Was A. Philip Randolph’s March on Washington movement successful? How did it relate to the Double V campaign?

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Choose one of the below for your main post. Respond to at least one student who answered a different question from you. Respond as many times as you wish. Your best two responses will be graded. The answers should come from your textbook, the lectures, videos, and the research you conducted in the APUS Library. The initial post with well referenced facts is due by Wednesday, 11:55 p.m. ET and 2 peer responses are due by Friday, 11:55 p.m. ET.

1.) Who were the Tuskegee Airmen and how did they contribute to WWII? Or -who were the Montford Point Marines and why are they significant? Please don’t choose either part of this question if you addressed it in week four’s military essays.

2.) Was A. Philip Randolph’s March on Washington movement successful? How did it relate to the Double V campaign?

3.) The Great Migration and the Negro Motorist Green Book are intricately related. What is the relationship there and what context can you offer for both?

4.) Paul Robeson is a fascinating figure. Many historians think that he is not widely studied or valued because his detractors made him out to be a controversial figure. What is your assessment? Were his stances communist or anti colonial? Was he an asset to civil rights?

Hints:

Remember: Generic sources are not acceptable. You have to understand the quality of the source you are using. Sources/citations are not required in the regular forums but they are required in the essays. Your participation posts need to be substantive – show you understand the readings and material for the week. Use Word to edit your work. In the essays, write in character, have two separate sections (as per the instructions) and be specific, refer to the lessons and readings.

How to Write a Winning Volunteer Year Motivation Letter That Will Help You Get a Visa in 2022

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What to put in a cover letter for a volunteer visa in 2022
Your cover letter for a volunteer position should be thorough, appropriate, and simple to read. Include the following in your volunteer cover letter:

names and addresses of you

Greeting

Introduction

Body

Conclusion

Sign-off

names and addresses of you

Your name, home address, and primary phone number should be listed at the start of your cover letter whether you are sending it through email or regular mail. With this, you introduce yourself to the reader and provide them with your contact details. Put your email address under your name if you’re mailing a physical letter.

 

Greeting

A brief salutation or greeting should be used at the start of your letter. If at all feasible, use the recipient’s name and be respectful and professional in your communication. If you are unsure of who will be reading your letter, use “Dear Mr./Ms. Last Name” or “Dear Sir/Madam” instead of the traditional salutation. If you are unsure of whom to address your letter to, be sure to look for a potential contact in the department you might be working in.

 

Introduction

Your recipient is introduced to you in the first complete paragraph of your letter. A succinct description of who you are and why you are interested in the job should be included. You might also mention how or where you learned about the opportunity in this sentence. This is a wonderful time to discuss any contacts or professional recommendations you may have for the company.

 

Body

The focus of your volunteer cover letter should be in this area. You should describe why you would be a good fit for the role in this paragraph. the following information in the letter’s body:

 

List your degrees, majors, and the institutions you attended under education. It’s crucial that you reveal your highest level of education because many organizations have educational requirements for volunteers. You might include any leadership roles you held or extracurricular activities you took part in during your time in school here.

Focus on highlighting any professional experience that is pertinent to the role while describing your experience. Include your title and the name of your employer or supervisor if you have worked for a firm or donated your time to another group. How has your prior experience equipped you to serve as a volunteer? Please briefly explain.

Skills: In your final paragraph, you should list any unique abilities that are pertinent to the role. This covers fundamental abilities like cooperation, integrity, and time management. Include any technical expertise that is pertinent to the duties you will be performing. For instance, if you want to volunteer as an administrative assistant, you would mention both your computer expertise and interpersonal communication abilities.

You might also list any credentials or professional accomplishments that attest to your abilities in the body of your letter.

 

Conclusion

Your cover letter should end with a word of gratitude that expresses your appreciation to the reader for reading and taking the time to think about it. You should reiterate your interest in the job and give a general summary of your schedule of availability. Finish by promising a follow-up email and expressing your eagerness to hear from the company soon.

 

Sign-off

If you are sending an email, enter your entire name followed by your personal contact information after your conclusion, followed by a professional sign-off such “Sincerely” or “With kind regards.” Type your name, print the letter, then sign your name by hand beneath your closing if you’re sending a physical letter.

 

Learn more about writing a cover letter here.

