English for Business Analysts Part 3. Business

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English for Business Analysts Part 3. Business MINISTRY OF EDUCATION AND SCIENCE OF UKRAINE SIMON KUZNETS KHARKIV NATIONAL UNIVERSITY OF ECONOMICS T. Borova O. Milov ENGLISH FOR BUSINESS ANALYSTS Textbook In 3 parts PART 3. BUSINESS INTELLIGENT TOOLS Kharkiv S. Kuznets KhNUE 2018 UDC 811.111(075.034) B78 Authors: Doctor of Sciences in Pedagogy, Professor T. Borova – units 1, 3, 5, glossary; PhD in Engineering, Associate Professor O. Milov – units 2, 4, 6, introduction, index. Рецензенти: проректор з науково-дослідної роботи та міжнародної діяльності Харківської державної академії дизайну і мистецтв, д-р пед. наук, професор О. В. Гончар; завідувач кафедри загального та прикладного мовознавства Харківського національ- ного університету імені В. Н. Каразіна, канд. філол. наук, доцент В. О. Гуторов. Рекомендовано до видання рішенням ученої ради Харківського національного економічного університету імені Семена Кузнеця. Протокол № 9 від 25.06.2018 р. Самостійне електронне текстове мережеве видання Borova T. B78 English for Business Analysts : textbook : in 3 parts. Part 3. Business Intelligent Tools [Electronic resource] / T. Borova, O. Milov. – Kharkiv : S. Kuznets KhNUE, 2018. – 179 p. (English) ISBN 978-966-676-743-4 The material for mastering the English language for business analysts is offered. The third part of the textbook deals with intelligent tools of business analytics which are the basis of business analysis, namely: data and tools for data mapping, decision making, knowledge management, computational economics, financial mathematics. These disciplines provide insight into various economic and mathematical aspects of business analysis. The textbook can be used both for training in groups and independent learning. For students of speciality 051 "Economics", lecturers, as well as those who learn and use English in the professional activity connected with the application of mathematical methods in economics. UDC 811.111(075.034) © Borova T., Milov O., 2018 © Simon Kuznets Kharkiv National ISBN 978-966-676-743-4 University of Economics, 2018 Introduction Fluency in English is an indisputable condition for success in all areas of life, including business. This textbook aims to provide an opportunity for future specialists in business analytics to master English of the economic and mathematical area of focus and develop communicative competences (linguistic and pragmatic) for general and professional purposes to ensure effective communication in the academic and professional environment. Step by step, students learn the basics of analytical activities in the area of business in English while simultaneously improving communicative and language skills. The third part is focused on the disciplines of business analysis tools and the application of these tools that are the basis of business analytics and business intelligence, in particular, the topics related to data and information, decision making, knowledge management, computational economics, and finance mathematics. These topics provide insight into different economic and mathe- matical aspects of business analysis. This part is addressed to student studying business analysis and business intelligence, decision making and knowledge management, computational economics, who are thinking about making a career in business analytics, or who are already working in system teams. The textbook gives an introductory overview of words and phrases used in business analytics, decision making, computational economics, and finance mathematics. Students specializing in various social and natural sciences, where business analysis is part of the curriculum, should therefore find this textbook useful. It will be particularly helpful to students who sometimes feel daunted by mathematical language and vocabulary. All units are identical in structure and consist of the basic text with comprehension exercises, including semantization of new lexical items and improving the grammatical competence of students, and speaking tasks promoting more efficient assimilation of new material. The textbook is based on the gradual complication of professional material. The content of the authentic texts selected for the textbook meets the academic and professional purposes. Language skills that are necessary for performing the communicative tasks are connected with learning economic and mathematical methods used in business analytics. The vocabulary selected according to the requirements to the educational level of graduates is topically 3 introduced and drilled in various tasks. Communicative integrated skills promote the efficiency of study of English. The textbook materials aim to develop students' professional communicative competence, particularly involving videos and cases. The structure of the textbook meets the modern requirements of learning English, the syllabus of English for professional purposes and the Common European Framework of Reference for Languages. The publication contains different materials for self-study and development of language and communicative skills. The textbook can be recommended to students studying economics with the focus on economic and mathematical methods, lecturers, postgraduate students, and all English language learners who use it for business and analytics purposes. 4 Unit 1. Business Analytics and Business Intelligence Task 1. Answer the questions. 1. What is business analytics? Give five associations with this notion. 2. What do you know about business intelligence? 3. What is a business analytics model? Task 2. Read the text and compare your answers with the information in the text "Why the term business analytics". Why the Term Business Analytics Today most business processes are linked together via electronic systems that allow them to run smoothly and in a coordinated way. The very same information systems generate electronic traces that we systematically collect and store all primarily for simple reporting purposes. Business analytics allows business to go beyond traditional BA report- ing. We are entering the analytical age, a window in time where competitive advantages will be gained from companies making increasingly more advanced use of information. It will also be a period when other companies will fail and falter as infosaurs, with only muscles and armor and not the brainpower needed to survive in changing market conditions. So to make it clear: Analytics is an advanced discipline within business intelligence. However, today business intelligence as a term is heavily associated with large software vendors that offer only simple technical reporting solutions for the end users. We will use the term business analytics in order to put extra focus on this missing element of the business intelligence equation, and which is by now the most exciting one. If mastered, this element will be what drives your company into a prosperous future. (Adapted from [5]) Task 3. Match the words with the definitions and then find and underline them in the text (Task 4). 1) deduce a) lose vigour 2) immense b) a dishonest artifice or trick 3) desperate c) to reach (a conclusion about something) by reasoning; conclude (that) 5 4) stellar d) risky 5) conduct e) unusually large; huge; vast 6) flag f) outstanding or immense 7) fraud g) the way of managing a business Task 4. Read the text and answer the questions. 1. What is data mining for a company? 2. Why is the collection and analysis of data important not only on the Internet? 3. What do credit card issuers often experience? (Give examples.) 4. What did Capital One create? (Explain the main idea of it.) 5. What do Harrah's Casinos use data analytics for? Analytics and Business The practice of business is changing. More and more companies are amassing larger and larger amounts of data, storing them in bigger and bigger databases. Data mining is particularly important for companies that only operate online (such as Amazon or Netflix). The reason is that these companies never meet their customers in person and thus do not have the ability to observe their behavior or directly ask them about their needs. Thus, the ability to deduce customers' preferences from their browsing behavior is key for online retailers. Indeed, Amazon carefully analyzes a user's past transactions (together with transactions from other users) in order to make recommendations about new products. For instance, it may recommend to us a new book (based on other books we have purchased in the past) or a product accessory (based on the accessories other customers have bought). If these recommendations match a user's preferences and needs, then there is a higher chance of a new transaction – and increased sales for Amazon. Automated and data-driven recommendations (also known as recommendation engines) have become the Holy Grail for many Internet retailers. The immense value of recommendation engines can be seen particularly in the example of Netflix, which paid 1 million dollars to a team of scientists who improved their in-house recommendation engine by 10 %. The collection and analysis of data is important not only on the Internet because it is equally important for more traditional (e.g. brick-and-mortar) 6 businesses. Take the example of the credit card industry (or other credit-granting industries, such as mortgage and banking or the insurance industry). Credit card issuers often experience adverse selection in the sense that those consumers who want their products most eagerly are often the ones who also carry the highest risk. Indeed, the reason that a person is desperate for a new credit card may
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