Wk4Dis Assignment Example | Topics and Well Written Essays - 500 words

Wk4Dis - Assignment Example he tasks and resources needed to realize the strategic goals, budget planning details how the resources will be acquired and distributed across the various tasks and departments. Therefore, budget planning helps the organization realize the tasks that are detailed in the operational plan by allocating human and financial resources. Budget thus planning helps the organization prioritize the activities included in the operation plan based on the required resources and expected returns. Since system thinking is a holistic approach in understanding the organization, it helps improve operation decision making by examining the interactions between various parts of the organization. It helps show how a change in one section will affect other sections in the organization, thereby allowing for well informed decision making. For example, through the system thinking approach, an organization will understand which areas modifications need to be made. In addition, system thinking allows for an organization make decisions that tackle the specific cause of a problem or specific areas where the problem lies since the entire system is analyzed. Deliberate strategies involve the set of intended actions that are taken after careful planning and deliberation. Deliberate strategies require that actions undertaken are not influenced by external forces, and must be accomplished as originally earlier agreed. For example, as noted in the article by Moore (2011), deliberate strategies are formulated based on a preexisting model that is made to fit the organization or industry. Emergent strategies on the other hand are those decisions taken and adapted over time and are not intended. This involves the organization understanding what best works in practice, and such actions are not pre-planned. Emergent strategies are useful in a future strategic planning process because they help the organization when plans fail. These strategies help the organization cope with unexpected events or

Colonial and Revolutionary Eras in America Essay Example for Free

Colonial and Revolutionary Eras in America Essay The colonial and Revolutionary eras in America are not so chronologically distant, yet they are two very different times for America. These two eras are very important parts of America’s history. The transformation of colonial America to Revolutionary America is quick but drastic. To be a colonial American would mean solely relying on God. An American at that time would center their whole life around God. They believed they did not personally own anything. For example, in Anne Bradstreet’s poem â€Å"Upon a Burning House†, Anne implied that it was wrong to feel sorry for the loss of your house or family, because the Puritan belief was that everything is owned by God. Anne considered herself lucky because she was left with the most important thing of all; her life (Chin 78). Anne Bradstreet most captured my attention with her writing style and her pure love of God. Puritans believed that â€Å"if God should let you go, you would immediately sink and swiftly descend and plunge into the bottomless gulf† (Chin 103). It was easy for the British to keep people of the Puritan lifestyle under its crown because of their religious beliefs (Kiracofe) The Revolutionary era is when the colonists began to become more opinionated. The start of the Revolutionary era was when the British began taxing sugar. The sugar act lead to a boycott of buying all British imports. The Boycott put the British in great debt and was eventually repealed. At that point, the colonists discovered that they do have a say in their government. The people of America began relying on logistics and facts instead of their faith. This lead to the Revolutionary war, also called the War of Independence (Higginbotham). More and more people began speaking their mind, such as Phillis Wheatley, an African American who writes a Revolutionary piece of art, praising George Washington for fighting and leading in the Revolutionary war. The colonists began to become more and more individualized from the British. The whispers of rebellion turned into shouts after the Stamp Act. The farmers and merchants of America quickly transformed from strongly religious and peaceful men, into soldiers of the Revolutionary war. Colonial and Revolutionary Americans are two very different groups of people. The colonists were Puritans which means they were highly religiously dependent. Although the Revolutionary Americans did believe that there was a God and that he was on their side, they took their own initiative and fought for their freedom from Britain’s crown. If it were not for Revolutionary thinkers such as Thomas Jefferson, John Adams, George Washington, and the men who fought for this country, America would still be under the British laws and taxation. We would not have the rights and freedoms we have today. The Revolutionary war has made America for what it is today.