 

Writing advice for a cover letter for a volunteer

Here are some more hints on how to draft an expert cover letter for a volunteer position:

 

Make research. Do your research on the company before applying. Find out who you should write to, what possibilities are available, and the criteria for volunteers before you write. You can be more explicit in the body of your cover letter if you are aware of this information beforehand.

Set objectives. Consider your motivations for volunteering as you write your letter. For instance, perhaps you want to advance your career, develop new skills, or find a fulfilling way to give back to your neighborhood. Your receiver will be able to tell if the position is a suitable fit for you if you include these aims in your letter.

Keep it brief. Every week, your receiver might get dozens of volunteer applications. They might simply take a few minutes to read through each letter that comes across their desk in the interest of expediency. Try to keep your cover letter to one page, with short, straightforward paragraphs, to make their task easier.

Startup in collage! Right or Wrong Pros & Cons Complete Explanation How to do your own Startup

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Startup in collage! Right or Wrong Pros & Cons Complete Explanation How to do your own Startup

 

While attending college, you can engage in a variety of activities, many of which will appear on your CV and have an impact on your future job. Many millennials aspire to launch a business after graduating from college or later in their careers. However, there is actually nothing that prevents you from launching a business while you are still in school. Of course, there are benefits and drawbacks to launching a business while still in college.

In the last 50 years, some of the most prosperous businesspeople were in their teens or early 20s. Many people, like Mark Zuckerberg of Facebook and Bill Gates of Microsoft, launched their first firms while still in college. Many young people were motivated by the success of Microsoft, Apple, Facebook, and other companies to pursue entrepreneurship, often by dropping out of school.

 

However, I would advise against dropping out. There are hundreds, perhaps thousands, of college dropouts whose businesses failed to succeed. However, starting a business while you are still in college could be a terrific idea if you have a great idea, want to gain new skills, and want to expand your professional opportunities. Thus, before taking a

 

 

 

Benefits of Launching a Business While in College

 

 

You’ll have time to start planning.

 

 

If you wait until after you graduate from college to begin making plans for a new business, even though this is unquestionably the course you want to take, you will already be a little behind schedule. The amount of work involved in the planning phase of launching a new company should not be understated.

 

You must think about writing a solid business plan, consider the start-up costs, the anticipated turnover and profit margins, and the time it would take to return the start-up costs. You must conduct market research to determine who your target clients are and how to attract them. If all of this work is completed, you can start working right away.

 

You Can Use Resources from College

 

 

You shouldn’t undervalue the amount of access to resources you have at your college. Students who want to start their own businesses have access to free counselors, printing and IT facilities, and a variety of awards and financial aid programs.

 

 

For entrepreneurs, universities offer a wealth of counseling, mentoring, educational, and financial tools. Finding a professor or graduate student who is willing to give advice on your business plan, for example, shouldn’t be difficult.

 

You’ll discover that several institutions have running initiatives to aid new enterprises, and in certain circumstances, even spaces are available. There are so many expenses, such as the $970 cost of trademark registration, that any savings should be appreciated.

 

Numerous prestigious colleges offer resources and cash for company incubation. There is also a Graduate Entrepreneur Visa (Tier 1) program in the UK. Therefore, starting a business while studying is very possible and has a lot of support networks. Of course, getting into the correct university is essential.

How To Learn English Online Part 4 Through Skype! With an Indian Teacher Online!

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Skype English lessons

Skype English courses are a really engaging approach to learn the language. You’ll be astounded to learn how technology enables you to take private English lessons from a tutor as if he were in your living room.
The benefits of Skype English classes
Simply put, Skype is an excellent tool for learning English with native English speakers wherever you are. The most recent version of Skype, an Internet connection, and a headset are all you need.
Engaging: The English courses we provide over Skype are quite interactive. Due to the fact that you are online, you have access to a wealth of materials, including articles, activities, and the teacher can write in new words or expressions for you.

For all levels: As the teacher and you speak, some terminology will appear on your screen. It’s incredibly helpful, especially for people with less advanced English.

Even less expensive: Since computer-to-computer connection is completely free, Live-English.net can provide you the finest deal!

Flexible: You can arrange a Skype English class at any time of the week for 45 or 30 minutes (also during the weekend).

How can I study English using Skype?

It’s fairly easy. You have immediate access to the Member Area after you register. You schedule a lesson with a Live-English instructor. On the day and time you specify, the instructor will call you on Skype. Now the lecture may begin.

 

Your needs are the focus of the courses, and the instructor adjusts to meet your goals and level. Since you’re using Skype, your teacher can send you instant messages as you converse. As a result, you may keep track of your errors and new terminology without constantly being interrupted. If your teacher wishes to share certain resources with you, he can also share his screen with you.

Prior to the class, material is typically supplied through email and can be shared.

Whatever course you decide on, learning English using Skype enables you to concentrate on speaking abilities, practice your English around topics that interest you, and feel more at ease speaking the language.

 

What is required for me to learn English using Skype?

Obviously, you must first download the free Skype app to your computer, tablet, or smartphone in order to begin. It simply takes a short while.

For the most recent version of Skype, visit Skype.com.

The call quality is excellent while using a typical Internet connection. Modern tablets and laptops typically have a decent microphone. You can also buy a headset if you’d like. Although it’s not required, we suggest using a webcam.

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IT Systems Used by Manulife Financial Cooperation

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IT Systems Used by Manulife Financial Cooperation

Manulife Financial Inc. is a multinational financial and insurance services provider based in Canada. Like other companies across the globe, it uses IT systems to communicate documents and reports, store operational data and revise historical information. This paper explores the IT systems utilized by Manulife Financial Inc., such as hardware and software.

Manulife Financial uses multiple software such as SunGard iWorks, Manulife Mobile, Manulife Bank Mobile, Manulife zoom, V3locity Insurance Administration, and Manulife Budgeting tool software. The Manulife Mobile software is used to set up, upgrade and troubleshoot their mobile devices, such as Blackberry, iPad, and iPhone, among others. The Manulife Budgeting tool is used to create, troubleshoot, and maintain user accounts on various servers like BES (Blackberry Enterprise Server) and Mobile Iron MDM (Mobile Device Management) servers. SunGard iWorks and V3locity Insurance Administration are used for insurance enterprise resource planning (Apps Run the World, 2021).

On the other hand, the commonly used hardware components include computers and printers. The hardware supports planning, coordination, and execution of hardware and software upgrades, resolving domain, active directory, and network share issues, training and mentoring new team members, configuring and troubleshooting any printer issues (Manulife Financial, n.d). Generally, the hardware ensures effective and efficient execution of the firm’s software, thus increasing its productivity and business bottom line.

 

 

 

 

References

Manulife Financial.  (n.d.). Technology Support Analyst. Manulife Financial. https://manulife.talentify.io/job/technology-support-analyst-boston-massachusetts-manulife-jobreq0436448

Apps Run the World.  (2021, August 20). Manulife Software Purchases and Digital Transformation Initiatives. Apps Run the World. https://www.appsruntheworld.com/customers-database/customers/view/manulife-canada

 

Problem Solving as a Data Scientist

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Critically think about your role as a data scientist and what you have learned in this program as you answer the following prompts: A current business problem that your organization faces that you can work towards solving with the skillsets and experiences you’ve gained in this program? If you are not currently employed, what is a different real-world problem that you might solve? What technologies / languages need to be “brought to bear” on the problem?

One of the problems that I can see my organization facing in the future is product testing, metric measuring, and forecasting. I work in a project office in army aviation. At some point we test new features and new air vehicle models. I have already discussed working with the test engineering team to build dashboard to display test metrics and help with predictive models. This program has given me the foundation needed to assist with those topics. It has also allowed me to be more deeply invested in my job and use my skills in more areas than one. I am also currently a data manager so I put together a lot of reports and plans for how we handle all forms of data in our office. This program has already helped me to run reports more efficiently and provide better insight into issues with data.

I work as a ski lift operator at Breckenridge Ski Resort, under the umbrella of Vail Resorts, in Breckenridge, Colorado.

An interesting business analysis that I could work with using tools in this program is something I have actually spoken to the senior manager of mountain operations about. Our mountain has an app that gives updates on all facets of the resort. It provides live updates on the wait time of each lift on the mountain, volume of people inside the restaurants scattered across the mountains, snow conditions, and various other points of interest. The way in which wait time is calculated and predicted is very interesting. Using data from the number of passes scanned at base lifts, people actively tracking their ride on our app, and purchases at restaurants, predictive analysis can be used to provide information on the number of people out and how long it will take to get food or to ride any of the 30 lifts across the mountain. Once the information is collected, we turn to the dashboard interface on the app and can portray the data in any number of ways.

