Comprehensive Guide on Data Mining (and Data Mining Techniques)

Comprehensive Guide on Data Mining (and Data Mining Techniques) © Shutterstock.com | ScandinavianStockJust hearing the phrase “data mining” is enough to make your average aspiring entrepreneur or new businessman cower in fear or, at least, approach the subject warily. It sounds like something too technical and too complex, even for his analytical mind, to understand.Out of nowhere, thoughts of having to learn about highly technical subjects related to data haunts many people. Many cave in and just opt to find other people to take care of that aspect for them. Worse, in other cases, they pay little attention to it, thinking they can get away with not having anything to do with data mining in their business.Once they try to understand what data mining really is, they will realize that it is something that cannot be ignored or overlooked, since it is part and parcel of the management of a business or organization.Businesses cannot do away with implementing or applying various business intelligence methodologies, applications and technologies in order to gather and analyze data providing relevant information about the market, the industry, or the operations of the business. It just so happens that data mining is one of the most important aspects of business intelligence.WHAT IS DATA MINING?Forget about any highly technical definition you may associate with data mining and let us look at it for the relatively simple concept that it truly is. Data mining is basically the process of subjecting available data to analysis by looking at it from different perspectives, to convert it into information that will be useful in the management of a business and its operations.A simple way to describe data mining is that it is a process that aims to make sense of data by looking for patterns and relationships, so that it can be used in making business decisions.For the longest time, many people have associated data mining with the image of a set of high-end computers utilizing equally high-end software and technology to obtain data and p rocess them. This isn’t entirely wrong, because technology is definitely a huge and integral part of data mining. However, data mining is actually a broader concept, not just limited to the use of technology and similar tools.Perhaps one of the biggest reasons why many are intimidated by the very mention and idea of data mining is the fact that it involves more than one or two disciplines. When we talk of data mining, we are talking about database management and maintenance, which automatically means the involvement or use of database software and technologies. Thus, it also often entails machine learning and heavy reliance on information science and technology.Further, the analysis of data, especially of the numerical kind, is bound to make use of statistics, which is another area that some people find complicated. This will also demand a lot in terms of visualization.In short, being involved in data mining implies dipping one’s fingers and toes in more than a few rivers, so to speak, since it entails the use or application of multiple disciplines. This is what often makes data mining a challenge in the eyes of most people.We can gain a deeper understanding of what data mining is by talking about its five major elements.Extraction, transformation and uploading of the data to a data warehouse system.Data storage and management in a database system.Data access to analysts and other users.Data analysis using various software, tools and technologies.Data presentation in a useful and comprehensible format.IMPORTANCE OF DATA MININGBusinesses, organizations and industries share the same problems when it comes to data. Either they aren’t able the find the data that they require or, even if they know where to find it, they have difficulty actually getting their hands on it. In other cases, they may have access to the data, but they cannot understand it. Worse, the data may be readily available to them, and they may be able to have comprehension of it.However, fo r some reason or another, they find that they are unable to use the data.This is where data mining comes in.The main reason why data mining is very important is to facilitate the conversion of raw data into information that, in turn, will be converted into knowledge applicable for decision-making processes of businesses.Data mining has become increasingly important, especially in recent years, when nearly all industries and sectors all over the world are facing problems on data explosion. All of a sudden, there is simply too much data, and this rapid rise in the amount of data demands a corresponding increase in the amount of information and knowledge. Thus, there is a need to quickly, efficiently and effectively process all that data into usable information, and data mining offers the solution. In fact, you could say that data mining is the solution.You will find data mining to be most often used or applied in organizations or businesses that maintain fairly large to massive databa ses. The sheer size of their databases and the amount of information contained within them require more than a small measure of organization and analysis, which is where data mining comes in. Through data mining, users are able to look at data from multiple perspectives in their analysis. It will also make it easier to categorize the information processed and identify relevant patterns, relationships or correlations among the various fields the data or information belong to.Therefore, we can deduce that data mining involves tasks of a descriptive and predictive nature. Descriptive, because it involves the identification of patterns, relationships and correlations within large amounts of data, and predictive, because its application utilizes variables that are used to predict their future or unknown values. APPLICATIONS OF DATA MININGThe application of data mining is apparent across sectors and industries.Retail and ServiceThe sale of consumer goods and services in the retail and ser vice industries results in the collection of large amounts of data. The primary purpose of using data mining in these industries is to improve the firm’s customer relationship management, its supply chain management and procurement processes, its financial management, and also its core operations (which is sales).The most common areas where data mining becomes highly effective among retail and service provider companies include:Promotion Effectiveness Analysis, where the company will gather and analyze data on past successful (and unsuccessful or moderately successful) campaigns or promotions, and the costs and benefits that the campaigns provided to the company. This will give the firm an insight on what elements will increase the chances of a campaign or promotion being successful.Customer Segmentation Analysis, where the firm will take a look at the responses of the customers â€" classified in appropriate segments â€" to shifts or any changes in demographics or some other segme ntation basis.Product Pricing, where data mining will play a vital role in the firm’s product pricing policies and price models.Inventory Control, where data mining is used in monitoring and analyzing the movements in inventory levels with respect safety stock and lot size. Lead time analysis also greatly relies on data mining.Budgetary Analysis, where companies will need to compare actual expenditures to the budgeted expenses. Incidentally, knowledge obtained through data mining will be used in budgeting for subsequent periods.Profitability Analysis, where data mining is used to compare and evaluate the profitability of the different branches, stores, or any appropriate business unit of the company. This will enable management to identify the most profitable areas of the business, and decide accordingly.ManufacturingEssentially, the areas where data mining is applied in manufacturing companies are similar to those in retail and service companies. However, manufacturing businesses also use data mining for its quality improvement (QI) initiatives, where data obtained through quality improvement programs such as Six Sigma and Kaizen, to name a few, are analyzed in order to solve any issues or problems that the company may be having with regards to product quality.Finance and InsuranceBanks, insurance companies, and other financial institutions and organizations are also actively using data mining in its business intelligence initiatives. Risk Management is generally the area where data mining is most utilized. This time, data mining is used to recognize and subsequently reduce credit and market risks that financial institutions are almost always faced with. Other risks assessed with the help of data mining include liquidity risk and operational risk.For example, banks and credit card companies use data mining for credit analysis of customers. Insurance companies are mostly concerned with gaining knowledge through claims and fraud analysis.Telecommunication and UtilitiesOrganizations engaged in providing utilities services are also recipients of the benefits of data mining. For example, telecommunication companies are most likely to conduct call record analysis. Electric and water