Bessie Smith & Duke Ellington (600 Words)

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I’m trying to learn for my Music class and I’m stuck. Can you help?

Read the particle, listen to the music then analysis the song

Part 1: (300 words)

Listen Bessie Smith’s “Downhearted Blues” and “Empty Bed Blues.” What is innovative or unique about her vocal style, and her artistic approach? How does her voice contrast with the music. How are these recordings emblematic of Classic blues music in the 1920s? Review the lyrics for both songs (attached below), and discuss what the songs are seemingly about; and describe her use of language and colorful imagery. How might these songs exhibit a sense of liberated feminism?

Down hearted Blues: https://www.youtube.com/watch?v=go6TiLIeVZA

Bessie Smith – “Empty Bed Blues” (1928): Bessie Smith – “Empty Bed Blues” (1928)

Part 2:

Listen to the following three Duke Ellington compositions. Compare and contrast these songs and create a list of musical attributes or characteristics that define the Ellington style. How does Ellington use instrumentation and space in his compositional pieces? What sounds unique about Ellington’s style? Which of the three did you enjoy the most? (300 words)

Duke Ellington Orchestra – “Black and Tan Fantasy” (1928)

Duke Ellington – “It Don’t Mean A Thing” (1931)

https://www.youtube.com/watch?v=-FvsgGp8rSE (Links to an external site.)

Duke Ellington Orchestra – “Sophisticated Lady” (1933)
https://www.youtube.com/watch?v=-Y__G3Pa5FwFinal Proposal and Action Plan
Need help with my Powerpoint question – I’m studying for my class.

For the final project, you will write a proposal as an HR Consultant hired to assist in identifying a realistic action plan to address workforce issues facing the company in the Case Study.The proposal will be distributed at an executive meeting, where the presentation will be presented. You will be utilizing the previous course project assignments to create an 8-10 page written report (not counting cover and reference pages).In addition, you will created an 8-10 slide PowerPoint presentation action plan addressing a comprehensive action plan. Both the written report and the PowerPoint presentation should include:
A summary of the current talent management system in place. An explanation of the current appraisal process and a suggested appraisal practice with a copy of the new performance appraisal recommendation. An Employee Development Plan for a high-potential, high-performing entry-level employee of the company in the Case Study. Your plan should include all elements of an employee development plan (also called an IDP). A recommended action plan addressing what impacts performance and what actions should be taken regarding employees that perform below expectations. The suggested on-going and future talent management program for the company – including recruiting, employee retention, and employee development.
The action plan needs to flow as one continuous plan, rather than appear to be several plans combined. There should be a reference slide in APA format.

The paper should demonstrate a strong thesis statement supported by research from at least 5 different sources. Include both in-text citations and a References page in APA format.Week 5- Association rules, find the attachments below: nursing case study help
I need support with this Computer Science question so I can learn better.

Many machine learning algorithms that are used for data mining and data science work with numeric data. And many algorithms tend to be very mathematical (such as Support Vector Machines, which we will discuss next week). But association rule mining is perfect for categorical (non-numeric) data and it involves little more than simple counting! That’s the kind of algorithm that MapReduce is really good at, and it can also lead to some really interesting discoveries.

Association rule mining is primarily focused on finding frequent co-occurring associations among a collection of items. It is sometimes referred to as “Market Basket Analysis”, since that was the original application area of association mining. The goal is to find associations of items that occur together more often than you would expect from a random sampling of all possibilities. The classic example of this is the famous Beer and Diapers association that is often mentioned in data mining books. The story goes like this: men who go to the store to buy diapers will also tend to buy beer at the same time. Let us illustrate this with a simple example. Suppose that a store’s retail transactions database includes the following information:
There are 600,000 transactions in total. 7,500 transactions contain diapers (1.25 percent) 60,000 transactions contain beer (10 percent) 6,000 transactions contain both diapers and beer (1.0 percent)
If there was no association between beer and diapers (i.e., they are statistically independent), then we expect only 10% of diaper purchasers to also buy beer (since 10% of all customers buy beer). However, we discover that 80% (=6000/7500) of diaper purchasers also buy beer. This is a factor of 8 increase over what was expected – that is called Lift, which is the ratio of the observed frequency of co-occurrence to the expected frequency. This was determined simply by counting the transactions in the database. So, in this case, the association rule would state that diaper purchasers will also buy beer with a Lift factor of 8. In statistics, Lift is simply estimated by the ratio of the joint probability of two items x and y, divided by the product of their individual probabilities: Lift = P(x,y)/[P(x)P(y)]. If the two items are statistically independent, then P(x,y)=P(x)P(y), corresponding to Lift = 1 in that case. Note that anti-correlation yields Lift values less than 1, which is also an interesting discovery – corresponding to mutually exclusive items that rarely co-occur together.

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