As the mountain expands and the data collection gets smoother, the predictive methods are continuing to be better and better and provide the data in a clearer manner. It was cool to discuss this, as it is not at all part of my day to day work, but something I have interest in and maybe something to work towards within the company in the future.

I work for a medical logistics company, and one area that falls under my responsibility is maintaining our handset and scanner inventory. One of the critical problems I encountered was holding a stable stock of devices for drivers in the field. There is an extremely high turnover of devices due to being destroyed, lost or stolen, or added work. Recently, I wrote some queries in SQL. These included ten years of data, geographical results of where tablets were utilized nationwide, and the growth of the business. Our company does not use Power BI, so I had to create a dashboard in Excel. I used Pivot Tables, slicers, and pretty colors of the data to show total units, historical purchases, predictability of new geographical areas, and trends on when assets were to be purchased. I made my presentation to management. Everyone was impressed with what the data showed. Management ignored my recommendations. We still have stock issues, and all my forecasting is proving accurate. Real-world reality – “You can lead a horse to water…..”

Many emergency departments throughout the country and around the world are experiencing long wait times. At our hospital, we are no exception. In order to resolve the problem, we need to identify the barriers. One of the challenges is having the right amount of staff and resources when the department experiences a surge of patients. If we could better predict hourly, daily, and monthly volumes based on historical data, the department may be able to balance staffing and resources.

To help solve this problem, I would need to utilize all the tools available to me, including R, SQL, CCL, PowerBI, Qlik, and real-time data feeds, to create a predictive model that could predict accurate volumes.

I would need to utilize historical weather data, including temperature and conditions such as sun, rain, or snow. I would also need to use moon phase data with daily percentages (to prove or disprove the full moon theory.) I would add calendar events around holidays, school schedules, report cards, end-of grades, government subsidy checks, and other community-type special events that draw large crowds. Most importantly, I need to add historical clinical data that includes dates, times, volumes, and reason for the visit to calculate recommended staffing patterns. I would also add historical epidemic data such as flu and COVID cases to help identify seasonal trends.

I would create a predictive analytic dashboard with historical data and a future forecast that is updated every 30-60 minutes. The combined data would help identify patterns and volumes for a specific time. This would allow the ED management team to see the daily forecast and predictions for the next seven and 30 days. The visualizations below are examples of some of the data and dashboards we currently have around ED volumes.

Predictive Analytics

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Predictive analytics requires structured data in general, so with this in mind. How does graph representation and graph analytics help with the unstructured data before or along with building predictive modeling? what are the trending Graph Database tools? What are the processing challenges with Graph databases compared to structured databases?

Graph representation and graph analytics is a huge help for that first step of understanding unstructured data. Unstructured data is information that isn’t arranged yet by patterns, trends, or any data model. Graph representations allows us to get a visual and draw initial conclusions and further predictions about the data we have in front of us.

Oracle, Neo4j, and Mark Logic are a couple trending graph database tools. We explored Neo4j a little bit in our previous course and it was neat to be able to see relationships and groups in the data we have.

In a graph database, the relationship among parts of our data are stored in the individual record level. Structured databases on the other hand uses these predefined structures to hold it all together. No standardized language is one of the biggest struggles. The other main issue is that sometimes graphs aren’t the best way to show and interpret things.

Graph databases allow a flexible schema or unstructured data to have linkages to different parts of the data. With easy expression of entities and relationships between data, graph databases make it easier for programmers, users and machines to understand the data and find insights. This deeper level of understanding is vital for successful machine learning initiatives, where context-based machine learning is becoming important for feature engineering, machine-based reasoning and inferencing.

What are the trending Graph Database tools?

The top ten graph database tools are listed below from this website (Links to an external site.).

Neo4j, ArangoDB, Amazon Neptune, Dgraph, DataStax,OrientDB, FlockDB, Cassandra and Titan.

What are the processing challenges with Graph databases compared to structured databases?

Specialty graph data engineers are needed to process and model the data
Maintaining the consistency and relationships of the data
Writing graph data queries
Every query you write is not re-usable. If you have 10 questions you want answered from the data you have to write ten different queries

Graph analytics is actually built to analyze both structured and unstructured data.
As the name suggests, graph analytics is a graph model which represents entities and relationships. We see unstructured data in social media, mobile, the Internet, and the huge volume that needs to be processed in a fraction of seconds. This makes it difficult to make the data structured (like relational data) and define the proper semantic layer.

