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-2000+ Questions Based on Latest Pattern with detailed Solutions -Covers all the types of DI such as Table| Pie | Bar | Line | Caselet |Radar -Includes Arithmetic Based & Missing DI asked in IBPS/SBI Mains Examinations -Includes Previous year questions asked in SBI Po mains 2018, IBPS PO mains 2018 and other exams. -Essential for both Prelims and Mains exams A Complete Book on Data Interpretation and Analysis eBook’ is an effort to assist all the government job aspirants with a comprehensive, reliable and satisfactory source of offline practice materials to improve their proficiency in Quantitative Aptitude. This ebook is a unique approach towards fulfilling the needs of our dedicated aspirants who wish to clear any obstacle with ease. We should never be confined by the limits of our brain and this eBook which is thoroughly revised and covers every crucial aspect of all the Banking and Insurance examinations assures you that it will help you in transcending your limits. The ebook comprises more than 300 DIs which include 2000+ Questions covering all the patterns and topics that the IBPS, SBI and other banking exams have been surprising us with for last few years. The ebook is elegantly divided into different chapters namely Table, Bar Graph, Line Graph, Pie Graph, Mixed Graph, Arithmetic and Caselets. Each chapter is further categorized into four parts – Solved Examples, Previous years’ exercises, Level 1 exercise (Basic to Moderate) and Level 2 exercise (Advance). There are new methods and approach to solving the latest pattern questions within a short time limit. Detailed solutions are provided to every question for better CONCEPTUAL learning. In the second edition, we have includes more than 500 Questions based on latest pattern and questions asked in recent exams like SBI PO 2018, IBPS PO 2018, RRB PO 2018 and other exams. The questions are duly framed and prepared by our best faculties in this field. While preparing, all the necessities including minute details have been taken care of. The questions are preferably selected based on their quality, inculcating different levels and types that are being asked in the banking and insurance examinations. The ebook will be extremely helpful in preparing for all the Banking and Insurance examinations like IBPS PO, SBI PO, BANK OF BARODA PO, SYNDICATE BANK PO, RBI ASSISTANT, OICL, UIIC, etc.
Novice and early career researchers often have difficulty with understanding how theory, data analysis and interpretation of findings “hang together” in a well-designed and theorized qualitative research investigation and with learning how to draw on such understanding to conduct rigorous data analysis and interpretation of their analytic results. Data Analysis, Interpretation, and Theory in Literacy Studies Research demonstrates how to design, conduct and analyze a well put together qualitative research project. Using their own successful studies, chapter authors spell out a problem area, research question, and theoretical framing, carefully explaining their choices and decisions. They then show in detail how they analyzed their data, and why they took this approach. Finally, they demonstrate how they interpreted the results of their analysis, to make them meaningful in research terms. Approaches include interactional sociolinguistics, microethnographic discourse analysis, multimodal analysis, iterative coding, conversation analysis, and multimediated discourse analysis, among others. This book will appeal to beginning researchers and to literacy researchers responsible for teaching qualitative literacy studies research design at undergraduate and graduate levels. Perfect for courses such as: Literacy Research Seminar | Introduction to Qualitative Research | Advanced Research Methods | Studying New Literacies and Media | Research Perspectives in Literacy | Discourse Analysis | Advanced Qualitative Data Analysis | Sociolinguistic Analysis | Classroom Language Research
"This book is written for all medical students and is ideal for OSCE practice, during ward rounds and clinical years ..." -- BOOK COVER.
A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.
"Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors"--
"In Responding to Data, author Eileen Depka expounds on how implementing a proficient system of assessment yields actionable data. In order to better respond to student needs, educators need the know-how of conducting proper assessment design, data collection, and data interpretation. This book provides systems, processes, and specific examples that help teachers thoroughly understand how to preplan assessments and collect the data available to them. The tools and templates outline how teachers can determine if the collective data indicate a response is needed and how teachers can then turn gathered information into immediate actions. By reading this book, educators gain strategies that build a culture of data analysis and student achievement in their classrooms"--
Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key FeaturesBridge your data analysis with the power of programming, complex algorithms, and AIUse Python and its extensive libraries to power your way to new levels of data insightWork with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time seriesExplore this modern approach across with key industry case studies and hands-on projectsBook Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learnA new toolset that has been carefully crafted to meet for your data analysis challengesFull and detailed case studies of the toolset across several of today’s key industry contextsBecome super productive with a new toolset across Python and Jupyter NotebookLook into the future of data science and which directions to develop your skills nextWho this book is for This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development.
In qualitative research, one can often hear the statement that research results are just (social) constructions. In criminal cases and in court hearings, we tend to expect that the true sequence of events has to be found rather than just any story. Here the author shows that qualitative social research can be conducted in the manner of police work or court proceedings. He does so by exhibiting how short pieces of transcriptions can be approached to uncover who, when, where, and how participated, what kind of social situation produced the transcription, and so on without any background knowledge other than that talk itself. Commenting on transcriptions of a researcher in the course of doing rigorous data analysis, readers learn doing ethnographically adequate accounts and critical institutional ethnography “at the elbow” of an experienced practitioners. Further topics include the role of turn sequences, the ethnomethods of knowledge-power and institutional relations, the documentary method of interpretation, and time-sensitive social analysis.
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples