Fitness Movement in the USA Assignment Example | Topics and Well Written Essays - 250 words

Fitness Movement in the USA - Assignment Example Several fitness businesses ranging from small storefronts to multipurpose clubs, women-only bastions to muscle gyms dotted the sporting landscape. Stand-alone clubs donated the industry until the twentieth century when the industry was transformed by the large centrally owned chains. According to Costa & Guthrie (1994), the last decade of the twentieth century was depicted by the formation of â€Å"Chandlerian† core, a contrast to the peripheral industry. The fitness movement thrived successfully in an environment that gifted collective individualism; an environment where the labor of public exercise initiated individual virtue. The shifting gender relations and the interest of women and men in molding a fit toned but healthy body were the founding issues of the movement (Costa & Guthrie, 1994).   In conclusion, the fitness movement focused on health and individuals’ responses to building self-esteem. The movement serves a positive and vital need while focussing on profits from people and depend on insecurities and the desire to have a glimpse at commercially constructed images of aesthetic value. The images of beauty taking the form of fitness in hyper-competitive, zero-sum, winner-take-all environment with an evanescent mirage security lead to deteriorating human happiness.

Slavery Before the Trans-Atlantic Slave Trade Essay Example for Free

Slavery Before the Trans-Atlantic Slave Trade Essay What is the difference between slavery prior to the 14th century with that of slavery after the 15th century? Slavery existed long before the original slaves came to America. In fact, slavery prior to the 14th century differed greatly from slavery after the 15th century. Slavery was essential to many economic and social structures. For example, ancient Greece and Rome had many slaves. They differed from the form of slavery after the 15th century, though, due to the background of their slaves. Slavery was not necessarily racial or ethnic in origin prior to 15th century slavery. It was often captured enemies of war from many different places. However, when the Trans-Atlantic trade began, the majority of the slaves were African. Another difference is that the Africans were treated as objects, whereas prior to the 14th century, they were not legally the same as objects. Another difference is the jobs that they occupied. For example, the majority of the Athenian slaves were domestic servants, but the majority of African Americans had brutal and tedious jobs such working in fields all day. Slaves were also used to enforce religions, such as Islam. The Ottoman Empire forcibly converted approximately one million non-Muslims. However, the main purpose of Africans in the Americas was primarily as a work force. A significant difference was that of the Aztec slavery. For them, slavery was not considered hereditary. Therefore, a slave’s child was free. After the 15th century, a slave’s child was still considered a slave: it was hereditary. Slave trade was different before the Trans-Atlantic Slave Trade began.

