7 Educators providing Data Mining courses

Business Intelligence Business Intelligence (BI) refers to a set of technology-based techniques, applications, and practices used to aggregate, analyze, and present business data. BI practices provide historical and current views of vast amounts of data and generate predictions for business operations. The purpose of Business Intelligence is the support of better business decision making. This course provides an overview of the technology and application of BI and how it can be used to improve corporate performance. What you will Learn You will learn how to: Specify a data warehouse schema Identify the data and visualization to be used for data mining and Business Intelligence Design a Business Intelligence user interface Getting Started Introductions Agenda Expectations Foundation Concepts The challenge of decision making What is Business Intelligence? The Business Intelligence value proposition Business Intelligence taxonomy Business Intelligence management issues Sources of Business Intelligence Data warehousing Data and information Information architecture Defining the data warehouse and its relationships Facts and dimensions Modeling, meta-modeling, and schemas Alternate architectures Building the data warehouse Extracting Transforming Loading Setting up the data and relationships Dimensions and the Fact Table Implementing many-to-many relationships in data warehouse Data marts Online Analytical Processing (OLAP) What is OLAP? OLAP and OLTP OLAP functionality Multi-dimensions Thinking in more than two dimensions What are the possibilities? OLAP architecture Cubism Tools OLAP variations - MOLAP, ROLAP, HOLAP BI using SOA Applications of Business Intelligence Applying BI through OLAP Enterprise Resource Planning and CRM Business Intelligence and financial information Business Intelligence User Interfaces and Presentations Data access Push-pull data access Types of decision support systems Designing the front end Presentation formats Dashboards Types of dashboards Common dashboard features Briefing books and scorecards Querying and Reporting Reporting emphasis Retrofitting Talking back Key Performance Indicators Report Definition and Visualization Typical reporting environment Forms of visualization Unconstrained views Data mining What is in the mine? Applications for data mining Data mining architecture Cross Industry Standard Process for Data Mining (CISP-DM) Data mining techniques Validation The Business Intelligence User Experience The business analyst role Business analysis and data analysis Five-step approach Cultural impact Identifying questions Gathering information Understand the goals The strategic Business Intelligence cycle Focus of Business Intelligence Design for the user Iterate the access Iterative solution development process Review and validation questions Basic approaches Building ad-hoc queries Building on-demand self-service reports Closed loop Business Intelligence Coming attractions - future of Business Intelligence Best practices in Business Intelligence

Business Intelligence: In-House Training Business Intelligence (BI) refers to a set of technology-based techniques, applications, and practices used to aggregate, analyze, and present business data. BI practices provide historical and current views of vast amounts of data and generate predictions for business operations. The purpose of Business Intelligence is the support of better business decision making. This course provides an overview of the technology and application of BI and how it can be used to improve corporate performance. What you will Learn You will learn how to: Specify a data warehouse schema Identify the data and visualization to be used for data mining and Business Intelligence Design a Business Intelligence user interface Getting Started Introductions Agenda Expectations Foundation Concepts The challenge of decision making What is Business Intelligence? The Business Intelligence value proposition Business Intelligence taxonomy Business Intelligence management issues Sources of Business Intelligence Data warehousing Data and information Information architecture Defining the data warehouse and its relationships Facts and dimensions Modeling, meta-modeling, and schemas Alternate architectures Building the data warehouse Extracting Transforming Loading Setting up the data and relationships Dimensions and the Fact Table Implementing many-to-many relationships in data warehouse Data marts Online Analytical Processing (OLAP) What is OLAP? OLAP and OLTP OLAP functionality Multi-dimensions Thinking in more than two dimensions What are the possibilities? OLAP architecture Cubism Tools OLAP variations - MOLAP, ROLAP, HOLAP BI using SOA Applications of Business Intelligence Applying BI through OLAP Enterprise Resource Planning and CRM Business Intelligence and financial information Business Intelligence User Interfaces and Presentations Data access Push-pull data access Types of decision support systems Designing the front end Presentation formats Dashboards Types of dashboards Common dashboard features Briefing books and scorecards Querying and Reporting Reporting emphasis Retrofitting Talking back Key Performance Indicators Report Definition and Visualization Typical reporting environment Forms of visualization Unconstrained views Data mining What is in the mine? Applications for data mining Data mining architecture Cross Industry Standard Process for Data Mining (CISP-DM) Data mining techniques Validation The Business Intelligence User Experience The business analyst role Business analysis and data analysis Five-step approach Cultural impact Identifying questions Gathering information Understand the goals The strategic Business Intelligence cycle Focus of Business Intelligence Design for the user Iterate the access Iterative solution development process Review and validation questions Basic approaches Building ad-hoc queries Building on-demand self-service reports Closed loop Business Intelligence Coming attractions - future of Business Intelligence Best practices in Business Intelligence