companies also perform power usage or consumption analysis through data mining.The global popularity of cellular phones in almost all transactions has made it a playground for many hackers and security threats. This spurred Coral Systems, a Colorado-based company, to create FraudBuster, which is described to be able to “track” down the types of fraud through data mining, specifically through analysis of cellular phone usage patterns in relation to fraud.TransportIn the transport industry, it is mainly all about logistics, which is why that is the area where data mining is most applied. Thus, logistics management benefits greatly from data mining. State or government transport agencies are also using data mining for its various projects, such as road construc tion and rehabilitation, traffic control, and the like.PropertyThe real estate industry heavily relies on information gleaned from property valuations which, in turn, resulted from the application of data mining. The focus is not entirely on the bottomline or the sales. Instead, data on property valuation trends over the years, as well as comparison on appraisals, are tackled.Healthcare and Medical IndustryEvery day, researches, studies and experiments are conducted in the healthcare and medical industry, which implies that there are tons of data being generated every single day. Data mining is often an integral part of those researches and studies.STEPS IN DATA MININGData mining is a process, which means that anyone using it should go through a series of iterative  steps or phases. The number of steps vary, with some packing the whole process within 5 steps. The one below involves 8 steps, primarily because we have broken down the phases into smaller parts. For example, steps #2 th rough #5 are lumped by other sources as a single step, which they call “Data Pre-processing”.For purposes of this discussion, however, let us take each step one at a time.Step #1: Defining the ProblemBefore you can get started on anything, you have to define the objectives of the data mining process you are about to embark on. What do you hope to accomplish with the data mining process? What problems do you want to address? What will the organization or business ultimately obtain from it as benefit?Step #2: Data IntegrationIt starts with the data, or the raw tidbit about an item, event, transaction or activity.The goal is to provide the users (those who are performing data mining) a unified view of the data, regardless of whether they are from single or multiple sources.This step involves:Identification of all possible sources of data. Chances are high that the initial list of sources will be quite long and heterogeneous. Integrating these data sources will save you a lot of tim e and resources later on in the process.Collection of data. Data are gathered from the sources previously identified and integrated. Usually, data obtained from multiple sources are merged.Data integration aims to lower the potential number and frequency of data redundancy and duplications in the data set and, consequently, improve the efficiency (speed) and effectiveness (accuracy) of the data mining process.Step #3: Data SelectionAfter the first step, it is highly probable that you will be faced with a mountain of data, a large chunk of which are not really relevant or even useful for data mining purposes. You have to weed out those that you won’t need, so you can focus on the data that will be of actual use later on.Create a target data set. The target data set establishes the parameters of the data that you will need or require for data mining.Select the data. From all the data gathered, identify those that fall within the data set you just targeted. Those are the data you wil l subject to pre-processing.Step #4: Data CleaningAlso called “data cleansing” and “data scrubbing”, this is where the data selected will be prepared and pre-processed, which is very important before it can undergo any data mining technique or approach.Some data mining processes refer to data cleaning as the first of a two-step data pre-processing phase.Data obtained, in their raw form, have a tendency to contain errors, inaccuracies and inconsistencies. Some may even prove to be incomplete or missing some values. Basically, the quality of the data is compromised. It is for these reasons that various techniques are employed to “clean” them up. After all, poor or low quality data is unreliable for data mining.One of the biggest reasons for these errors is the data source. If data came from a single source, the most common quality problems that require cleaning up are:Data entry errors, mostly attributed to ‘human’ factor, or error of the person in charge of the input of data into the data warehouse. They could range from simple misspellings to duplication of entries and data redundancy.Lack of integrity constraints, such as uniqueness and referential integrity. Since there is only one source of data, there is no way of ascertaining whether the data is unique or not. In the same way, duplication and inconsistency may arise due to the lack of referential integrity.Similarly, data obtained from multiple sources also have quality problems.Naming conflicts, often resulting from the fact that there are multiple sources of the same data, but named differently. The risk is that there may be data duplication brought about by the different names. Or it could be the other way around. More than one or two sources may use the same name for two sets of data that are completely unrelated or different from each other.Inconsistent aggregating, or contradictions arising from data being obtained from different sources. Duplications of data may result to them cance ling each other out.Inconsistent timing, where data may tend to overlap among each other, resulting to more confusion. The data then becomes unreliable. For example, data on shopping history of a customer may overlap when sourced from various shopping sites or portals.Cleaning up data often involves performing data profiling, or examining the available data and their related statistics and information, to determine their actual content, quality and structure.Other techniques used are clustering and various statistical approaches. Once the data has been cleaned, there is a need to update the record with the clean version.Step #5: Data TransformationThis is considered to be the second data pre-processing step. Other authors even describe data transformation as part of the data cleaning process.Despite having “cleaned” the data, they may still be incapable of being mined. To make the clean data ready for mining, they have to be transformed and consolidated accordingly. Basically, t he source data format is converted into “destination data”, a format recognizable and usable when using data mining techniques later on.The most common data transformation techniques used are:Smoothing. This method removes “noise” or inconsistencies in data. “Noise” is defined as a “random error or variance in a measured variable. Smoothing often entails performing tasks or operations that are also performed in data cleaning, such as:Binning. In this method, smoothing is done by referring to the ‘neighborhood’ of the chosen data value, and categorically distribute them in ‘bins’. This neighborhood essentially refers to the values around the chosen data value. Sorting the values in bins or buckets will smooth out the noise.Clustering. This operation is performed by organizing values into clusters or groups, ordinarily according to a certain characteristic or variable. In short, data values that are similar will belong to one cluster. This will smooth and remove any data noise.Regression. As a method for smoothing noise in data values, linear regression works by determining the best line to fit two variables and, in the process, improve their predictive value. Multiple regression, on the other hand, also works, but involves more than two variables.Aggregation. This involves the application of summarization tactics on data to further reduce its bulk and streamline processes. Usually, this operation is used to create a data cube, which will then be used later for analysis of data. A common example is how a retail company summarizes or aggregates its sales data periodically per period. Therefore, they have data on daily, weekly, monthly and annual sales.Generalization. Much like aggregation, generalization also leads to reduction of data size. The low-level or raw data are identified and subsequently replaced with higher-level data. An example is when data values on customer age is replaced by the higher level data concept of grouping them as pre-teen, teen, middle-aged, and senior. In a similar manner, raw data on families’ annual income may be generalized and transformed into higher-level concepts such as low-level, mid-level, or high-income level families.Normalization or Standardization. Data variations and differences can also have an impact of data quality. Large gaps can cause problems when data mining techniques are finally applied. Thus, there is a need to normalize them. Normalization is performed by specifying a small and acceptable range (the standard), and scaling the data in order to ensure they fall within that range.Examples of