectual property protection relates strongly to information security, which is ripe for analytics. Predictive models help call out unusual behavior in shared drive file access, internet access from outside countries, or even employee behavior with badging in and out of a building or room. These help increase security and information protection, and protect overall profits. "Analytics" is one of those buzzwords that is so prevalent right now you may wish you never hear it again. But look beyond the buzzword to the types of business intelligence you could gain from digging into your data, and you might be surprised at the opportunities. Our CANA team would be delighted to join you in the dig. We want to hear your comments on how your organization is using analytics, and we encourage you to give us a call at 703-317-7178 or comment for more information on how we can join forces. #bigdata #analytics #law #predictive #manufacturing #analysis #statistics #CANA #simulation #modeling #CarolDeZwarte