New Excel Functions Course Description Ross Maynard Description In the second half of 2020 Microsoft released a significant upgrade to the most used spreadsheet programme in the world. Microsoft Excel now offers the ability to handle dynamic arrays – functions that return a range of results that update as the source data changes. In this course, we discuss the power of dynamic arrays and introduce the new functions. The functions I am going to cover are: RANDARRAY- creating a table of random numbers or random dates UNIQUE – identifying the distinct items in a list SEQUENCE – listing numbers with a set interval SORT and SORTBY – new ways of dynamically sorting data FILTER – building the ability to filter data into formulae XLOOKUP – replacing VLOOKUP with greater flexibility IFS and SWITCH making it easier to construct IF statements The new CONCAT, and TEXTJOIN text functions If you have an earlier version of Microsoft Excel then these functions will not be available to you and this course might not be for you. But if you do have a subscription to Office365 – either personally or through your work – I think you will find this course extremely useful. Learning Outcomes Participants in this course will learn: What the new dynamic arrays feature in Microsoft Excel means How the new RANDARRAY function works How the new UNIQUE function works How the new SEQUENCE function works How the new SORT and SORTBY functions work How the new FILTER function works How the new XLOOKUP function can replace VLOOKUP How to build IF statements with the new IFS function How the new SWITCH function works How the new TEXTJOIN function can replace CONCATENATE and CONCAT How the new functions can be used in management reporting Course Requirements There are no pre-course requirements. Additional Resources Course Spreadsheet with the examples covered. About Ross Ross Maynard is a Fellow of the Chartered Institute of Management Accountants in the UK. He is director of Ideas2Action Process Excellence Ltd and has 30 years’ experience as a process improvement consultant and facilitator. Ross is also a professional author of online training courses for accountants. Ross lives in Scotland with his wife, daughter and Cocker Spaniel
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Description Microsoft Excel has hundreds, if not thousands, of functions and features. This course aims to cover some of the best – that is the ones I find most useful. In “Adventures in Excel”, I cover the simple-to-use but powerful functions that I use most often: Basic features including products and powers Key date functions including the calculation of due dates and days past due Generating random numbers and random dates The new IFS functions (new to Office365) Text functions The most useful logical functions and IS functions How to create a drop-down list The new XLOOKUP function Pivot tables These functions are easy to use, and, unless your role is extremely specialised, they are probably the ones you’ll use 90% of the time. I hope you find the course helpful. Learning Outcomes Participants in this course will learn: Basic Excel functions including SUM, AVERAGE, MIN, MAX, PRODUCT, POWER and SQRT Useful date functions including TODAY, EOMONTH, EDATE, and DAYS Generating random numbers using RAND and RANDBETWEEN; generating random dates; and randomly picking an item from a list or table The new RANDARRAY function in Office365 The new IFS functions in Office365 including AVERAGEIFS, MAXIFS, MINIFS, COUNTIFS, and SUMIFS Text functions including TRIM, LOWER, UPPER, PROPER, LEFT, MID, RIGHT, FIND, TEXTJOIN and CONCATENATE Logical functions AND, OR, and NOT and IS functions ISTEXT, ISNUMBER, ISBLANK and ISERROR How to create a drop-down list The new SORT and FILTER functions in Office365 VLOOKUP and the new XLOOKUP function How to create a Pivot table and analyse data with one Course Requirements There are no pre-course requirements. Additional Resources Course Spreadsheet with the examples covered. About Ross Ross Maynard is a Fellow of the Chartered Institute of Management Accountants in the UK. He is director of Ideas2Action Process Excellence Ltd and has 30 years’ experience as a process improvement consultant and facilitator. Ross is also a professional author of online training courses for accountants. Ross lives in Scotland with his wife, daughter and Cocker Spaniel
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