normalization tactics employed are Min-Max Normalization, Z-Score Normalization, and Normalization by Decimal Scaling.Step #6: Data MiningData mining techniques will now be employed to identify the patterns, correlations or relationships within and among the database. This is the heart of the entire data mining process, involving extraction of data patterns using various methods and operations.The choice on which data mining approach or operation to use will largely depend on the objective of the entire data mining process.The most common data mining techniques will be discussed later in the article.Step #7: Pattern EvaluationThe pattern, correlations and relationships identified through data mining techniques are inspected, evaluated and analyzed. Evaluation is done by using “interestingness” parameters or measures in figuring out which patterns are truly interesting and relevant or impactful enough to become a body of useful knowledge.The interpretation in this stepwill formally mark the transformation of a mere information into an entire “bag of knowledge”.Step #8: Knowledge PresentationThe knowledge resulting from the evaluation and interpretation will now have to be presented to stakeholders. Presentation is usually done through visualization techniques and other knowledge representation mechanisms. Once presented, the knowledge may, or will, b e used in making sound business decisions. DATA MINING TECHNIQUESOver the years, as the concept of data mining evolved, and technology has become more advanced, more and more techniques and tools were introduced to facilitate the process of data analysis. In Step #5 of the Data Mining process, the mining of the transformed data will make use of various techniques, as applicable.Below are some of the most commonly used techniques or tasks in data mining, classified whether they are descriptive or predictive in nature.Descriptive Mining TechniquesClustering or Cluster AnalysisClustering is, quite possibly, one of the oldest data mining techniques, and also one of the most effective and simplest to perform. As briefly described earlier, it involves grouping data values that have something in common, or have a similarity, together in a meaningful subset or group, which are referred to as “clusters”.The grouping or clustering in this technique is natural, meaning there are no predefi ned classes or groups where the data values are distributed or clustered into.Perhaps the most recognizable example of clustering used as a data mining tool is in market research, particularly in market segmentation, where the market is divided into unique segments. For instance, a manufacturer of cosmetic and skin care products for females may cluster its customer data values into segments based on the age of the users. Most likely the main clusters may include teens, young adults, middle age and mature.Association Rule DiscoveryThe purpose of this technique is to provide insight on the relationships and correlations that associate or bind a set of items or data values in a large database. Analysis of data is done mostly by looking for patterns and correlations.Customer behavior is a prime example of the application of Association Rules in data mining. Businesses analyze customer behavior in order to make decisions on key areas such as product price points and product features to b e offered.Incidentally, this technique may also be predictive, such as when it is used to predict customer behavior in response to changes. For example, if the company decides to launch a new product in the market, how will the consumers receive it? Association Rules may help in making hypotheses on how the customers will accept the new product.Sequential Pattern DiscoveryThis mining technique is slightly similar to the Association Rule technique, in the sense that the focus is on the discovery of interesting relationships or associations among data values in a database. However, unlike Association Rule, Sequential Pattern Discovery considers order or sequence within a transaction and even within an organization.Sequence Discovery or Sequence Rules is often applied to data contained in sequence databases, where the values are presented in order. In the example about customer behavior, this technique may be used to get a detailed picture of the sequence of events that a customer foll ows when making a purchase. He may have a specific sequence on what product he purchases first, then second, then third, and so on.Concept or Class DescriptionThis technique is straightforward enough, focusing on “characterization” and “discrimination” (which is why it is also referred to often as the Characterization and Discrimination technique. Data, or its characteristics, are generalized and summarized, and subsequently compared and contrasted.A data mining system is expected to be able to come up with a descriptive summary of the characteristics or data values. That is the data characterization aspect.For example, a company planning to expand its operations overseas is wondering which location would be most appropriate. Should they open an overseas branch in a county that experiences precipitation and storms for a greater half of the year, or should they pick a location that is mostly dry and arid throughout the year? Data characteristics on these two regions will be l ooked into for their descriptions, and then compared (or discriminated) for similarities and differences.Predictive Mining TechniquesClassificationThis method has several similarities with Clustering, which leads many to assume that they are one and the same. However, what makes them different is how, in Classification, there are already predetermined and pre-labeled instances, groups or classes. In clustering, the clusters are defined first, and the data values are put into the clusters they belong to. In classification, there are already pre-defined groups and, of course, it in these groups where the data values will be sorted into.In Classification, the data values will be segregated to the grouping or instances and be used in making predictions on how each of the data values will behave, depending on that of the other items within the class.An example is in medical research when analyzing the most common diseases that a country’s population suffers from. The classifications of diseases are already existing, and all that is left is for the researchers to collect data on the symptoms suffered by the population and classify them under the appropriate types of diseases.Nearest Neighbor AnalysisThis predictive technique is also similar to clustering in the sense that it involves taking the chosen data value in context of the other values around it. While clustering involves data values in extremely close proximity with each other, seeing as they belong to the same cluster, the nearest neighbor is more on the nearness of the data values being matched or compared to the chosen data value.In the cosmetic and skin care product manufacturing company example cited above, this technique may be used when the company wants to figure out which of their products are the bestsellers in their many locations or branches. If Product A is the bestseller in Location 1, and Location 10 is where Product J is selling like hot cakes, then the chances are greater that Location 2, which is nearer to Location 1 than Location 10 is, will also record higher sales for Product A more than Product J.RegressionRegression techniques come in handy when trying to determine relationships dependent and independent variables. It is a popular technique primarily because of its predictive capabilities, which is why you are likely to see it applied in business planning, marketing, budgeting, and financial forecasting, among others.Simple linear regression, which contains only one predictor (independent variable) and one dependent variable, resulting to a prediction. Presented graphically, the regression model that demonstrates a shorter distance or line between the X-axis (the predictor) and the Y-axis (the prediction or data point) will be the simple linear regression model to be used for predictive purposes.Multiple linear regression, which aims to predict the value of the responses or predictions with respect to multiple independent variables or predictors. Compared to th e simple regression, this is fairly more complicated and work-intensive, since it deals with a larger data set.Regression analysis is often used in data mining for purposes of predicting customer behavior in making purchases using their credit cards, or making an estimate of how long a manufacturing equipment will remain serviceable before it requires a major overhaul or repair. In the latter example, the company may plan and budget its expenditure on repairs and maintenance of equipment accordingly, and maybe even assess the feasibility of purchasing a new equipment instead of repeatedly spending more money on maintenance of the old one.So, now here is the fun stuff (hint: its the video :-). Decision TreesWhat makes this predictive technique very popular is its visual presentation of data values in a tree. The tree represents the original set of data, which are then segmented or divided into the branches, with each leaf representing a segment. The prediction is the