Programming Languages for Data Analysis

Programming Languages for Data Analysis R and Python for Data Analysis Abstract This paper discusses the comparison between the popular programming languages for Data analysis. Although there are plenty of choices in programming languages for Data science like Java, R Language, Python etc. With a whole lot of research carried out to know the strengths of these languages, we are going to discuss any two of these. Data Analytics has been the most important and trusted tool for business and markets. Data Analytics is nowadays making use of SAAS (Software As a Service). For this literature review, two popular languages (R and python) have been studied and evaluated the characteristics to decide which one will be the right language for data analysis. Both Languages shows their own strength and weakness and based on that, to understand the data based processing environments in the Distributed File Systems. Keywords-Programming language; Data analytics; R; Python, Big Data; For an industry to grow in a market is not an easy task. With the help of Data Analytics, it can grow bigger and better. It can help to deliver quick corporate results and a value to business. The major challenge with the data is to process it and then make decisions worth value. Data Crunching requires proper tools and powerful analysis. Out of all languages, we choose two popular language i.e R language and Python for data analysis. We are going to discuss the need of using a programming language in Data Analysis and list some of the characteristics of these two languages. In the end, we will conclude which language performs and delivers in the field of Data Analysis. While carrying out research in Data Analytics, we came across multiple programming languages apart from R and Python which are described below- Julia Not a well-recognized language but hackers surely talk of Julia. It is said to be faster than R upgradable than Python. [5] Java In comparison to R and Python, Java seems less capable in terms of Data Visualization but can be the first choice for the prototype of the statistical system. [6] MATLAB Became popular and was used before the release of python and R. To be good fit as a programming language we should consider different aspects of data analysis. For this review purpose we will broadly classify them as follow- Collection of Raw Data Data is available in variety of format. Programming languages were evaluated in terms of support for various data formats and efficiency in handling them. Data processing Once imported into program, datasets might require cleansing in terms of missing values, unrelated or redundant data values etc. Capabilities to deal with such data were evaluated for programming languages Data Exploration Simplicity of applying commonly used statistical methods like grouping, pattern recognition, switching and sorting is evaluated for programming languages. Data Analysis Availability of special purpose in-built functions and various methods of machine learning and deep analysis are used as evaluation measures. Data Visualization Visualization is important aspect of data analytics. Visualization capabilities of programming languages were evaluated on the basis of ease of creation, simplicity and sharing in various formats. In addition to these capabilities we will discuss a bit about history and accolades of every programming language. We will also discuss popular choices for IDE (Integrated Development Environment) for these1 language. Introduced in 1995, by Ross Ihaka and Robert Gentleman, R is implementation of S programming language (Bell Labs). Latest version is 3.1.3 which was released in March, 2015. Rs architectural design and evolution is maintained by R-foundation and R-Core Group. [1] Rs software environment is written primarily in C, FORTRAN, and R. RStudio is very popular IDE used to perform data analysis using R. Primary used for academic research, R is rapidly expanding into enterprise market. [1] A. Collection of Raw Data You can Import data from variety of formats like excel, CSV, and from text files. DataFrames, primary data structure in R, can import files from SPSS or MiniTab. Basically R can handle data from most common sources without glitch. Where R is not so great at is data collection from web. Lot of work is being carried to address this limitation. To name few, Rvest package will perform basic web-scraping while magrittr will parse the information on webpages. [1][3] B. Data Processing It is very easy to reshape dataframe in R. Tasks like adding new columns, populating missing values etc. can be done with just one line of code. Many new packages like reshape2 allow users to manipulate data frames to fit the criteria set per requirements. [3] C. Data Exploration R is built by statisticians. For exploratory work its easy for beginners. Many models can be written with very few lines of codes. With R, users will be able to build probability distributions and apply statistical methods for machine learning. For advance work in analytics, optimization and analysis, users may have to rely on third party packages. [3] Many popular packages like zoo (to work with time-series), caret (machine learning) represent strength of R. Python is loosely bind programming language with very wide user base. D. Data Visualization Visualization is strong forte of R. R was built to perform statistical analysis and demonstrate the results. By default, R allows you to make basic charts and plot graphs which can be saved in variety of formats like jpeg or PDFs. With advance packages like ggvis, lattice and ggplot2 user can extend data visualization capabilities of R program. [1][3] Created by Guido Van Rossum in 1991, Python is inspired by C, Modula-3 and in-perticular ABC. Python software foundation (PSF) is curator for Python language. Current version is 3.4.3/2.7.9 released in Feb 2015/Dec 2014. Python has been popular choice for programmer to build web and multitier applications. In context of data analytics, Python is majorly use by programmers to apply statistical techniques. Coding in python is easy because of nice syntax. [4] IPython Notebook and ANACONDA are popular IDEs used for data analysis using Python. A. Collection of Raw Data In addition to excel, CSV and text data, python also supports JASON and semi-structured data formats like XML and YAML. Using certain libraries, users can import SQL tables into python program [4] Python Request Library facilitates web scrapping, where user can get data from websites to analyze in depth. [2] B. Data Processing To uncover underlying information, Pandas library of python comes handy. Like R, data is held in DataFrames which can be used and reused throughout program without hampering performance. [2] Users can apply standard methods of cleaning data or process data to fill out incompelete information just like R. C. Data Exploration Pandas is very powerful library. Users will be able to group by datavalues and sort them according to timeseries. Comlex grouping clauses like time-series analysis to seconds can be performed on dataframes in python program. D. Data Visualization Using MetaPlotlib [2] library, user can plot basic graphs and chrats from available data-points. For advance visulization, Plot.ly can be used, which is another python library. Users can use powerful IDEs like Anaconda or IPython Notebook to create powerful visualization and convert them into various formats like HTML. In addition to their differences, there are few common positives about both Python and R which make them so popular among data analysts and statisticians. R and Python are distributed under open license which make them free to download and modify per users need. In contrast to other programming tools, like SAS and SPSS, which come with hefty price tag. Being open source, many advancements in statistics will come to python and R first.[6] Both of them are widely loved and supported by big community of statisticians and developers. [6] IDE like IPython Notebook will consolidate your datasets in one file, thereby simplifies your workflow.[2] R has rich ecosystem of cutting edge packages to string your work together which proves useful in particular to Data Analysis.[3] Python is more of general purpose language. Its easy and intuitive, therefor it has simplified learning curve. Pythons testing framework guaranties reusability and reliability of code. R is language developed by statisticians for statisticians while python is easier to learn general purpose programming language.[3] Working through research in programming languages for data analytics, there are many other options which are listed below- Julia Though not yet widely recognized, data hackers talk fondly of Julia. It is regarded as faster than R and more scalable than Python.[5] Java Although java is not as capable as python and R in terms of visualization, it can be primary choice to build prototype for statistical system. [6] KAFKA Developed by linked-in, KAFKA is highly regarded for its real-time analytics capabilities.[6] STORM Storm is framework written in SCALA which saw recent tides of popularity in Silicon Valley MATLAB Excel Used by many statisticians before outburst of python and R. Special thanks to Prof. Oisin Creaner, for presenting this opportunity to dig out for various options available for programming in Data Analytics Ihaka, R. and Gentleman, R., 1996. R: a language for data analysis and graphics. Journal of computational and graphical statistics, 5(3), pp.299-314. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J., 2011. Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, pp.2825-2830.. Nasridinov, A. and Park, Y.H., 2013, September. Visual Analytics for Big Data Using R. In Cloud and Green Computing (CGC), 2013 Third International Conference on (pp. 564-565). IEEE. Sanner, M.F., 1999. Python: a programming language for software integration and development. J Mol Graph Model, 17(1), pp.57-61. Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A., 2012. Julia: A fast dynamic language for technical computing. arXiv preprint arXiv:1209.5145. Fan, W. and Bifet, A., 2013. Mining big data: current status, and forecast to the future. ACM sIGKDD Explorations Newsletter, 14(2), pp.1-5.

