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Author: Srinivasa K G
Publisher: Springer Nature
ISBN: 3030702588
Category : Education
Languages : en
Pages : 326
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Book Description
This book A Beginner’s Guide to Learning Analytics is designed to meet modern educational trends’ needs. It is addressed to readers who have no prior knowledge of learning analytics and functions as an introductory text to learning analytics for those who want to do more with evaluation/assessment in their organizations. The book is useful to all who need to evaluate their learning and teaching strategies. It aims to bring greater efficiency and deeper engagement to individual students, learning communities, and educators. Covered here are the key concepts linked to learning analytics for researchers and practitioners interested in learning analytics. This book helps those who want to apply analytics to learning and development programs and helps educational institutions to identify learners who require support and provide a more personalized learning experience. Like chapters show diverse uses of learning analytics to enhance student and faculty performance. It presents a coherent framework for the effective translation of learning analytics research for educational practice to its practical application in different educational domains. This book provides educators and researchers with the tools and frameworks to effectively make sense of and use data and analytics in their everyday practice. This book will be a valuable addition to researchers’ bookshelves.
Author: Srinivasa K G
Publisher: Springer Nature
ISBN: 3030702588
Category : Education
Languages : en
Pages : 326
Get Book
Book Description
This book A Beginner’s Guide to Learning Analytics is designed to meet modern educational trends’ needs. It is addressed to readers who have no prior knowledge of learning analytics and functions as an introductory text to learning analytics for those who want to do more with evaluation/assessment in their organizations. The book is useful to all who need to evaluate their learning and teaching strategies. It aims to bring greater efficiency and deeper engagement to individual students, learning communities, and educators. Covered here are the key concepts linked to learning analytics for researchers and practitioners interested in learning analytics. This book helps those who want to apply analytics to learning and development programs and helps educational institutions to identify learners who require support and provide a more personalized learning experience. Like chapters show diverse uses of learning analytics to enhance student and faculty performance. It presents a coherent framework for the effective translation of learning analytics research for educational practice to its practical application in different educational domains. This book provides educators and researchers with the tools and frameworks to effectively make sense of and use data and analytics in their everyday practice. This book will be a valuable addition to researchers’ bookshelves.
Author: Srinivasa K G
Publisher:
ISBN: 9783030702595
Category :
Languages : en
Pages : 0
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Book Description
This book A Beginner's Guide to Learning Analytics is designed to meet modern educational trends' needs. It is addressed to readers who have no prior knowledge of learning analytics and functions as an introductory text to learning analytics for those who want to do more with evaluation/assessment in their organizations. The book is useful to all who need to evaluate their learning and teaching strategies. It aims to bring greater efficiency and deeper engagement to individual students, learning communities, and educators. Covered here are the key concepts linked to learning analytics for researchers and practitioners interested in learning analytics. This book helps those who want to apply analytics to learning and development programs and helps educational institutions to identify learners who require support and provide a more personalized learning experience. Like chapters show diverse uses of learning analytics to enhance student and faculty performance. It presents a coherent framework for the effective translation of learning analytics research for educational practice to its practical application in different educational domains. This book provides educators and researchers with the tools and frameworks to effectively make sense of and use data and analytics in their everyday practice. This book will be a valuable addition to researchers' bookshelves.
Author: Simplilearn
Publisher: IndraStra Whitepapers
ISBN:
Category : Computers
Languages : en
Pages : 25
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Book Description
Data analytics is increasingly becoming a key element in shaping a company’s business strategy. Today, data influences every decision made by an organization, and this is driving the wide-scale adoption of data analytics, including machine learning technologies and artificial intelligence solutions. The heightened focus is propelling a surge in data analytics spending, reflected in various studies conducted by leading market research firms. The field of data analytics offers some amazing salaries and is not only the hottest IT job, but it is also one of the best-paying jobs in the world. This guide aims at providing the readers with everything they need to know about the data analytics field, basic terminologies, key concepts, real-life use cases, skills you must master in order to scale up your career, and training and certifications you might need to reach your dream job.