result of a seri es of decisions, presented in the tree diagrams as a Yes/No question.What makes this model even more preferred is how the segments come with descriptions. This versatility â€" offering both descriptive and predictive value in an easy-to-understand presentation â€" is the main reason why decision trees are gaining much traction in data mining and database management, in general.Outlier AnalysisIn instances where there are already established models or general behavior expected from data objects, data mining may be done by taking a look at the exceptions or, in this case, what we call the “outliers”. These are the data objects that do not fall within the established model or do not comply with the expected general behavior. The result of these deviations may prove to be data that can be used as a body of knowledge later on.A classic example of applying outlier analysis is in credit card fraud detection. The shopping history of a specific customer already provides an e-tailer (onli ne retail store) a set of general behavioral data to base on. When trying to find if the fraudulent purchases have been made using the credit card of that customer, the focus of the analysis will be unusual purchases in his shopping history, such as surprisingly large amounts spent on a single purchase, or the unusual purchase of a specific item that is completely unrelated to all previous purchases.If the customer, for the past three years, has made a purchase at least once in every 2 months, a single month with the customer purchasing more than two or three times is enough to raise a red flag that his credit card may have been stolen and being improperly and fraudulently used.Evolution AnalysisWhen the data to be subjected to mining inherently changes or evolves over time, and the goal is to establish a clear pattern that will help in predicting the future behavior of the data object, a recommended approach is evolution analysis.Evolution analysis involves the identification, desc ription and modeling of trends, patterns and other regularities with respect to the behavior of data objects as they evolve or change. Thus, you will often find this applied the mining and analysis of time-series data. Stock market trends, specifically on stock prices in the stock market, are subjected to time-series analysis. The output will enable investors and stock market analysts to predict the future trend of the stock market, and this will ultimately guide them in making their stock investment decisions.There are a lot of other techniques used in data mining, and we named only a few of the most popular and the most commonly used approaches. Application of these techniques also require the use of other disciplines and tools, such as statistics, mathematics, and software management.The success of a business rides a lot on how good management is at decision-making. And let us not forget that a decision will only be as good as the quality of the information or knowledge tapped in to by the decision-makers. High quality information will rely heavily on how the collection, processing and evaluation of data. If data mining was unsuccessful or less than effective in the first place, then there is a great chance that the resulting “bag of knowledge” will not be as accurate and effective as well, and poor business decisions may be arrived at.

My love for Beethoven - Free Essay Example

Sample details Pages: 3 Words: 962 Downloads: 8 Date added: 2019/07/30 Category People Essay Level High school Tags: Ludwig van Beethoven Essay Did you like this example? I went to my classical music concert on December 2, 2018 at 1 PM! It was through the Colorado Symphony at the Boettcher Concert Hall. I arrived to see an All Beethoven performance with Hans Graf as the conductor and Inon Barnatan as the pianist. It consisted of Overture to Egmont, Op. Don’t waste time! Our writers will create an original "My love for Beethoven" essay for you Create order 84; Piano Concerto No, 3 in C minor, Op. 37allegro con brio, largo, rondo: allegro; and Symphony No. 6 in F major, Op. 68, Pastoral. I have never been to an orchestra type concert so this was my very first time. Upon arriving downtown, the streets surrounding the concert hall were very crowded. It was a congested area and took a while to drive only a couple blocks to park in the halls parking garage. There were people of all agesyounger, middle aged, older, couples, families with kids. Everyone was dressed nicely. My ticket was scanned and I was led to my seat by a concert hall employee. I was told that I could only step out during intermission. I took a few pictures and a very short video because as I was taking the video, the announcer stated that taking pictures was not allowed. The stage was in the center with seats all around the stages at 360?‚Â °. The concert hall was about 50% packed, I imagine it being a Sunday afternoon had a lot to do with it. This was the final con cert in a total of three showingsthe first and second having been on Friday and Saturday at 7 PM. The composer for all three pieces was Beethoven! The first piece performed was Overture to Egmont, Op. 84. It consists of a triple meter, polyphonic texture, and diatonic scale. It is a triple meter because it starts out as the beat being strong, weak, weak. This piece was performed by a full symphony orchestra and held parts for instruments of all groups (i.e. strings, winds, brass).This piece is about the Count Egmont story, the Netherlands revolution against the Spanish inquisition in the 16th century. The theme of this piece is victory. Form of AABA was used by him and the tonality of this piece is in F major. I really enjoyed the piece, I felt there was an intensity and darkness within this piece particularly the low volume of the cello/strings initially. It is interesting to hear the combination of music and history within this piece. This piece was very well done and you can hear the theme being followed really well throughout. I especially loved the ending. The second piece performed was Piano Concerto No, 3 in C minor, Op. 37. This piece consisted of three movements (allegro con brio, largo, rondo allegro) and was about ~ 34 minutes long. The solo piano portion was completed by Inon Barnatan. Other instruments in this piece included was stringsviolins, cellos, trumpets, oboes, flutes, clarinets, horns, timpani, and bassoons. The strings introduce the theme and are used throughout the movement of allegro con brio. Although I enjoyed the first piece the best I also liked this piece. The orchestra was louder and more dramatized whereas the piano solo was softer and added a beautiful element to the piecethe back and forth from the orchestra to the piano was well done and a beautiful transition. I liked how Inon Barnatan swayed his body with the music. You could see his body posture and facial expressions matching the emotions of the music. Lastly, the piece played was Symphony No. 6 in F major, Op. 68, Pastoral which was also the longest of about ~45 minutes. After the first two pieces there was an intermission then this was performed. This piece consisted of five movements each movement receiving a title. The instruments consisted of woodwinds, strings, percussion, and brass. This piece depicts elements/scenes from nature. The first movement is in sonata form, and it is happy and very cheerful with great orchestral textures/harmonies. The second movement showcases the calling of birds with the use of the woodwinds. The third movement displays a fun folk dance, similar to the first movement in terms of repetition. The fourth movement depicts a thunderstorm which starts building in intensity from a nice, calm rain to loud thundering. The final movement returns to its home key of F major and is in sonata rondo form. After the climax, the listener is returned to the peaceful, tranquil environment of the first piece. I was initially liking the first piece performed the most but after hearing this oneit became my favorite! I loved this piece the most. It painted a beautiful picture and showcased the picture painting aspect of music that weve discussed in class. It was rather calm and peaceful initially but then dramatized ultimately returning to the calm scenery. Overall, I really enjoyed this concert. It was a brand new experience for me. It was interesting to see the orchestra set-up, the playing of instruments in unison, the musicians kept turning the pages to the music paper in front of them which I thought was cool because its cool that they are able to read music. It seems difficult and they followed along perfectly. I had previously listened to these pieces on YouTube for this class so hearing it in person intensified the emotions I felt. At some points, I got chills because of how powerful the music was. The conductor and pianist were wonderful. I went alone to the concert but had such a great time that I plan on taking my girlfriend sometime soon to share a symphony experience. This enhanced my love for Beethoven and brought forward even more so acknowledgement to how talented he was. His music is full of masterpieces. I particularly enjoy the visionary provided via the pieces. Thank you for this assignment, it was a breath of fresh air and wonderful new experience.

