Anamind helps organizations build business planning and forecasting capability. With simplicity at the core of our approach we offer a world class planning system - PLANAMIND, process consulting services, and training for business planning. This course has been specifically designed by us to help planning professionals as well as aspirants of this function worldwide, to understand and build their skills in business planning. The course contains both quantitative and qualitative aspects of planning. This course is business oriented and not purely academic in nature.

Almost all companies these days are investing thousands of dollars in data analysis to get their data analyzed. Well, in fact studies say that there are around 73% of organizations have invested in Big Data. Why do you think that is the case? What can you reap of the data, ideally just 1s and 0s? Moreover, how does this data help an organization's future?

Machine learning and predictive analytics are transforming the way that businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, and is becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data. Its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. This video gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, making advanced data exploration and insight accessible to anyone interested in learning it. This video empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. You will learn to carry out different tasks on the data to bring it into action.By the end of this course, you will be able to carry out different analyzing techniques, apply classification and regression, and also reduce data.

Abstract argumentation offers an appealing way of representing and evaluating arguments and counterarguments. This approach can be enhanced by considering probability assignments on arguments, allowing for a quantitative treatment of formal argumentation. In this paper, we regard the assignment as denoting the degree of belief that an agent has in an argument being acceptable. While there are various interpretations of this, an example is how it could be applied to a deductive argument. Here, the degree of belief that an agent has in an argument being acceptable is a combination of the degree to which it believes the premises, the claim, and the derivation of the claim from the premises. We consider constraints on these probability assignments, inspired by crisp notions from classical abstract argumentation frameworks and discuss the issue of probabilistic reasoning with abstract argumentation frameworks. Moreover, we consider the scenario when assessments on the probabilities of a subset of the arguments are given and the probabilities of the remaining arguments have to be derived, taking both the topology of the argumentation framework and principles of probabilistic reasoning into account. We generalise this scenario by also considering inconsistent assessments, i.e., assessments that contradict the topology of the argumentation framework. Building on approaches to inconsistency measurement, we present a general framework to measure the amount of conflict of these assessments and provide a method for inconsistency-tolerant reasoning.

As bonus, you also learn how to share your analysis results with your collegues friends and others and create visual analysis of your results. You will also have access to some practice questions, which will give you hand on experience.

R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This video course will take you from very basics of R to creating insightful machine learning models with R. You will start with setting up the environment and then perform data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationship. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Yu-Wei, Chiu (David Chiu) is the founder of LargitData, a startup company that mainly focuses on providing big data and machine learning products.

Do you find GIS & Spatial Data books & manuals too vague, expensive & not practical and looking for a course that takes you by hand, teaches you all the concepts, and get you started on a real life project? Or perhaps you want to save time and learn how to automate some of the most common GIS tasks? I'm very excited you found my spatial data analysis course. My course provides a foundation to carry out PRACTICAL, real-life spatial data analysis tasks in popular and FREE software frameworks. My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate.

This course is designed to take users who use R and QGIS for basic spatial data/GIS analysis to perform more advanced GIS tasks (including automated workflows and geo-referencing) using a variety of different data. In addition to making you proficient in R and QGIS for spatial data analysis, you will be introduced to another powerful free GIS software.. GRASS. This course takes a completely practical approach to spatial data analysis and mapping- Each lecture will teach you a practical application/processing technique which you can apply easily. The course is taught by Minerva Singh, A PhD graduate from Cambridge University, UK, who has several years of research experience in Quantitative Ecology and an MPhil in Geography and Environment from Oxford University. Minerva has published papers in international peer reviewed journals and given talks at international conferences.