‘Big’ Data Science and Scientists

‘Big’ Data Science and Scientists If you could possibly take a trip back in time with a time machine and say to people that today a child can interact with one another from anywhere and query trillions of data all over the globe with a simple click on his/her computer they would have said that it is science fiction ! Today more than 2.9 million emails are sent across the internet every second. 375 megabytes of data is consumed by households each day. Google processes 24 petabyte of data per day. Now that’s a lot of data !! With each click, like and share, the worlds data pool is expanding faster than we comprehend. Data is being created every minute of every day without us even noticing it. Businesses today are paying attention to scores of data sources to make crucial decisions about the future. The rise of digital and mobile communication has made the world become more connected, networked and traceable which has typically resulted in the availability of such large scale data sets. So what is this buzz word â€Å"Big Data† all about ? Big data may be defined as data sets whose size is beyond the ability of typical database software tools to capture, create, manage and process data. The definition can differ by sector, depending on what kinds of software tools are commonly available and what sizes of data sets are common in a particular industry. The explosion in digital data, bandwidth and processing power – combined with new tools for analyzing the data has sparked massive interest in the emerging field of data science. Big data has now reached every sector in the global economy. Big data has become an integral part of solving the worlds problems. It allows companies to know more about their customers, products and on their own infrastructure. More recently, people have become extensively focused on the monetization of that data. According to a McKinsey Global Institute Report[1] in 2011, simply making big data more easily accessible to relevant stakeholders in a timely manner can create enormous value. For example, in the public sector, making relevant data more easily accessible across otherwise separated departments can sharply cut search and processing time. Big data also allows organizations to create highly specific segmentations and to tailor products and services precisely to meet those needs. This approach is widely known in marketing and risk management but can be revolutionary elsewhere. Big Data is improving transportation and power consumption in cities, making our favorite websites social networks more efficient, and even preventing suicides. Businesses are collecting more data than they know what to do with. Big data is everywhere; the volume of data produced, saved and mined is startling. Today, companies use data collection and analysis to formulate more cogent business strategies. Manufactures use data obtained from the use of real products to improve and develop new products and to create innovative after-sale service offerings. This will continue to be an emerging area for all industries. Data has become a competitive advantage and necessary part of product development. Companies succeed in the big data era not simply because they have more or better data, but because they have good teams that set clear objectives and define what success looks like by asking the right questions. Big data are also creating new growth opportunities and entirely new categories of companies, such as those that collect and analyze industrial data. One of the most impressive areas, where the concept of Big data is taking place is the area of machine learning. Machine Learning can be defined as the study of computer algorithms that improve automatically through experience. Machine learning is a branch of artificial intelligence which itself is a branch of computer science. Applications range from data mining programs that discover general rules in large data sets, to information filtering systems that learns automatically the user’s interests. Rising alongside the relatively new technology of big data is the new job title data scientist. An article by Thomas H. Davenport and D.J. Patil in Harvard Business Review[2] describes ‘Data Scientist’ as the ‘Sexiest Job of the 21st Century’. You have to buy the logic that what makes a career â€Å"sexy† is when demand for your skills exceeds supply, allowing you to command a sizable paycheck and options. The Harvard Business Review actually compares these â€Å"data scientists† to the quants of 1980s and 1990s on Wall Street, who pioneered â€Å"financial engineering† and algorithmic trading. The need for data experts is growing and demand is on track to hit unprecedented levels in the next five years Who are Data Scientists ? Data scientists are people who know how to ask the right questions to get the most value out of massive volumes of data. In other words, data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician. Good data scientists will not just address business problems; they will choose the right problems that have the most value to the organization. They combine the analytical capabilities of a scientist or an engineer with the business acumen of the enterprise executive. Data scientists have changed and keep changing the way things work. They integrate big data technology into both IT departments and business functions. Data scientist’s must also understand the business applications of big data and how it will affect the business organization and be able to communicate with IT and business management. The best data scientists are comfortable speaking the language of business and helping companies reformulate their challenges. Data science due to its interdisciplinary nature requires an intersection of abilities of hacking skills, math and statistics knowledge and substantive expertise in the field of science. Hacking skills are necessary for working with massive amount of electronic data that must be acquired, cleaned and manipulated. Math and statistics knowledge allows a data scientist to choose appropriate methods and tools in order to extract insight from data. Substantive expertise in a scientific field is crucial for generating motivating questions