Argumentative Essay On Racism - 1326 Words

Growing up in a small town that consists of almost 90 percent of the same race, you don’t take into consideration the social diversity that goes on in the world around you. So, growing up in a family of white, I never really knew what it was like to truly see color. Color isn’t always vibrant and beautiful, sometimes it ugly, and I learned this the hard way. Racism is a word that we hear every day; whether it’s on the news, the internet, or even television shows, it’s literally all around us. Just because we hear the word, doesn’t mean we fully comprehend the significance of the meaning. Racism, by definition, is the practice of discriminating against people based on their race, national or ethnic background. Now knowing the definition of†¦show more content†¦You cannot judge one person on the actions of another. We must adapt to accepting each other, regardless of how â€Å"different† everyone may seem. I’ve never been oblivi ous to our history, I knew that slavery existed and that the world we live in is a cruel place, but I also knew that I was a privileged white girl. I had heard the stories, I knew who Rosa Parks was and I knew Martin Luther King had a dream, I understood all of it. But this time, I wasn’t just another student reading in my history text book of a situation that I would never have to be in. Rather, I was an eighteen-year-old white girl who fell in love with a black guy. My boyfriend is of the mixed ethnicity; he is Caucasian, African-American, and Hawaiian. Compared to me, he has a darker complexion and is clearly not fully Caucasian. To me, this never matter, but I didn’t realize how much it mattered to other people. We were in Tyler County, for the local fair, when Troy went to go get some food. I sat down beside some woman, while I waited for him to return. When he approached me, the woman grasped her purse and immediately pulled it against her chest, as if he was goi ng to steal it. I didn’t think much of it at first, but the more the day continued the moreShow MoreRelatedArgumentative Essay On Racism1710 Words   |  7 PagesRacism is the belief that one race is superior to another. Discrimination has been going on for generations among generations. Many years ago people of different races were divided from each other. Public places were segregated. Colored people had to use specific water fountains, schools were segregated, and blacks had to sit at the back of the buses. If they were to disobey then there would be consequences and repercussions. Equality was a figment of imagination, a dream the the minority groupsRead MoreArgumentative Essay About Racism1758 Words   |  8 Pagesâ€Å"There is nothing wrong with a little casual racism.† One of my friends recently commented this phrase to me, in a joking manner, but it struck me. 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How to Write a Critique Essay Free Essays

This guide looks at writing a critique essay (also known as a critical essay).A critique essay looks critically at a particular subject, area or topic. It means evaluating information, comparing and contrasting theories and analysing situations. We will write a custom essay sample on How to Write a Critique Essay or any similar topic only for you Order Now A critical essay does not mean being overly critical, it rather involves being able to challenge points of view and asking questions. Most further education courses involve writing essays of this type. How to Prepare for Writing a Critical Essay Understanding the title is particularly important in a critical essay. You need to deconstruct what you are being asked. First look for the underlying task you are asked to do (are you to produce an argument, argue for a position, or analyse a concept?). Next, identify the content words in the question: what subject are you to write about Also identify any limiting words in the question: what limits the scope of the essay Plan by creating a concept or mind map of your current knowledge and what you need to expand (see figure 1 for example mind map) A useful time-planner for writing a critical essay can be found here: http://www.jcu.edu.au/tldinfo/writingskills/documents/Critical_Essay_Planner.pdf How to Structure a Critical Essay Critique essays share the same structure as other types of essay, that is they should have an introduction, main body and conclusion. However, there are some features that distinguish the critique essay from other types: The introduction needs to include a thesis statement which identifies your position. You should also indicate briefly how you will argue for that position. The main body will present your argument logically and in a coherent way. You could use an appropriate paragraph structure for example starting each paragraph with a topic sentence (explaining the subject and main idea), follows this with one or more supporting sentence(s) (justifying the point you are making with evidence, critiquing opposing viewpoints) and end the paragraph with a conclusion which relates it back to the main question and thesis. The conclusion will summarise the main points of the essay, and relate the evidence discussed back to the original thesis. It may also consider the implications of the conclusions drawn, examine limitations, explore other relevant aspects and make suggestions. Critical Essay Skills You will need to display skills in analysis and the ability to critique in essays of this sort. Analysis involves a systematic and thorough approach to your topic, breaking ideas down into constituent parts, looking at how ideas work in isolation and in the context of a wider theoretical framework, and asking questions. Critical skills involve interpretation, evaluation, judgement and justifying; the ability to compare with other ideas; understanding how phenomena can be interpreted in different ways; and assessing arguments in terms of evidence for and against. The ability to construct an argument is key to successful critical writing. You should develop a line of reasoning which backs up your position. You also need to be able to identify and critique opposing positions. You should present your reasoning in a way which is clear and well structured, and flows logically. There are a number of general critical questions which apply to any text. Keep the following in mind to hone your approach to essay writing: How is this knownWhat makes the writer think it is true How reliable is this What is really going on here WhyHowWhen What has been left unsaid Which argument is stronger and why What is the main argument hereDo I agree with it(Why, Why Not?) Is this relevant How will I use this information How does this information relate to what I already know Bibliography James Cook University (2013) ‘What is a critical essay’, [online] (cited 13th February 2013) available from http://www.jcu.edu.au/tldinfo/writingskills/models/critical.html James Cook University (2013) ‘Guidelines for a critical essay’, [online] (cited 13th February 2013) available from http://www.jcu.edu.au/tldinfo/writingskills/documents/critical_essay_guidelines.pdf James Cook University (2013) ‘Critical Essay Planner’, [online] (cited 13th February 2013) available from http://www.jcu.edu.au/tldinfo/writingskills/documents/Critical_Essay_Planner.pdf Palgrave (2013) ‘Skill development guide: writing a critical essay’, http://www.palgrave.com/business/brattonob2e/student/docs/critical.pdf [online] (cited 13th February 2013) available from University of Bristol Union (2009) ‘Critical Thinking’, [online] (cited 13th February 2013) available from http://www.bristol.ac.uk/enhs/ct.pdf University of Sussex (2013) ‘Critical Writing’ [online] (cited 13th February 2013) available from http://www.sussex.ac.uk/s3/?id=122 How to cite How to Write a Critique Essay, Essay examples

Deyaar Development