Author: Alex Root
Publisher:
ISBN: 9781691418831
Category :
Languages : en
Pages : 184
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Book Description
Learn data analysis using Python with this easy to follow beginners guide. It covers all aspects of processing, manipulation, crunching, and cleaning data using Python programming language. It has been designed to prepare you for: analyzing data creating relevant data visualizations carrying out statistical analyses for large data estimating the upcoming future trends by using current data and lots more! This book will help you learn the various parts of Python programming language, its libraries, and scientific computation using Python. Learn to practically solve extensive sets of problems related to data analysis. Python is on par with other programming languages like MATLAB, Stata, R, SAS, and others when it comes to data analysis and data visualization. Python's rich set of libraries (mainly Pandas) has grown rapidly in recent years and is considered one of the best among its competitors for tasks related to data manipulation. When combined with Python's own internal solidity, as a general purpose programming language, we can say that it is an excellent choice to build data centric web applications. You will learn how to use the essential Python libraries required for data analysis like NumPy, Pandas, matplotlib, IPython, and SciPy. Each one of them performs a particular functionality for data analysis and you will be surprised at how easy it is. So what are you waiting for? Now is your chance to learn hands on Python with ease. Click the BUY NOW button to get started on your Python journey.
Author: Renee M. P. Teate
Publisher: John Wiley & Sons
ISBN: 1119669391
Category : Computers
Languages : en
Pages : 400
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Book Description
Jump-start your career as a data scientist—learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner’s perspective, moving your data scientist career forward!
Author: John R Mattox II
Publisher: Kogan Page Publishers
ISBN: 0749476362
Category : Business & Economics
Languages : en
Pages : 256
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Book Description
Faced with organizations that are more dispersed, a workforce that is more diverse and the pressure to reduce costs, CEOs and CFOs are increasingly asking what the return on investment is from training and development programmes. Learning Analytics provides a framework for understanding how to work with learning analytics at an advanced level. It focuses on the questions that training evaluation is intended to answer: is training effective and how can it be improved? It discusses the field of learning analytics, outlining how and why analytics can be useful, and takes the reader through examples of approaches to answering these questions and looks at the valuable role that technology has to play. Even where technological solutions are employed, the HR or learning and development practitioner needs to understand what questions they should be asking of their data to ensure alignment between training and business needs. Learning Analytics enables both senior L&D and HR professionals as well as CEOs and CFOs to see the transformational power that effective analytics has for building a learning organization, and the impacts that this has on performance, talent management, and competitive advantage. It helps learning and development professionals to make the business case for their activities, demonstrating what is truly adding value and where budgets should be spent, and to deliver a credible service to their business by providing metrics based on which sound business decisions can be made.
Author: Oliver Theobald
Publisher:
ISBN: 9781081762469
Category :
Languages : en
Pages : 88
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Book Description
While exposure to data has become more or less a daily ritual for the rank-and-file knowledge worker, true understanding-treated in this book as data literacy-resides in knowing what lies behind the data. Everything from the data's source to the specific choice of input variables, algorithmic transformations, and visual representation shape the accuracy, relevance, and value of the data and mark its journey from raw data to business insight. It's also important to grasp the terminology and basic concepts of data analytics as much as it is to have the financial literacy to be successful as a decisionmaker in the business world. In this book, we make sense of data analytics without the assumption that you understand specific data science terminology or advanced programming languages to set you on your path. Topics covered in this book: Data Mining Big Data Machine Learning Alternative Data Data Management Web Scraping Regression Analysis Clustering Analysis Association Analysis Data Visualization Business Intelligence
Author: Renato Baruti
Publisher: Packt Publishing Ltd
ISBN: 1788398688
Category : Computers
Languages : en
Pages : 219
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Book Description