and hypotheses to interpret results. Traditional research lies at the intersection of knowledge of math and statistics with substantive expertise in a scientific field. Machine learning stems from combining hacking skills with math and statistics knowledge, but does not require scientific motivation. Science is about discovery and raising knowledge, which requires some motivating questions about the world and hypotheses that can be brought to data and tes ted with statistical methods. Hacking skills combined with substantive scientific expertise without rigorous methods can beget incorrect analysis. A good scientist can understand the current state of a field, pick challenging questions were a success will actually lead to useful new knowledge and push that field further through their work. How to become a Data Scientist ? No university programs in India have yet been designed to develop data scientists, so recruiting them requires creativity. You cannot become a big data scientist overnight. Data Scientist need to know how to code and should be comfortable with mathematics and statistics. Data Scientist need know machine learning software engineering. Learning data science can be really hard. They also need to know how to organize large data sets and use visualization tools and techniques. Data scientists need to know how to code either in SAS, SPSS, Python or R. Statistical Package for the Social Sciences (SPSS) is a software package currently developed by IBM is widely used program for statistical analysis in social science. Statistical Analysis System (SAS) software suite developed by SAS Institute is mainly used in advanced analytics. SAS is the largest market-share holder for advanced analytics. Python is a high-level programming language, which is the most commonly used by data scientist’s community. Finally, R is a free software programming language for statistical computing and graphics. R language has become a de facto standard among statisticians for developing statistical software and is widely used for statistical software development and data analysis. R is a part of the GNU Project which is a collaboration that supports open source projects. A few online courses would help you learn some of the main coding languages. One such course that is available currently is through the popular MOOCs website coursera.org. A specialization course offered by Johns Hopkins University through coursera helps you learn R programming, visualize data, machine learning and to develop data products. There are few more courses available through coursera that helps you to learn data science. Udacity is another popular MOOCs website that offers courses on Data Science, Machine Learning Statistics. CodeAcademy also offers similar courses to learn data science and coding in Python. When you start operating with data at the scale of the web, the fundamental approach and process of analysis must and will change. Most data scientists are working on problems that cant be run on a single machine. They have large data sets that require distributed processing. Hadoop is an open-source software framework for storing and large-scale processing of data-sets on clusters of commodity hardware. MapReduce is this programming paradigm that allows for massive scalability across the servers in a Hadoop cluster. Apache Spark is Hadoops speedy Swiss Army knife. It is a fast -running data analysis system that provides real-time data processing functions to Hadoop. It is important that a data scientist must be able to work with unstructured data, whether it is from social media, videos or even audio. KDnuggets is a popular website among data scientist that mainly focuses on latest updates and news in the field of Business Analytics, Data Mining, and Data Science. KDnuggets also offers a free Data Mining Course the teaching modules for a one-semester introductory course on Data Mining, suitable for advanced undergraduates or first-year graduate students. Kaggle is a platform for data prediction competitions. It is a platform for predictive modeling and analytics competitions on which companies and researchers post their data and statisticians and data miners from all over the world compete to produce the best models. Kaggle hosts many data science competitions where you can practice, test your skills with complex, real world data and tackle actual business problems. Many employers do take Kaggle rankings seriously, as they can be seen as pertinent, hands-on project work. Kaggle aims at making data science a sport. Finally to be a data scientist you’ll need a good understanding of the industry you’re working in and know what business problems your company is trying to solve. In terms of data science, being able to find out which problems are crucial to solve for the business is critical, in addition to identifying new ways should the business should be leveraging its data. A study by Burtch Works[3] in April 2014, finds that data scientists earn a median salary that can be up to 40% higher than other Big Data professionals at the same job level. Data scientists have a median of nine years of experience, compared to other Big Data professionals who have a median of 11 years. More than one-third of data scientists are currently in the first five years of their careers. The gaming and technology industries pay higher salaries to data scientists than all other industries. LinkedIn, a popular business oriented social networking website voted statistical analysis and data mining the top skill that got people hired in the year 2014. Data science has a bright future ahead there will only be more data and more of a need for people who can find meaning and value in that data. Despite the growing opportunity, demand for data scientist has outpaced supply of talent and will for the next five years. [1] McKinsey Global Institute, â€Å"Big data: The next frontier for innovation, competition, and productivity†, June 2011 [2] Thomas H. Davenport, D.J. Patil, â€Å"Data Scientist: The Sexiest Job of the 21st Century†, Harvard Business Review, October 2012 [3] Burtch Works â€Å"Big Data Career Tips† http://www.burtchworks.com/big-data-analyst-salary/big-data-career-tips/, accessed December 2014