Question: You are required to provide a brief historical background of Deyaar Development PJSC in terms of performance, benchmarking against industry, and major changes in the past or foreseeable future. Answer: Preface: Deyaar Development is a renowned name and a recognized brand face of the real estate industry. It is a public limited company listed on DFM (Dubai Financial Market). It is headquartered in Dubai (UAE). Deyaar got listed on DFM in September 2007. The prime focus of the company is full-fledged real estate activities and if the criteria of going global is considered, it have more than 36 subsidiaries across the world running successfully. Deyaar was established in 2002 and on origination, it was property management unit of DIB (Dubai Islamic Bank). Later, it got established as independent real estate company and their business marked organized structure to develop real estate properties. As today, the infrastructural development of Dubai is considered extra-ordinary in the world and beyond the shadow of doubt, credit counts for giant firms like Deyaar.(EMIS) In the words of CEO Saeed Al Qatami, Deyaar always keep the track of the time and continuously refresh the core services of the co mpany. The purpose behind doing this is to let customers stay happy and satisfied with their relation to Deyaar. The journey of becoming the trust-worthy and customer focused real estate developer have not been very smooth. It took a while for Deyaar to bring success, name and fame in real estate sector. (Deyaar) Strategy: It is always a milestone for any business to become one stop solution provider for customers in their expert area. Deyaar have strategized its policies to provide customers with supremacy of services and vision to create natural living habitat. The scope of services never ends with handling over the property because customers are always preferred and their needs are met with Deyaar facilities management services. Currently, Deyaar is service provider for facilities management for over 18000 plus residential and commercial spaces. In a nutshell, success key of the Deyaar business are the happy customers. CEO emphasis on the success criteria by saying that successful projects not only mark the expansion of business but also shape entities that are serving as sustainable sources of revenue. (Deyaar) Key strategies: Adaptability to the fast moving global business environment helps Deyaar stay ahead in the race. Good deals to customers keeping their financial stability in context and this adds valued customer to the business pipeline. Unrivalled service quality Prioritizing social responsibility over profits have raised the performance bars for Deyaar. Market intelligence and world-class services are the backbone to enjoy edge over competitors.(Deyaar) In financial language, there are certain set of ratios applied to understand the financial performance of any firm. For Deyaar, following ratios are taken from the end of first quarter for the year 2016: P/E Ratio: Definition: It refers to the ratio of the companys stock price to the companys earnings per share. High P/E ratio could be achieved through: -Innovation in business activities. -Profits of the business. -Quality of the management. -Good return on capital invested(money control) Deyaar status on P/E ratio: Deyaar with P/E ratio of 68.39 and perhaps best in real estate industry and with rising stock price have positive impacts on earning of the company. (English Mubasher) Return on Equity: Definition: ROE is a profitability ratio. It measures the ability of a firm to generate profits from its shareholders investments in the company. (My accounting course) ROE can never be interpreted to be on the basis of high or low because lot more depends on the debt-equity mix. Deyaar status on ROE: From the financials of Deyaar, it is observed that equity is higher is the debt-equity mix. It concludes that investment from shareholders is utilized in the best potential. Deyaar have good ROE of 4.33 in the first quarter end of 2016 and thus, is positive in terms of growth and expansion of the business. (English Mubasher) Return on Assets: Definition: ROA is very simple in terms of understandings because it is effective indicator on how efficient is firm in utilizing its assets. (Accounting explained) Deyaar status on ROA: With ROA of 3.29, it is observed that Deyaar did not throw the funds in one area to earn profits but rather went for resourceful allocation to excel with large profits and less funds. (English Mubasher)Financial Figures: Deyaars financial report net profit for the year 2015 amounted to AED 291 million rising from AED 282 million of the year 2014. Alterations, modifications and predictions: Transfer from properties held for sales to investment properties- This move by Deyaar Development is being utilized in setting up hotel chains and other investment options. This resulted in generating long term revenues either by leasing or from rent. (Deyaar) Partnership with Ascioglu- Deyaar Development entered into strategic partnership in May 2016 with Ascioglu, a leading Turkish real estate brand. Deyaar with this partnership aims to offer optimal value to customers and strong returns on their investment. (Deyaar) Technical move to customer e-service portals- Deyaar development took this initiate to automate the process of feedback, complaints or service requests. E-portal is user friendly and also offers quick response. (Deyaar). Reference: ae,(n,d) CEO Message, viewed on June 13,2016 Myaccounting course,(n.d),Return on equity ratio, Accounting explained,(n.d).

Metaphors by Sylvia Plath free essay sample

The poem, â€Å"Metaphors† by Sylvia Plath, would be an example of this. Some may look at this poem and believe it is random metaphors put into nine lines. I believe this is a poem about Plath’s idea of pregnancy as compared to traditionally unrelated objects. â€Å"Metaphors† has a clue in each line that would lead the reader to believe that it is depicting the process pregnancy. In the poem â€Å"Metaphors†, Plath opens with the line, â€Å"I’m a riddle with nine syllables. In this poem there are nine lines, and each line has nine syllables. This gives the reader a sense of importance revolving around the number nine. Also, people associate the number nine with the time span of pregnancy. There is a designed commonality in these, and the author intended for the reader to put these pieces together. The first part of this line, â€Å"I’m a riddle† describes the unknowns of pregnancy. We will write a custom essay sample on Metaphors by Sylvia Plath or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page â€Å"An elephant, a ponderous house,† (2). If we were to break this line down into two parts, the author would first tell us she is an elephant. Elephants are depicted as very large and heavyweight creatures. This could mean that the author thought of herself as that too. When you carry a baby, you begin to get larger, and so the author may have compared herself to the largest land mammals as a way of exaggerating her weight gain from the pregnancy. The second line states that she is a â€Å"ponderous house† (2). A house is something that people live in; when the author compares herself to a house, she merely states that something is living inside her. Tendrils are slender threadlike appendages of a climbing plant. A melon strolling on two tendrils,† (3), describes the mother’s legs as compared to her pregnant body. This line creates imagery in the readers head. A melon is a larger object, which would not be able to stroll on two tendrils. The melon could resemble the baby, which is strolling on the mother’s legs. Just as the melon looks too big to be strolling on the tendrils, a mother could have a stomach that appears too big to be carried on her two small legs. â€Å"O red fruit, ivory, fine timbers! † (4), as said in the fourth line is a biblical allusion to the fruit of thy womb. A woman’s fruit of thy womb is her baby, the fruit being the child she is bearing in her womb. Ivory and fine timbers refer to a house, or her womb in which her baby is kept. When this line is read, it is the first you read about the actual baby, the previous