2. Literary Analysis Essay. This the most common and most used type of analysis in college or high school. After you are done with the piece that your professor has assigned to you, you are required to write an informative analysis of a situation or critically analyze a metaphor and its impact on the plot Analytical Paragraph writing - An analytical paragraph is a form of descriptive writing which is written on the basis of a given chart, graph, data, outline, clues, table, etc. When writing an analytical paragraph, one should remember to describe the facts in the best possible manner and to cover the information provided. The analytical paragraph has to be written in around words. So the Common criteria of undergraduate essay writing focus on the following requirements: students need to be analytical and critical in their response students need to structure their writing logically students need to be persuasive writers students need to answer the question This booklet looks at the requirement to be analytical in your response
Types of Analytics: Descriptive, Predictive, Prescriptive Analytics
The big data revolution has given birth to different types of analytical writing, types, and stages of data analysis, types of analytical writing. Boardrooms across companies are buzzing around with data analytics - offering enterprise-wide solutions for business success.
However, what do these really mean to businesses? The key to companies successfully using Big Data is by gaining the right information which delivers knowledge, which gives businesses the power to gain a competitive edge. The main goal of big data analytics is to help organizations make smarter decisions for better business outcomes. Big data analytics cannot be considered as a one-size-fits-all blanket strategy, types of analytical writing.
In fact, what distinguishes the best data scientist or data analyst from others, is their ability to identify the different types of analytics that can be types of analytical writing to benefit the business - at an optimum.
The three dominant types of analytics —Descriptive, Predictive and Prescriptive analytics, are interrelated solutions helping companies make the most out of the big data types of analytical writing they have. Each of these analytic types offers a different insight. Big data analytics helps a business understand the requirements and preferences of a customer so that businesses can increase their customer base and retain the existing ones with personalized and relevant offerings of their products or services.
According to IDC, the big data and analytics industry is anticipated to grow at a CAGR of The big data industry is growing at a rapid pace due to various applications like smart power grid management, sentiment analysis, fraud detection, personalized offerings, types of analytical writing, traffic management, etc. across myriad industries.
After the organizations collect big data, the next important step is to get started with analytics. Many organizations do not know where to begin, types of analytical writing, what kind of analytics can nurture business growth, and what these different types of the analytics mean.
Let's explore the different types of analytics and the value they bring in to any business. Free access to solved Python and R codes for analytics can be found here these are ready-to-use for your projects. This type of analytics, analyses the data coming in real-time and historical data for insights on how to approach the future. The main objective of descriptive analytics is to find out the reasons behind precious success or failure in the past.
The vast majority of big data analytics used by organizations falls into the category of descriptive analytics. A business learns from past behaviors to understand how they will impact future outcomes. Descriptive analytics is leveraged when a business needs to understand the overall performance of the company at an aggregate level and describe the various aspects.
Descriptive analytics are based on standard aggregate functions in databaseswhich just require knowledge of basic school math. Most of the social analytics are descriptive analytics. They summarize certain groupings based on simple counts of some events. The number of followers, likes, posts, fans are mere event counters. that are the outcome of basic arithmetic operations. The best example to explain descriptive analytics is the results, that a business gets from the web server through Google Analytics tools.
The outcomes help understand what actually happened in the past and validate if a promotional campaign was successful or not based on basic parameters like page views. The subsequent step in data reduction is predictive analytics, types of analytical writing.
Analyzing past data patterns and trends can accurately inform a business about what could happen in the future. This helps in setting realistic goals for the business, effective planning, types of analytical writing, and restraining expectations. Michael Wu, chief scientist of San Francisco-based Lithium Technologies said -"The purpose of predictive analytics is NOT to tell you what will happen in the future.
It cannot do that. In fact, no analytics can do that, types of analytical writing. Predictive analytics can only forecast what might happen in the future because all predictive analytics are probabilistic in nature. Organizations collect contextual data and relate it with other customer user behavior datasets and web server data to get real insights through predictive analytics.
Companies can predict business growth in the future if they keep things as they are. Predictive analytics provides better recommendations and more future-looking answers to questions that cannot be answered by BI. To make predictions, algorithms take data and fill in the missing data with the best possible guesses.
This types of analytical writing is pooled with historical data present in types of analytical writing CRM systems, POS Systems, ERP, and HR systems to look for data patterns and identify relationships among various variables in the dataset.