Free Native Son Essays: Bigger :: Native Son Essays

Native Son: Bigger    In his most famous novel, Native Sun, Richard Wright successfully develops three major themes: Racism, violence as a personal necessity, and social injustice. He has captured the powerful emotions and suffering, the frustrations and yearnings, the restlessness and hysteria, of all the Bigger Thomas's in this grippingly dramatic novel.      Ã‚  Ã‚  Ã‚   Wright shows to us, through Bigger Thomas, how bad things were for the black race. He tells how Bigger was raised in a one ªroom apartment, living with his family and rats. The rent was very high, and his mother was barely able to pay it. Bigger's education like most blacks at that time , did not exceed the eighth grade. Without the help of the Relief Agency, Bigger and his family may not have been able to keep up much longer financially. Bigger had no money, except for the spare change his mother gives him, so he would usually just hang out at the pool hall, which was in the black district, or southside.      Ã‚  Ã‚  Ã‚   Bigger used to pull little jobs with his friends, but all of them including Bigger wanted to pull off a big job, by robbing Blum's store. They were afraid though, of getting caught for robbing a white man. They know the police don't care about blacks, and would probably accuse them of many more crimes. Luckily for Bigger, though, the Relief Agency did find him a job with the Daltons. When Bigger went to the Daltons house for the first time, he brought his gun, because it made him feel equal to the white people.      Ã‚  Ã‚  Ã‚   When Bigger got to the Daltons house, he didn't know whether to enter the house by the front or back door. He looks for a way to the back, and realizes the only way in is through the front door. As he rang the doorbell, he felt very disturbed. And when he started talking to Mr. Dalton, Mr. Dalton asks Bigger about his past crimes, which made Bigger feel pressured. Then Mary Dalton walked in and asked Bigger if he was in a union, if he knew about communism, and then still more questions, until her father finally asked her to leave the room. Bigger was afraid that this little brat was going to get him to lose his job. Then he met Peggy, a maid, Who asks Bigger all these questions, like he could understand what Free Native Son Essays: Bigger :: Native Son Essays Native Son: Bigger    In his most famous novel, Native Sun, Richard Wright successfully develops three major themes: Racism, violence as a personal necessity, and social injustice. He has captured the powerful emotions and suffering, the frustrations and yearnings, the restlessness and hysteria, of all the Bigger Thomas's in this grippingly dramatic novel.      Ã‚  Ã‚  Ã‚   Wright shows to us, through Bigger Thomas, how bad things were for the black race. He tells how Bigger was raised in a one ªroom apartment, living with his family and rats. The rent was very high, and his mother was barely able to pay it. Bigger's education like most blacks at that time , did not exceed the eighth grade. Without the help of the Relief Agency, Bigger and his family may not have been able to keep up much longer financially. Bigger had no money, except for the spare change his mother gives him, so he would usually just hang out at the pool hall, which was in the black district, or southside.      Ã‚  Ã‚  Ã‚   Bigger used to pull little jobs with his friends, but all of them including Bigger wanted to pull off a big job, by robbing Blum's store. They were afraid though, of getting caught for robbing a white man. They know the police don't care about blacks, and would probably accuse them of many more crimes. Luckily for Bigger, though, the Relief Agency did find him a job with the Daltons. When Bigger went to the Daltons house for the first time, he brought his gun, because it made him feel equal to the white people.      Ã‚  Ã‚  Ã‚   When Bigger got to the Daltons house, he didn't know whether to enter the house by the front or back door. He looks for a way to the back, and realizes the only way in is through the front door. As he rang the doorbell, he felt very disturbed. And when he started talking to Mr. Dalton, Mr. Dalton asks Bigger about his past crimes, which made Bigger feel pressured. Then Mary Dalton walked in and asked Bigger if he was in a union, if he knew about communism, and then still more questions, until her father finally asked her to leave the room. Bigger was afraid that this little brat was going to get him to lose his job. Then he met Peggy, a maid, Who asks Bigger all these questions, like he could understand what