Using a graph database here like NoSQL makes it more accessible, and dynamic with a low cost for new data to be integrated into the existing system. It has the capability to process and retrieve a high volume of data from multiple sources. Due to high flexibility, it becomes more accessible to interlink data, derives meaningful analysis, and make better decisions. (Link (Links to an external site.))

Some of the trending Graph database tools are : (Link (Links to an external site.))

Neo4j: This we have covered in detail in the previous courses.
AWS Neptune
Dgraph
Cassandra
Graph QL

Since we are dealing with highly interconnected data of both structured and unstructured with any volume we face the below problems (Link (Links to an external site.))
1. To Maintain consistency of data
2. Modelling the interconnected data
3. Writing the graph query
From my previous program I knew about knowledge graphs and deep learning but I was unaware of graph analytics, which is similar to knowledge graphs. Knowledge graphs are three dimensional graphs with for example text graphs words are along two axes and the third axis provides the distance in meaning between the words. These graphs along with convolutional neural network models were used to develop original images and text based on data seeds (Chollet, 2018). The concept of utilizing graph analytics with predictive modeling is a similar methodology. The graph analytics can provide degree centrality, shortest or longest path, community Ids and pagerank results which are similar to the knowledge graph distance between word meanings and clustering algorithms.

The only Graph Database tool I have used is Neo4j. I have heard of Oracle Spatial and Graph. The following article provides ranking of current graph databases https://www.predictiveanalyticstoday.com/top-graph-databases/ (Links to an external site.). Their top ones are ArangoDB, Neo4j, OrientDB, and AllegroGraph (Predictive Analytics Today, 2022).

With my limited experience, the issues with graph databases are that they are resource intensive and consume a significant amount of time to run complex queries. For one of my queries, I needed 64 gigabytes of RAM and a week to run the code. However, their syntax for complicated queries is easier than using PL/SQL with cursor code. PL/SQL code that is tens of lines is just a couple of lines in Neo4j, but the tradeoff is the time to run the complex query.

Research for data science article synopsis

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Research for a data science article synopsis or blog post that details a complicated real-world problem that was solved (or remains unsolved) using data analytics or predictive analytics.
Include details about the problem space itself, the technologies that were used to solve the problem, and what difficulties they encountered. If the problem remains unsolved, be sure to mention why and the obstacles that haven’t been overcome.
Provide a link to the article/blog post you are discussing. Finally:

Source: https://www.business-standard.com/article/companies/2022-to-see-rise-in-adoption-of-chatbots-hiring-in-data-analytics-report-122011001150_1.html (Links to an external site.)
One problem solved by data analytics is chatbots and remote work opportunities. From this article, we can see that chatbots are the solution to customer service needs. We probably all encounter chatbots nearly every day. The utilization of Chatbots allows for a better, more focused customer service experience. From my experience, there are chatbots that can pick up on keywords and phrases and yield an answer that is fitting to what the consumer is searching for.

UPS created a handheld route optimization tool called ORION (Links to an external site.). ORION provided drivers with the most efficient route for deliveries and pickups. To create this route UPS had to make custom detailed maps. The reason they couldn’t use map services like Bing or Google is because some drop off and delivery locations are not addresses but they are locations. Over time they have enhanced ORION to provide real time updates based on traffic and unknown pickup/drop off commitments when the original route was planned.

After collecting all of this logistical data, UPS was have to review the data and provider even further optimization. In 1959 George Dantzig created the vehicle routing problem which can be used to organize many things. For UPS finding the shortest route was instrumental but later finding the most optimized route which shortened total drive time became the most important. What UPS found after reviewing the data is that left turns have a much higher risk of accidents and a much longer wait time for traffic to clear to make the left turn. UPS found that if they optimized their route using right turns and only taking left turns when absolutely needed, it saved 10 million gallons of fuel, avoided the emissions of 20,000 tons of CO2, and delivered 350,000 more packages a year. The optimization model allowed for a max of 10% left turns in the route. As a result in this new optimization UPS was able to reduce the number of trucks by 1,100 and total distance travelled by 28.5 million miles. For a company it’s always great to save money and have a better customer experience.

This is a beautifully described use case from UPS on how we can achieve the most out of Data analytics and find an optimum route for delivery. With the power of analytics, UPS has dramatically changed its mode of operation.