Machine literacy in data wisdom is a fleetly expanding discipline and now is the crucial element. This groundbreaking field equips computers and systems with the capacity to learn from data and ameliorate their performance over time without unequivocal programming. Statistical ways are employed to train algorithms to produce groups or prognostications and to find significant findings in data mining systems. immaculately, the conclusions made from these perceptivity impact crucial growth pointers in operations and companies. What's Machine Learning? . Machine learning classes in pune The machine literacy term was chased by Arthur Samuel in 1959. It's the discipline solely concentrated on studying and erecting tools and ways that can let machines learn. These styles use data to enhance the computer performance of a particular set of tasks. Machine literacy algorithms induce prognostications or possibilities and produce a model grounded on data samples, also called training data. There's a need for machine literacy as these algorithms are applied in a broad range of operations, for illustration, computer vision, dispatch filtering, speech recognition, husbandry, and drugs, where it's a challenge to produce traditional algorithms that can negotiate the needed tasks. orders in Machine Learning Being such a vast and complicated field, machine literacy is divided into three different orders machine literacy orders Supervised literacy – In this system, the algorithm is trained using data that has been labeled and in which the target variable or asked result is known. Once trained, the algorithm may make prognostications grounded on unidentified information by learning how to associate input variables with the willed affair. Unsupervised literacy – In this case, the algorithm is trained on unlabeled data, and its thing is to discover structures or patterns within the data without having a specific target variable in mind. Common unsupervised literacy tasks include dimensionality reduction and clustering. underpinning literacy – An algorithm is trained via relations with the terrain in this type of literacy. The algorithm learns how to operate in order to maximize a price signal or negotiate a particular ideal. Through prices or penalties, it receives feedback that helps it upgrade its decision-making process. Artificial Intelligence and Machine Learning Artificial intelligence( AI) is divided into several subfields, and machine literacy( ML) is one of them. In order to produce intelligent machines that can pretend mortal intelligence, a variety of methodologies, approaches, and technologies are used. This notion is known as artificial intelligence( AI). The development of ways and models that allow computers to acquire knowledge from data and make recommendations or judgments without unequivocal programming is the focus of machine literacy( ML). Some academics were interested in the idea of having machines learn from data in the early stages of AI as an academic field. They tried to approach the issue using colorful emblematic ways and neural networks. They were primarily perceptrons, along with other models that were ultimately discovered to be reimaginings of the generalized direct models of statistics. For this case, you aim to make a system secerning cows and tykes. With the AI approach, you'll use ways to make a system that can understand the images with the help of specific features and rules you define. Machine literacy models will bear training using a particular dataset of pre-defined images. You need to give numerous farmlands of cows and tykes with corresponding markers. Why is Machine Learning Important? Machine literacy is an abecedarian subfield of artificial intelligence that focuses on assaying and interpreting patterns and structures in data. It enables logic, literacy, and decision-making outside of mortal commerce. The significance of machine literacy is expanding due to the extensively more expansive and more varied data sets, the availability and affordability of computational power, and the availability of high-speed internet. It facilitates the creation of new products and provides companies with a picture of trends in consumer geste and commercial functional patterns. Machine literacy is a high element of the business operations of numerous top enterprises, like Facebook, Google, and Uber. Prophetic Analytics Machine learning course in pune Machine literacy makes prophetic analytics possible by using data to read unborn results. It's salutary in the fields of finance, healthcare, marketing, and logistics. Associations may prognosticate customer growth, spot possible troubles, streamline operations, and take visionary action to ameliorate results using prophetic models. Personalization and recommendation systems Machine literacy makes recommendation systems and substantiated gests possible, impacting every aspect of our diurnal lives. Platforms like Netflix, Amazon, and Spotify use machine literacy algorithms to comprehend stoner preferences and offer substantiated recommendations. Personalization boosts stoner pleasure and engagement while promoting business expansion. Image and speech recognition Algorithms for machine literacy are particularly good at jobs like speech and picture recognition. Deep literacy, a branch of ML, has converted computer vision and natural language processing. It makes it possible for machines to comprehend, dissect, and produce visual and audio input. This technology is helpful for driverless vehicles, surveillance, medical imaging, and availability tools, among other effects. Machine learning training in pune