Organizations should capitalize on hiring a group of data scientists in who can develop statistical and machine learning algorithms to leverage predictive analytics and design an effective business strategy. Sentiment analysis is the most common kind of predictive analytics. The learning model takes input in the form of plain text and types of analytical writing output of the model is a sentiment score that helps determine whether the sentiment is positive, negative or neutral.
Organizations like WalmartAmazon, and other retailers leverage predictive analytics to identify trends in sales based on purchase patterns of customers, forecasting customer behavior, forecasting inventory levels, predicting what products customers are likely to purchase together so that they can offer personalized recommendations, predicting the number of sales at the end of the quarter or year.
The best example where predictive analytics finds great application is in producing the credit score. A credit score helps financial institutions decide the probability of a customer paying credit bills on time. Access Data Science and Machine Learning Project Code Examples.
Big data might not be a reliable crystal ball for predicting the exact winning lottery numbers but it definitely can highlight the problems and help a business understand why those problems occurred. Businesses can use the data-backed and data-found factors to create prescriptions for the business problems, that lead to realizations and observations. Prescriptive analytics is the next step of predictive analytics that adds the spice of manipulating the future.
Prescriptive analytics advises on possible outcomes and results in actions that are types of analytical writing to maximize key business metrics. Simulating the future, under various sets of assumptions, allows scenario analysis - which when combined with different optimization techniques, allows prescriptive analysis to be performed. The prescriptive analysis explores several possible actions and suggests actions depending on the results of descriptive and predictive analytics of a given dataset.
Prescriptive analytics is a combination of data and various business rules, types of analytical writing. The data for prescriptive analytics types of analytical writing be both internal within the organization and external like social media data. Business rules are preferences, best practices, boundaries, and other constraints. Mathematical models include natural language processing, machine learning, statistics, operations research, etc.
Prescriptive analytics is comparatively complex in nature and many companies are not yet using them in day-to-day business activities, as it becomes difficult to manage.
Prescriptive analytics if types of analytical writing properly can have a major impact on business growth. Large scale organizations use prescriptive analytics for scheduling the inventory in the supply chain, optimizing production, etc. to optimize the customer experience. Prescriptive analytics can be used in healthcare to enhance drug development, finding the right patients for clinical trials, etc.
This kind of analytics is used by businesses to types of analytical writing an in-depth insight into a given problem provided they have enough data at their disposal. Diagnostic analytics helps identify anomalies and determine casual relationships in data. For example, eCommerce giants like Amazon can drill the sales and gross profit down to various product categories like Amazon Echo to find out why they missed on their overall profit margins.
Diagnostic analytics also find applications in healthcare for identifying the influence of medications on a specific patient segment with other filters like diagnoses and prescribed medication. A lioness hired a data scientist fox to help find her prey.
The fox had access to a rich DataWarehouse, which consisted of data about the jungle, its creatures, and events happening in the jungle. On its first day, the fox presented the lioness with a report summarizing where she found her prey in the last six months, which helped the lioness decide where to go hunting next. This is an example of descriptive analytics.
Next, the fox estimated the probability of finding a given prey at a certain place and time, using advanced ML techniques. This is predictive analytics. Also, it identified routes in the jungle for the lioness to take to minimize her efforts in finding her prey. This is an example of Optimization. Finally, types of analytical writing, based on the above models, the fox got trenches dug at various points in the jungle so that the prey got caught automatically.
This is Automation. This is the AnalyticsLifeCycle. As an increasing number of organizations realize that big data is a competitive advantage and they should ensure that they choose the right kind of data analytics solutions to increase ROI, types of analytical writing, reduce operational costs and enhance service quality.
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From Descriptive to Analytical writing
, time: 15:455 Steps to Write a Great Analytical Essay
Common criteria of undergraduate essay writing focus on the following requirements: students need to be analytical and critical in their response students need to structure their writing logically students need to be persuasive writers students need to answer the question This booklet looks at the requirement to be analytical in your response Analytical Paragraph writing - An analytical paragraph is a form of descriptive writing which is written on the basis of a given chart, graph, data, outline, clues, table, etc. When writing an analytical paragraph, one should remember to describe the facts in the best possible manner and to cover the information provided. The analytical paragraph has to be written in around words. So the All analytical papers include a thesis, analysis of the topic, and evidence to support that analysis. When developing an analytical essay outline and writing your essay, follow these five steps: #1: Choose a topic. #2: Write your thesis. #3: Decide on your main points.
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