G4S Competitive Forces

According to â€Å"Management, 10th Edition† and Michael Porter’s model, when speaking in terms of competitive forces it should be viewed in five different areas; the first being the threat of new entrants. As for a company such as G4S Secure Solutions, the threat from new companies are pretty much non-existent. It being the top security firm worldwide, its threats comes from its larger competitors that are pretty much on their level and well established. Start-up security companies are challenged by the behemoths that dominate the market which are able to overshadow them by offering one stop shopping for multiple security needs.Also the smaller companies have often been taken over by the larger companies. According to Security Guard Magazine (www. securitymagazine. com), two in 10 of guarding firms say they purchased another firm in 2002, while 10 percent say they completed the purchase of another firm in the first half of last year. New companies do not appear to pose a threat to G4S Secure Solutions as they are well established and far ahead of its established competitors. The second of the competitive forces having an impact on the company is its competitive rivalries. The biggest rivalry to G4S Secure Solutions is Securitas Security Services.These two companies offer the same services and both also operate overseas which allows them to compete for business in the same markets. They often end up bidding against each other for contracts vying to provide the exact service for the least amount of dollars. Securitas Security Services entered the U. S. market in 1999 by acquiring Pinkerton and became the largest security firm in the world; they were already the leading protective service company in Europe. They have acquired numerous security firms; in one year alone they had acquired four firms.With them continuing to grow in assets and size, they continue to prove themselves as the most formidable rivalry in this market. The threat of substitute products also has an affect on the company. The consumers are always looking for a better or less costly product that could provide the same service. Businesses are also looking for the next big thing to revolutionize the market. There are some substitute products that have an impact on the company such as guard dogs, security cameras, rolling shutters, security tags, and added lighting.Guard dogs and rolling shutters eliminate the need for security officers altogether and they are a more cost efficient way to provide security. Department stores are now using security tags instead of officers; if someone leaves the store without the tag being removed it will activate an alarm at the door. Some people have the belief that just adding extra lighting to an area ensuring that it is well-lit can also deter burglars or criminals. Depending on the level of security needed, all of these products can be used as a substitute product.As a competitive force, the buyer does not influence what th e company sells, but the company has power over the buyer. This is a well established company that is known to provide top notch service; which also happens to be the top security firm. They do not have to continually lower their fees in order to attract customers. Their reputation is outstanding and many times, consumers may be willing to pay the extra dollars because they know they will be getting premium service with this company.When it comes to G4S Secure Solutions the power is in the hands of the supplier; they have their reputation to go own as they easily have the ability to influence potential buyers. Their resume speaks for itself, the largest security firm, servicing more places worldwide than any other firm, possibly the best trained personnel of security firms, and more trusted by the most prestigious companies than any other security firm. With a background as such, G4S Secure Solutions definitely has the competitive edge when it comes to luring coustomers.

Hebrew Syllabus

Hebrew Syllabus IBMYP LEVEL: Level 4 and 5Mrs. Orit cohen2014s of such activities include but are not limited to the following definitions:A. CheatingUsing or attempting to use unauthorized assistance, material, or study aids in examinations or other academic work or preventing, or attempting to prevent, another from using authorized assistance, material, or study aids. Example: using a cheat sheet in a quiz or exam, altering a graded exam and resubmitting it for a better grade, etc.B. PlagiarismUsing the ideas, data, or language of another without specific or proper acknowledgment. Example: copying another person's paper, article, or computer work and submitting it for an assignment, cloning someone else's ideas without attribution, failing to use quotation marks where appropriate, etc.C. FabricationSubmitting contrived or altered information in any academic exercise. Example: making up data for an experiment, fudging data, citing nonexistent articles, contriving sources, etc.D. Multiple Submission sMultiple submissions: submitting, without prior permission, any work submitted to fulfill another academic requirement.E. Misrepresentation of academic recordsMisrepresentation of academic records: misrepresenting or tampering with or attempting to tamper with any portion of a student's transcripts or academic record, either before or after coming to Scheck Hillel Community Day School. Example: forging a change of grade slip, tampering with computer records, falsifying academic information on one's resume, etc.F. Facilitating Academic DishonestyKnowingly helping or attempting to help another violate any provision of the Code. Example: working together on a take: gaining or providing unauthorized access to examination materials, obstructing or interfering with another student's efforts in an academic exercise, lying about a need for an extension for an exam or paper, continuing to write even when time is up during an exam, destroying or keeping library materials for one's own use., etc.* If a student is unsure whether his action(s) constitute a violation of the Code of Academic Integrity, then it is that student's responsibility to consult with the instructor to clarify any ambiguities.Citation: Penn: Academic Integrity at Penn. (n.d.). Penn: University of Pennsylvania. Retrieved June 25, 2013, fromupenn.edu/academicintegrity/ai_codeofacademicintegrity.htmlTurnitin.com:This site is used by ALL INSTRUCTORS at Hillel for turning in end of unit essays, projects, etc. In addition, a teacher reserves the right to submit ANY student work to the service at their discretion, and check other sites to authenticate student work.Technology policy:Each student has signed and acknowledged the appropriate use policy for technology, and will be held to the standards identified in this document.Acknowledgement:I understand the contents of this syllabus, and will abide by the conditions set forth herein.Student signature: Parent/guardian signature:______________________________ ____ __________________________________Date: ___________________________Appendix AScheck Hillel Community SchoolJewish Holiday Test, Quiz, HW andAthletic Games and Practices Policy 2014/15In an attempt to provide clarity with regards to our Test/Quiz/HW and Athletic Practice/Game policies both before and immediately after Jewish holidays please see this document with all the details, dates and policies per Jewish holiday over the course of the year.Please review this detailed list carefully and let me know if you have any questions or concerns.If you would like an explanation as to what each of these holidays are all about I would be happy to sit with you and learn.Rosh Hashana:Wed. Sept. 24 - day before Rosh Hashana/No Classes - HW may be given that day that is due for Mon. Sept. 29. No athletic practices or gamesThurs.-Fri. Sept. 25/26 - Rosh Hashana - No Classes - No HW can be done on these daysTzom Gedalia:Sun. Sept. 28 - No athletic practices or gamesYom Kippur:Fri. Oct. 3 - da y before Yom Kippur - No Classes - HW may be given that day that is due for Mon. Oct. 6Sat. Oct. 4 - Yom Kippur - No ClassesSukkot/Simchat Torah:Wed. Oct. 8 - day before Sukkot - No Classes - No HW can be given that day that is due for Mon. Oct. 13Thurs-Fri. Oct. 9/10 - First Days of Sukkot - No ClassesMon.-Tues. Oct. 13/14 - Chol HaMoed Sukkot - No tests or quizzes. Homework can be given if it is absolutely necessary but no HW should be given those days that are due for Mon. Oct. 20. Yes to athletic practices and gamesWed. Oct. 15 - Day before Shemini Atzeret - No Classes - No HW should be given that day that is due for Mon. Oct. 20. No athletic practices and gamesThurs.-Fri. Oct. 16/17 - Shemini Atzeret/Simchat Torah - No ClassesChanukah:Tues. Dec. 16 - Eve of Chanukah - No HW should be given that is due during the week of Chanukah. You can give tests and quizzes on this dayWed. - Wed. Dec. 17 - 24 - Chanukah - No tests, quizzes or HW. Yes to athletic practices and gamesTaanit Est her:Wed. Mar. 4 - Fast of Esther - No tests or quizzes should be given. No athletic practices or gamesPurim:Thurs./Fri. Mar. 5/6 - Purim/Shushan Purim - No tests or quizzes should be given. No athletic practices or gamesPesach:Wed. April 1 - day before Passover - No HW, assignments or projects should be given that day that are due for Mon. April 13Thurs. April 2 - Sun. April 12 - Passover - No classesYoms:Wed. April 15 - Yom HaShoah (Holocaust Remembrance Day) - No athletic practices or games. You can give tests, quizzes and HWWed. April 22 - Yom HaZikaron (Israel Memorial Day) - No HW should be given that day that is due on Thurs. April 23. You can give tests, quizzes and HW. No athletic practices or gamesThurs. April 23 - Yom HaAtzmaut (Israel Independence Day) - No test, quizzes or HW. No athletic practices or gamesShavuot:Fri. May 22 - No HW should be given that day that is due for Tues. May 26