- Descriptive, predictive and prescriptive analytics ppt
- Descriptive analytics definition
- Prescriptive analytics examples
- Prescriptive analytics techniques
- Descriptive analytics techniques
- What is predictive analytics
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- Descriptive, diagnostic, predictive, prescriptive analytics
Descriptive, predictive and prescriptive analytics pptToggle navigation. Help Preferences Sign up Log in. View by Category Toggle navigation. Products Sold on our sister site CrystalGraphics. Description: The business intelligence is very necessary for solving problems, creating new methods and for business decision making by using various analytics data. There are various tools in type of data analysis such as descriptive analytics, predictive and prescriptive analytics, which are the future of business intelligence. Provided by: rachanapriya. Latest Highest Rated. There is high scope for the growth of the Prescriptive and Predictive Analytics Market in various regions across the globe through Prediction analytics and prescriptive analytics are collectively used for data intelligence and data mining applications such as Big Data through North America is one of the major investor of the Prescriptive and Predictive Analytics Market with many companies in this region providing analytical services and systems suggests IndustryARC analysis. China and India are the major markets for applications of this market through This market is poised to have rapid growth owing to the increasing investment by corporations for various types of business intelligence tools for analytics and growing utilization of prescriptive and descriptive data mining for intelligent solutions through Whether your application is business, how-to, education, medicine, school, church, sales, marketing, online training or just for fun, PowerShow. And, best of all, most of its cool features are free and easy to use. You can use PowerShow. Or use it to find and download high-quality how-to PowerPoint ppt presentations with illustrated or animated slides that will teach you how to do something new, also for free. Or use it to upload your own PowerPoint slides so you can share them with your teachers, class, students, bosses, employees, customers, potential investors or the world. That's all free as well! For a small fee you can get the industry's best online privacy or publicly promote your presentations and slide shows with top rankings. But aside from that it's free. We'll even convert your presentations and slide shows into the universal Flash format with all their original multimedia glory, including animation, 2D and 3D transition effects, embedded music or other audio, or even video embedded in slides. All for free. Most of the presentations and slideshows on PowerShow. You can choose whether to allow people to download your original PowerPoint presentations and photo slideshows for a fee or free or not at all. Check out PowerShow. There is truly something for everyone! Related More from user. Promoted Presentations. World's Best PowerPoint Templates - CrystalGraphics offers more PowerPoint templates than anyone else in the world, with over 4 million to choose from. They'll give your presentations a professional, memorable appearance - the kind of sophisticated look that today's audiences expect. Boasting an impressive range of designs, they will support your presentations with inspiring background photos or videos that support your themes, set the right mood, enhance your credibility and inspire your audiences. Chart and Diagram Slides for PowerPoint - Beautifully designed chart and diagram s for PowerPoint with visually stunning graphics and animation effects.
Descriptive analytics definition
The big data revolution has given birth to different kinds, types and stages of data analysis. 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 Datais by gaining the right information which delivers knowledge, that 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. In fact, what distinguishes a best data scientist or data analyst from others, is their ability to identify the kind of analytics that can be leveraged 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 that they have. Each of these analytic types offers a different insight. A lioness hired a data scientist fox to help find her prey. Next, the fox estimated the probability of finding a given prey at a certain place and time, using advanced ML techniques. Also, it identified routes in the jungle for the lioness to take to minimize her efforts in finding her prey. Finally, based on above models, the fox got trenches dug at various points in the jungle so that the prey got caught automatically. 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. 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, traffic management, etc. After the organizations collect big data, the next important step is to get started with analytics. Many organizations do not know where to begin, what kind of analytics can nurture business growth and what these different types of analytics mean. To help release your Data Science projects faster we have put together a library of solved code example. Click here to get free access. 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 behaviours 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. The best example to explain descriptive analytics are 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.
Prescriptive analytics examplesWe've covered a few fundamentals and pitfalls of data analytics in our past blog posts. In this blog post, we focus on the four types of data analytics we encounter in data science: Descriptive, Diagnostic, Predictive and Prescriptive. Note: This blog post was published on the KDNuggets blog - Data Analytics and Machine Learning blog - in July and received the most reads and shares by their readers that month. When I talk to young analysts entering our world of data science, I often ask them what they think is data scientist's most important skill. Their answers have been quite varied. My message to them is that their most important skill will be their ability to translate data into insights that are clear and meaningful to a non-quant. The Swedish statistician Hans Rosling is famous for this. The following TedTalk by Hans Rosling sheds some light:. On this theme, it would be worth unpacking some of the tools used to help individuals understand the role of analytics in helping develop valuable insights. One such tool is the 4-dimensional paradigm of analytics. Simplistically, analytics can be divided into four key categories. I'll explain these four in more detail below. Also see our guide to using machine learning in businesswhere we explore how to use machine learning to better tap into your business data and gain valuable, informing insights to improve business revenue. Designed by Freepik. This is the most common of all forms. In business, it provides the analyst with a view of key metrics and measures within the company. Similarly, an analyst could have data on a large population of customers. Understanding demographic information on their customers e. Utilising useful visualisation tools enhances the message of descriptive analytics. On the assessment of the descriptive data, diagnostic analytical tools will empower an analyst to drill down and in so doing isolate the root-cause of a problem. Well-designed business information BI dashboards incorporating reading of time-series data i. Predictive analytics is all about forecasting. Predictive models typically utilise a variety of variable data to make the prediction. The variability of the component data will have a relationship with what it is likely to predict e. These data are then compiled together into a score or prediction. In a world of significant uncertainty, being able to predict allows one to make better decisions. Predictive models are some of the most important utilised across many fields. Here are the Top Pitfalls to avoid in Predictive Analytics. The next step up regarding value and complexity is the prescriptive model. A prescriptive analysis is typically not just with one individual response but is, in fact, a host of other actions. An excellent example of this is a traffic application helping you choose the best route home and taking into account the distance of each route, the speed at which one can travel on each road and, crucially, the current traffic constraints. Another example might be producing an exam time-table such that no students have clashing schedules.
Descriptive analytics techniques