Implement your Business Intelligence solutions without any coding - by leveraging the power of the Alteryx platform About This Book Experience the power of codeless analytics using Alteryx, a leading Business Intelligence tool Uncover hidden trends and valuable insights from your data across different sources and make accurate predictions Includes real-world examples to put your understanding of the features in Alteryx to practical use Who This Book Is For This book is for aspiring data professionals who want to learn and implement self-service analytics from scratch, without any coding. Those who have some experience with Alteryx and want to gain more proficiency will also find this book to be useful. A basic understanding of the data science concepts is all you need to get started with this book. What You Will Learn Create efficient workflows with Alteryx to answer complex business questions Learn how to speed up the cleansing, data preparing, and shaping process Blend and join data into a single dataset for self-service analysis Write advanced expressions in Alteryx leading to an optimal workflow for efficient processing of huge data Develop high-quality, data-driven reports to improve consistency in reporting and analysis Explore the flexibility of macros by automating analytic processes Apply predictive analytics from spatial, demographic, and behavioral analysis and quickly publish, schedule Share your workflows and insights with relevant stakeholders In Detail Alteryx, as a leading data blending and advanced data analytics platform, has taken self-service data analytics to the next level. Companies worldwide often find themselves struggling to prepare and blend massive datasets that are time-consuming for analysts. Alteryx solves these problems with a repeatable workflow designed to quickly clean, prepare, blend, and join your data in a seamless manner. This book will set you on a self-service data analytics journey that will help you create efficient workflows using Alteryx, without any coding involved. It will empower you and your organization to take well-informed decisions with the help of deeper business insights from the data.Starting with the fundamentals of using Alteryx such as data preparation and blending, you will delve into the more advanced concepts such as performing predictive analytics. You will also learn how to use Alteryx's features to share the insights gained with the relevant decision makers. To ensure consistency, we will be using data from the Healthcare domain throughout this book. The knowledge you gain from this book will guide you to solve real-life problems related to Business Intelligence confidently. Whether you are a novice with Alteryx or an experienced data analyst keen to explore Alteryx's self-service analytics features, this book will be the perfect companion for you. Style and approach Comprehensive, step by step guide filled with real-world examples to step through the complex business questions using one of the leading data analytics platform.
Author: Leonard Deep
Publisher:
ISBN: 9781097858309
Category :
Languages : en
Pages : 236
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Book Description
Are you interested to get into the programming world? Do you want to learn and understand Python and Machine Learning? Python Machine Learning for Beginners is the guide for you. Python Machine Learning for Beginners is the ultimate guide for beginners looking to learn and understand how Python programming works. Python Machine Learning for Beginners is split up into easy to learn chapters that will help guide the readers through the early stages of Python programming. It's this thought out and systematic approach to learning which makes Python Machine Learning for Beginners such a sought-after resource for those that want to learn about Python programming and about Machine Learning using an object-oriented programming approach. Inside Python Machine Learning for Beginners you will discover: An introduction to Machine Learning The main concepts of Machine Learning The basics of Python for beginners Machine Learning with Python Data Processing, Analysis, and Visualizations Case studies and much more! Throughout the book, you will learn the basic concepts behind Python programming which is designed to introduce you to Python programming. You will learn about getting started, the keywords and statements, data types and type conversion. Along with different examples, there are also exercises to help ensure that the information sinks in. You will find this book an invaluable tool for starting and mastering Machine Learning using Python. Once you complete Python Machine Learning for Beginners, you will be more than prepared to take on any Python programming. Scroll back up to the top of this page and hit BUY IT NOW to get your copy of Python Machine Learning for Beginners! You won't regret it!
Author: Sakti Mishra
Publisher: Packt Publishing Ltd
ISBN: 180107772X
Category : Computers
Languages : en
Pages : 430
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Book Description
Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services Key FeaturesBuild data pipelines that require distributed processing capabilities on a large volume of dataDiscover the security features of EMR such as data protection and granular permission managementExplore best practices and optimization techniques for building data analytics solutions in Amazon EMRBook Description Amazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS. What you will learnExplore Amazon EMR features, architecture, Hadoop interfaces, and EMR StudioConfigure, deploy, and orchestrate Hadoop or Spark jobs in productionImplement the security, data governance, and monitoring capabilities of EMRBuild applications for batch and real-time streaming data analytics solutionsPerform interactive development with a persistent EMR cluster and NotebookOrchestrate an EMR Spark job using AWS Step Functions and Apache AirflowWho this book is for This book is for data engineers, data analysts, data scientists, and solution architects who are interested in building data analytics solutions with the Hadoop ecosystem services and Amazon EMR. Prior experience in either Python programming, Scala, or the Java programming language and a basic understanding of Hadoop and AWS will help you make the most out of this book.