Essay --

Taking a Stand Not since the start of the 1994/95 football season have we seen standing areas in the top two divisions of the English football league. But yet much like the movie ‘Jurassic Park’ these stands are coming back, without the death and dinosaurs this time however. The cost of ticket prices are now ridiculously high especially considering the economic problems we are in. Average ticket prices in the Premier League are the highest within the four major European leagues, the others being La Liga, Bundesliga and Serie A. The average ticket price of a Premier League game is  £28.30, this is a huge price to pay to see a game of football. This compared to the average price of a ticket to a Bundesliga football match which is only  £10, clearly shows the just how shocking the gulf in prices are. However many clubs can claim that this influx of cash each week is needed to support the ever growing maintenance costs of all seated stadiums and to support the club financially at the s ame time. So if only there was a way to lower the prices yet allow the club to make more money from match attendances. Well my friends I think I have found the cure to this disease. The solution lies in the return of standing areas to football grounds. Now these standing areas would not be the same dangerous, hooliganism plagued standing areas of old; no they would be cheap, safe standing areas. Introducing safe standing areas would lower ticket prices and season tickets dramatically; this can be proven by looking at one of the largest football clubs in the world, Bayern Munich. You would expect a club of such magnitude to have season ticket prices as high as the moon, but you’d be wrong. The lowest costing season ticket for the standing area is only  £150... ...s a credit not only German football but standing areas as well. The premier league should be looking over its shoulder, for everyday they waste squabbling over what to do the Bundesliga gets stronger and will soon be challenging to take over the title of ‘Best League in the World’. Even the lousy, misery filled stadiums of the horrific Scottish Premier League (SPL) have followed Germanys lead, by dropping its ban against standing areas. This shows just how far behind the apparently almighty Premier League is, that essentially an amateur league is further ahead than it is. Fortunately the situation is looking brighter as supporters groups from 12 Premier League clubs have confirmed they are backing a trial for standing areas and despite the fact that progress is slow, the wheels are in motion and it is only a matter of time before the momentum begins to build.