lines only depict her body shape, while this one depicts what is inside of her. When women are pregnant, their stomach grows and rises every day, just as the baby grows. â€Å"This loaf’s big with its yeasty rising. † (5), is a metaphor describing the growth of a mother’s stomach. Just as bread gets larger as it cooks in an oven, the baby gets larger as it grows inside a mom. This analogy can also depict a relationship between the mother and the child. Just like bread needs the oven to grow, the child needs its mother to grow as well. â€Å"Money’s new- minted in this fat purse. † (6), explains the importance and impact the baby is having on her. The process of minting something is making something better. This line is also referring to the growth of the baby, because she is making the baby better every day. The use of the words money and purse are also clues to depict pregnancy. Money is a material thing, that has value and importance, the purse is just the carrier. She could be showing the reader that the baby has the meaning and the value, but she is just the carrier of the child, not the true value of the process. This is the point in the poem where she becomes scared, she is not going to be the center of attention, because the baby will have more worth and value than her. â€Å"I’m a means, a stage, a cow in a calf† (7), is when Plath becomes saddened. She is starting to feel as though she will have no value after the baby is born. She is just a means, or a way for the baby to come onto earth. She is a stage, a part of a production, musical or play, but she does not get as much praise as the production itself. She is a cow in a calf, the calf being the one who is praised after birth, not the cow itself. She is starting to feel more depressed about the outcomes of being a mom, because the most valuable thing is going to be her baby, not herself. Crazy cravings have always been a part of pregnancy. This line could refer to a crazy craving, as most people would think, but it could also refer to another biblical allusion. I’ve eaten a bag of green apples,† (8), could be a symbol of sin, and coming upon something too early in life. When Eve bites the apple in the Garden of Eden, she is condemned to a fate very painful, which could be referring to the painful process of delivering a child. This apple is also green, which could mean she is not ready for this pain, due the lack of ripeness the apple has. The last line reads â€Å"Boarded the train there’s no getting off. † (9) This means that she is too far along in her pregnancy to give up. She as realized that her life will not be the same, but now she has to accept this new life. She cannot give up on her baby now, and she has to become the best mother she can under whatever circumstances she has. This group of metaphors did tell a story, and I believe it was a story about pregnancy. Her struggles and her observations in a process all mothers have to go through in order to create a child. Although some of these metaphors could be interpreted differently, most of them seem to be drawing the same conclusion and have a common theme of pregnancy.

Best Summary and Analysis The Great Gatsby

Best Summary and Analysis The Great Gatsby SAT / ACT Prep Online Guides and Tips Maybe you’ve just finished The Great Gatsby and need some guidance for unpacking its complex themes and symbols. Or maybe it’s been awhile since you last read this novel, so you need a refresher on its plot and characters. Or maybe you’re in the middle of reading it and want to double check that you’re not missing the important stuff. Whatever you need - we’ve got you covered with this comprehensive summary of one of the great American novels of all time! Not only does this complete The Great Gatsby summary provide a detailed synopsis of the plot, but it’ll also give you: capsule descriptions for the book’s major characters, short explanations of most important themes, as well as links to in-depth articles about these and other topics. (Image: Molasz / Wikimedia Commons) Quick Note on Our Citations Our citation format in this guide is (chapter.paragraph). We're using this system since there are many editions of Gatsby, so using page numbers would only work for students with our copy of the book. To find a quotation we cite via chapter and paragraph in your book, you can either eyeball it (Paragraph 1-50: beginning of chapter; 50-100: middle of chapter; 100-on: end of chapter), or use the search function if you're using an online or eReader version of the text. The Great GatsbySummary: The Full Plot Our narrator, Nick Carraway, moves to the East Coast to work as a bond trader in Manhattan. He rents a small house in West Egg, a nouveau riche town in Long Island. In East Egg, the next town over, where old money people live, Nick reconnects with his cousin Daisy Buchanan, her husband Tom, and meets their friend Jordan Baker. Tom takes Nick to meet his mistress, Myrtle Wilson. Myrtle is married to George Wilson, who runs a gas station in a gross and dirty neighborhood in Queens. Tom, Nick, and Myrtle go to Manhattan, where she hosts a small party that ends with Tom punching her in the face. Nick meets his next-door neighbor, Jay Gatsby, a very rich man who lives in a giant mansion and throws wildly extravagant parties every weekend, and who is a mysterious person no one knows much about. Gatsby takes Nick to lunch and introduces him to his business partner - a gangster named Meyer Wolfshiem. Nick starts a relationship with Jordan. Through her, Nick finds out that Gatsby and Daisy were in love five years ago, and that Gatsby would like to see her again. Nick arranges for Daisy to come over to his house so that Gatsby can â€Å"accidentally† drop by. Daisy and Gatsby start having an affair. Tom and Daisy come to one of Gatsby’s parties. Daisy is disgusted by the ostentatiously vulgar display of wealth, and Tom immediately sees that Gatsby’s money most likely comes from crime. We learn that Gatsby was born intoa poor farming family as James Gatz. He has always been extremely ambitious, creating the Jay Gatsby persona as a way of transforming himself into a successful self-made man - the ideal of the American Dream. Nick, Gatsby, Daisy, Tom, and Jordan get together for lunch. At this lunch, Daisy and Gatsby are planning to tell Tom that she is leaving him. Gatsby suddenly feels uncomfortable doing this in Tom’s house, and Daisy suggests going to Manhattan instead. In Manhattan, the five of them get a suite at the Plaza Hotel where many secrets come out. Gatsby reveals that Daisy is in love with him. Tom in turn reveals that Gatsby is a bootlegger, and is probably engaged in other criminal activities as well. Gatsby demands that Daisy renounce Tom entirely, and say that she has never loved him. Daisy can’t bring herself to say this because it isn’t true, crushing Gatsby’s dream and obsession. It’s clear that their relationship is over and that Daisy has chosen to stay with Tom. That evening, Daisy and Gatsby drive home in his car, with Daisy behind the wheel. When they drive by the Wilson gas station, Myrtle runs out to the car because she thinks it’s Tom driving by. Daisy hits and kills her, driving off without stopping. Nick, Jordan, and Tom investigate the accident. Tom tells George Wilson that the car that struck Myrtle belongs to Gatsby, and George decides that Gatsby must also be Myrtle’s lover. That night, Gatsby decides to take the blame for the accident. He is still waiting for Daisy to change her mind and come back to him, but she and Tom skip town the next day. Nick breaks up with Jordan because she is completely unconcerned about Myrtle’s death. Gatsby tells Nick some more of his story. As an officer in the army, he met and fell in love with Daisy, but after a month had to ship out to fight in WWI. Two years later, before he could get home, she married Tom. Gatsby has been obsessed with getting Daisy back since he shipped out to fight five years earlier. The next day, George Wilson shoots and kills Gatsby, and