Here is the takeaway from the blog:

Hierarchy of Impact through the use of analytics
Descriptive -> Diagnostics -> Predictive -> Prescriptive

Note: With the growth towards the more advanced feature of analytics the business also grows.

UPS was overwhelmed with great results with the help of analytics. They were able to save 100M miles driven, 8M gallons of fuel saving, 95 % reduction in load training time, 8B fewer manual entry, and 100,000 metric tons of carbon emissions

We should be able to forecast and plan ahead of what would be our demands and how we can meet their demands. With the use of Prescriptive analytics and by the use of the analytical tool and advanced algorithms we can take an even better decision (rather than taking risks in taking a decision )
For e.g.: ORION helps the UPS drivers to assist them on which route to go and which order to deliver in order. All the driver needs to do is follow the algorithm. It helps in route optimization.

And with the help of prescriptive analytics UPS was able to save an additional 300 to 400 M dollars annually

There are still many open questions that UPS is trying to figure out by use of data that has already been collected and analyzed it like if they need more access points, lockers, etc.

Link: https://www.zdnet.com/article/in-the-cloud-everyone-wants-to-be-your-partner/

ETL – Extract, Transform, Load

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ETL – Extract, Transform, Load. Raw Data coming from various sources including internal and external sources need to be cleaned and prepared so it can be integrated in a meaningful way before any analytics can be performed over it. There are many tools that can be used to perform the ETL. With this information in mind:

Tell Us:

Some of the trending ETL tools that are out there in the industry and which ones you have used. What are the some of the challenging ETL process you have come across or solved?

What are the limitations to the ETL step itself compared to Graph Data representation? Are there any use of Machine Learning Algorithms to perform ETL? Finally:

I believe one of easiest ETL tools to use is Alteryx. Alteryx allows you to input data and preform many different transformations in the data and then output the results you build. What makes it easy to use is how the program is designed. The user interface is all drag and drop and once your transformation is complete it will look like a process flow which is really easy to read and follow. Alteryx has a great community when you are struggling to google answers on as well. It’s a very expensive program which may limit the amount of people that have used it. I’m currently using Alteryx at my workplace but I don’t have access to an Alteryx Gallery (Server). This would allow me to schedule my workflows so they run automatically.

Alteryx Image.png

One of the challenges I came across during and ETL exercise is storing data. Some of the data at my workplace is a “point in time” data. Meaning the data can only be measured on the day of. What this doesn’t allow is good historical tracking. For example for our customers we know how many of them have an email address today. Lets say today we had 100 email address. Yesterday we don’t know if we had 90 or 100. So what I do is each month I query the email table to join to our customer table and store a monthly history. I use an insert statement to add records to a table I’ve already setup each month. This allows me to measure if the number of emails for our customers have increased or decreased over time.

With a relational database that you would use ETL in a star schema you’re setting up Dimension and Fact tables. In the graph data models you use one transactional data set and tell the software what fields to merge or connect on.

Several ETL (Extract – Transform – Load) tools are available for data management, such as Oracle, Informatica, Stitch, SAS, and Qlik. The hospital uses the Cerner EMR software that is part of Oracle. The Cerner Command Language (CCL) is the primary database query and scripting language used to extract healthcare data from the EMR Oracle database. Programmers can write CCL queries and scripts from the raw data and transform the information into operational reporting formats. Most reports are sent to their Discern Explorer Menu, their proprietary way for the end user to access reports and data. The CLL results can also be sent to other databases for custom SQL queries as well as the data warehouse for real-time dashboards.

Our CCL programmers take care of the CCL code and transform it into the EDW. My part is to validate the accuracy of the data going to the EDW for the end user to extract into reporting. This is often challenging as I need to understand where the source data is coming from, what data points to pull, and what the end result should be.

Relational Databases are great for storing and processing data. This practice has been around for several decades. They are used in Graph databases and do great with online transaction processing, analytical processing, business intelligence, ETLs, and advanced analytics. The information from the database can be processed into visualizations and graphs. RDBMS links data together through modeling. Graph databases offer flexibility and are easier to work with when linking information. However, graph modeling falls short when querying complex relationships.

I could not locate any information on true machining learning algorithms to perform ETL. Still, many algorithms provide a more automated approach to extracting, processing, and transforming the data.

The following is an example of our source data in the EMR and how it is stored, extracted, transformed, and loaded into the EDW for the end user to use for data mining, reporting, and Qlik dashboards.