Marketing and Gillette

1. How is the Gillette Series being positioned with respect to (a) competitors, (b) the target market, (c) the product class, (d) price and quality? What other positioning possibilities are there? a. The Gillette Series is positioned as premium to the competition. b. Using the slogan, â€Å"The Best a Man can Get† appeals to the target market not because it is the most convenient or the most price effective, but because of the value that is added to products by building on the popularity of sensor. c. The Gillette Series is positioned as differentiated due to functional attributes through innovation to the product class. . These products are priced at a premium at an index of 10 to 20 percent higher than competition. Many other positioning possibilities are available for Gillette. This brand can position the myriad of products it has separately, or treat itself as a master Brand. The positioning should be the same as the other series of men’s grooming products if Gillet te positions itself as one Brand. However, if it breaks the brand into classes, then there will be a shaving line, a deodorant line, an aftershave line. 2. Is Gillette making the best use of the brand equity that has been created with Sensor?Sensor has been a huge success for Gillette. It makes sense to use the energy of that to tie into the other products offered. The tagline of â€Å"The best a man can get,† is a solid platform for this brand. Since the equity was established for the slogan used and not just Sensor, Gillette is making good use of the equity, since the Sensor is viewed as a product from Gillette, and one that works very well according to consumer response. 3 What strategies do you propose to Gillette? Address the entire marketing mix.Conceivably, a staggered approach may work better for Gillette. Releasing the products at different times as opposed to all at one time would give consumers time to build a prevailing purpose to have faith in a product. The equi ty of sensor may be diluted with too many different products. I would suggest to first scale not only consumer reaction to product quality, but to gauge consumer understanding of the brand. If the Brand is best known for a smooth comfortable shaving razor, then it would be wise to stagger other products based on customer review. Related post: Advantages and Disadvantages of Administrative Management

China, India, and Wal-Mart: Issues of Price, Quality, and Sourcing Essay

1. What are the ethical issues associated with Wal-Mart’s extensive sourcing of low-cost products from China? Wal-Mart pricing is too low. As the world’s largest retailer, Wal-Mart leverages its huge orders to convince factories to sell goods at low prices that are not sustainable. This puts pressure on other brands to pay less, thereby setting a dangerous industry precedent. According to Correspondent Hedrick Smith: â€Å"We heard that story again and again from American manufacturers in sectors as diverse as electronics, apparel, bicycles, furniture, and textiles. They expressed private dismay at the relentless pressure from the likes of Wal-Mart and Target to cut costs to the bone in America and then, when that did not satisfy the mass retailers, more pressure to move production to China or elsewhere offshore. But most did not dare to go on camera and tell their story publicly for fear of jeopardizing their remaining sales to Wal-Mart.† (Smith) Another ethical issue is safety of the products we receive and the working conditions of the outsourced employees. From the Wal-Mart routinely turns a blind eye to poor conditions in supplier factories unless investigations are made public. â€Å"Retailer admits fire safety aspects are not adequately covered in ethical sourcing audits†. (Yardley) Wal-Mart needs more transparent ethical sourcing efforts. â€Å"Wal-Mart buys more than $1 billion in garments from Bangladesh each year, attracted by the country’s $37-a-month minimum wage, the lowest in the world.†(Yardley) 2. Based on your experience, does Wal-Mart sacrifice product quality in order to offer customers low prices  ¾ always? Yes. I don’t really shop at Wal-Mart anymore. I don’t believe the price you pay is for a quality product. I would rather save up my money and buy it from another retailer with a good reputation. I believe in the saying â€Å"You get what you pay for†. I have a problem with how they treat employees and when you go in the stores these days there are definite operation issues: Customers and analysts have noticed the operational problems in the stores, Wal-Mart associates have felt the impact most acutely. In the first national independent poll of Wal-Mart associates, conducted by Lake Research Partners in May and June of 2011, concerns about staffing levels were broadly cited by associates among top three things they would change with the company, after higher pay and more respect on the job. Among the other findings: * Nearly  ¾ say understaffing has created problems such as stock-outs, messy stores and poor customer service; * In contrast to company statements regarding high levels of employee satisfaction, 84% say they would take a better job if they could find one *  ½ say they are living paycheck to paycheck; only 14% describe their household as living comfortably. Across the country the reductions in staffing have translated into significantly increased workloads. A few examples convey the scale of the changes: * An associate in the electronics department in Southern California: â€Å"There used to be four or five people in consumer electronics at any given time, now it’s one or two;† * An associate in overnight stocking in Southern California: â€Å"I used to do five pallets a night, now they say I have to do 12;† * A former assistant manager in Seattle: â€Å"Our store used to have about 600 employees, now it’s about 350.† (Marshall) What advice would you give to critics of Wal-Mart in order to enhance their impact on the company? To enhance their impact on governmental and regulatory agencies? To enhance their impact on society in general? I can’t seem to think of anything or in my research, it has all been negative. I would love to hear what others in our class had to say on this subject and the problem lies that I am not a fan either.