then himself. The police leave the Buchanans and Myrtle’s affair out of the report on the murder-suicide. Nick tries to find people to come to Gatsby’s funeral, but everyone who pretended to be Gatsby’s friend and came to his parties now refuses to come. Even Gatsby’s partner Wolfshiem doesn’t want to go to the funeral. Wolfshiem explains that he first gave Gatsby a job after WWI and that they have been partners in many illegal activities together. Gatsby’s father comes to the funeral from Minnesota. He shows Nick a self-improvement plan that Gatsby had written for himself as a boy. Disillusioned with his time on the East coast, Nick decides to return to his home in the Midwest. Other Ways to Study the Plot of The Great Gatsby See what happens when in actual chronological order and without flashbacks in our Great Gatsby timeline. Read our individualThe Great Gatsby chapter summariesfor more in-depth details about plot, important quotes and character beats, and how the novel’s major themes get reflected: Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Learn the significance behind the novel’s title, itsbeginning, and its ending. List of the Major Characters in The Great Gatsby Click on each character's name to read an in-depth article analyzing their place in the novel. Nick Carraway - our narrator, but not the book’s main character. Coming East from the Midwest to learn the bond business, Nick is horrified by the materialism and superficiality he finds in Manhattan and Long Island. He ends up admiring Gatsby as a hopeful dreamer and despising the rest of the people he encounters. Jay Gatsby - a self-made man who is driven by his love for, and obsession with, Daisy Buchanan. Born a poor farmer, Gatsby becomes materially successful through crime and spends the novel trying to recreate the perfect love he and Daisy had five years before. When she cannot renounce her marriage, Gatsby’s dream is crushed. Daisy Buchanan - a very rich young woman who is trapped in a dysfunctional marriage and oppressed by her meaningless life. Daisy has an affair with Gatsby, but is ultimately unwilling to say that she has been as obsessed with him as he has with her, and goes back to her unsatisfying, but also less demanding, relationship with her husband, Tom. Tom Buchanan - Daisy’s very rich, adulterous, bullying, racist husband. Tom is having a physically abusiveaffair with Myrtle Wilson. He investigates Gatsby and reveals some measure of his criminal involvement, demonstrating to Daisy that Gatsby isn’t someone she should run off with. After Daisy runs over Myrtle Wilson, Tom makes up with Daisyand they skip town together. Jordan Baker - a professional golfer who has a relationship with Nick. At first, Jordan is attractive because of her jaded, cynical attitude, but then Nick slowly sees that her inveterate lying and her complete lack of concern for other people are deal breakers. Myrtle Wilson - the somewhat vulgar wife of a car mechanic who is unhappy in her marriage. Myrtle is having an affair with Tom, whom she likes for his rugged and brutal masculinity and for his money. Daisy runs Myrtle over, killing herin agruesome and shocking way. George Wilson - Myrtle’s browbeaten, weak, and working class husband. George is enraged when he finds out about Myrtle’s affair, and then that rage is transformed into unhinged madness when Myrtle is killed. George kills Gatsby and himself in the murder-suicide that seems to erase Gatsby and his lasting impact on the world entirely. Other Ways to Study Great Gatsby Characters Need a refresher on all the other people in this book? Check out our overview of the charactersor dive deeper with our detailed character analyses. Get some help for tackling the common assignment of comparing and contrasting the novel’s characters. Start gathering relevant character quotesto beef up your essay assignments with evidence from the text. List of the Major Themes in The Great Gatsby Get a broadoverview of the novel’s themes, or click on each theme to read a detailed individual analysis. Money and Materialism- the novel is fascinated by how people make their money, what they can and can’t buy with it, and how the pursuit of wealth shapes the decisions people make and the paths their lives follow. In the novel, is it possible to be happy without a lot of money? Is it possible to be happy with it? Society and Class- the novel can also be read as a clash between the old money set and the nouveau riche strivers and wannabes that are trying to either become them or replace them. If the novel ends with the strivers and the poor being killed off and the old money literally getting away with murder, who wins this class battle? The American Dream- does the novel endorse or mock the dream of the rags-to-riches success story, the ideal of the self-made man? Is Gatsby a successful example of what’s possible through hard work and dedication, or a sham whose crime and death demonstrate that the American Dream is a work of fiction? Love, Desire, and Relationships- most of the major characters are driven by either love or sexual desire, but none of these connections prove lasting or stable. Is the novel saying that these are destructive forces, or is just that these characters use and feel them in the wrong way? Death and Failure - a tone of sadness and elegy (an elegy is a song of sadness for the dead) suffuses the book, as Nick looks back at a summer that ended with three violent deaths and the defeat of one man’s delusional dream. Areambition and overreach doomed to this level of epic failure, orare theyexamples of the way we sweep the past under the rug when looking to the future? Morality and Ethics - despite the fact that most of the characters in this novel cheat on their significant others, one is an accidental killer, one is an actual criminal, and one a murderer, at the end of the novel no one is punished either by the law or by public censure. Is there a way to fix the lawless, amoral, Wild East that this book describes, or does the replacement of God with a figure from a billboard mean that this is a permanent state of affairs? The Mutability of Identity - the key to answering the title’s implied questions (What makes Gatsby great? Is Gatsby great?) is whether it is possible to change oneself for good, or whether past history and experiences leave their marks forever. Gatsby wants to have it both ways: to change himself from James Gatz into a glamorous figure, but also to recapitulate and preserve in amber a moment from his past with Daisy. Does he fail because it’s impossible to change? Because it’s impossible to repeat the past? Or both? Other Ways to Study Great Gatsby Themes Often, themes are represented by the a novel's symbols. Check out our overview of the main symbols in The Great Gatsby, or click on an individual symbol for a deeper exploration of its meaning and relevance: The green light at the end of Daisy's dock The eyes of Doctor T. J. Eckleburg The valley of ashes Themes are also often reinforced by recurring motifs. Delve into a guide to the way motifs color and enrich this work. The Bottom Line Our guide toThe Great Gatsbyoffers a variety of ways to study the novel's: plot characters themes symbols motifs Use our analysis, gathered quotations, and description for help with homework assignments, tests, and essays on this novel. What’s Next? More Great Gatsby Analysis and Study Guides! Understand how the book is put together by looking at its genre, narrator, andsetting. Learn the background of and context for the novel in our explanations of the history of the composition of the bookand the biography of F. Scott Fitzgerald. Get a sense of how the novel has been adapted by reading about its many film versions. Hammer out the nitty gritty basics of the novel’s hardest vocab words. Want to improve your SAT score by 160 points or your ACT score by 4 points?We've written a guide for each test about the top 5 strategies you must be using to have a shot at improving your score. Download it for free now: