Struggling with data at work? Wasting valuable time working in multiple spreadsheets …
Struggling with data at work? Wasting valuable time working in multiple spreadsheets to gain an overview of your business? Find it hard to gain sharp insights from piles of data on your desktop?
If you are looking to enhance your efficiency in the office and improve your performance by making sense of data faster and smarter, then this advanced data analysis course is for you.
If you have already sharpened your spreadsheet skills in Data Analysis: Take It to the MAX(), this course will help you dig deeper. You will learn advanced techniques for robust data analysis in a business environment. This course covers the main tasks required from data analysts today, including importing, summarizing, interpreting, analyzing and visualizing data. It aims to equip you with the tools that will enable you to be an independent data analyst. Most techniques will be taught in Excel with add-ons and free tools available online. We encourage you to use your own data in this course but if not available, the course team can provide.
These course materials are part of an online course of TU Delft. Do you want to experience an active exchange of information between academic staff and students? Then join the community of online learners and enroll in this MOOC. This course is part of the Data Analysis XSeries.
Short Description: This open resources textbook contains 10 Units that describe and …
Short Description: This open resources textbook contains 10 Units that describe and explain the main concepts in statistical analysis of psychological data. In addition to conceptual descriptions and explanations of the basic analyses for descriptive statistics, this textbook also explains how to conduct those analyses with common statistical software (Excel) and open-source free software (R).
Word Count: 27173
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Short Description: Data analytics is a rapidly evolving field. In today's labour …
Short Description: Data analytics is a rapidly evolving field. In today's labour market, knowing how to acquire, process, and interpret large amounts of data to make optimal decisions is crucial for many professionals, especially those in business and engineering. This open textbook, "a new online course" if you will, focuses on three key concept areas: data acquisition, data processing, and decision-making models. In this course, students will be able to develop advanced knowledge and skills to acquire related data for operations of business or projects; apply quantitative literacy skills such as statistics and machine learning; and use predictive or prescriptive modeling to make timely, actionable, and meaningful decisions.
Long Description: Data analytics is a rapidly evolving field. In today’s labour market, knowing how to acquire, process, and interpret large amounts of data to make optimal decisions is crucial for many professionals, especially those in business and engineering. This open textbook, “a new online course” if you will, focuses on three key concept areas: data acquisition, data processing, and decision-making models. In this course, students will be able to develop advanced knowledge and skills to acquire related data for operations of business or projects; apply quantitative literacy skills such as statistics and machine learning; and use predictive or prescriptive modeling to make timely, actionable, and meaningful decisions.
Word Count: 2054
(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)
Short Description: Data analytics is a rapidly evolving field. In today's labour …
Short Description: Data analytics is a rapidly evolving field. In today's labour market, knowing how to acquire, process, and interpret large amounts of data to make optimal decisions is crucial for many professionals, especially those in business and engineering. This open textbook, "a new online course" if you will, focuses on three key concept areas: data acquisition, data processing, and decision-making models. In this course, students will be able to develop advanced knowledge and skills to acquire related data for operations of business or projects; apply quantitative literacy skills such as statistics and machine learning; and use predictive or prescriptive modeling to make timely, actionable, and meaningful decisions.
Long Description: Data analytics is a rapidly evolving field. In today’s labour market, knowing how to acquire, process, and interpret large amounts of data to make optimal decisions is crucial for many professionals, especially those in business and engineering. This open textbook, “a new online course” if you will, focuses on three key concept areas: data acquisition, data processing, and decision-making models. In this course, students will be able to develop advanced knowledge and skills to acquire related data for operations of business or projects; apply quantitative literacy skills such as statistics and machine learning; and use predictive or prescriptive modeling to make timely, actionable, and meaningful decisions.
Word Count: 2038
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Beta Version Word Count: 92165 ISBN: 979-8-88895-422-5 (Note: This resource's metadata has …
Beta Version
Word Count: 92165
ISBN: 979-8-88895-422-5
(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)
Data Carpentry trains researchers in the core data skills for efficient, shareable, …
Data Carpentry trains researchers in the core data skills for efficient, shareable, and reproducible research practices. We run accessible, inclusive training workshops; teach openly available, high-quality, domain-tailored lessons; and foster an active, inclusive, diverse instructor community that promotes and models reproducible research as a community norm.
6.263J / 16.37J focuses on the fundamentals of data communication networks. One …
6.263J / 16.37J focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.
A Claremont Graduate University EDUC 448 Fall 2021 Course Publication Short Description: …
A Claremont Graduate University EDUC 448 Fall 2021 Course Publication
Short Description: This glossary is intended to support professionals who are seeking to understand Data Management and Governance in the context of K-12 and higher education. The definitions included in this ebook provide a fundamental understanding of common Data Management and Governance terms. This glossary was co-created by education professionals and graduate students enrolled in Claremont Graduate University’s EDUC 448: Data Management & Governance course taught by Dr. Gwen Garrison, PhD during the Fall 2021 semester.
Word Count: 2578
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Data that has relevance for managerial decisions is accumulating at an incredible …
Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to-use software and cases.
This course is designed to introduce first-year Sloan MBA students to the …
This course is designed to introduce first-year Sloan MBA students to the fundamental techniques of using data. In particular, the course focuses on various ways of modeling, or thinking structurally about decision problems in order to make informed management decisions.
The Data Renaissance delves into the complexities of data's role in various …
The Data Renaissance delves into the complexities of data's role in various industries and its broader impact on society. It highlights the challenges in investigating data practices, citing examples like TikTok, where algorithms and data handling are closely guarded secrets. The content, contributed by students under the guidance of an expert, covers a wide range of topics, including the ethical aspects of generative AI in education and the workplace, and case studies reflecting real-world experiences. This evolving text, intended to be updated with each class, serves as a dynamic resource for educators and students alike, offering insights and discussion guides for an in-depth understanding of the ever-changing landscape of data in our digital age.
This page shares five units of youcubed lessons for grades 6-10 that …
This page shares five units of youcubed lessons for grades 6-10 that introduce students (and teachers) to data science. The units start with an introduction to the concept of data and move to lessons that invite students to explore their own data sets. These lessons teach important content through a pattern-seeking, exploratory approach, and are designed to engage students actively.
Data Science LessonsThis page shares five units of youcubed lessons for grades 6-10 that introduce students (and teachers) to data science. The units start with an introduction to the concept of data and move to lessons that invite students to explore their own data sets. These lessons teach important content through a pattern-seeking, exploratory approach, and are designed to engage students actively. The culminating unit is a citizen science project that gives students an opportunity to conduct a data inquiry. The lessons accompany a new online course for teachers, where some of the lessons are featured, along with other lesson ideas. These lessons are offered with ideas for in-person or online teaching, and can be taught at any time of year.
LessonsTeacher Online Course: 21st Century Teaching and LearningUnit 1: Data Is EverywhereUnit 2: Working With Data Analysis ToolsUnit 3: Measures of Center & SpreadUnit 4: Understanding VariabilityUnit 5: A Community Data Collection Project
ResourcesHigh School Data Science CourseCODAPWhat's Going On In This Graph?Data Science Initiative VideoThe Data Science K-12 MovementData Talks
Word Count: 6664 Included H5P activities: 11 (Note: This resource's metadata has …
Word Count: 6664
Included H5P activities: 11
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Cleaning, reshaping, and transforming data for analysis and visualization, with R and …
Cleaning, reshaping, and transforming data for analysis and visualization, with R and the Tidyverse
Word Count: 3515
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Short Description: Database Design - 2nd Edition covers database systems and database …
Short Description: Database Design - 2nd Edition covers database systems and database design concepts. New to this edition are SQL info, additional examples, key terms and review exercises at the end of each chapter.
Long Description: This second edition of Database Design book covers the concepts used in database systems and the database design process. Topics include: The history of databases Characteristics and benefits of databases Data models Data modelling Classification of database management systems Integrity rules and constraints Functional dependencies Normalization Database development process
Word Count: 30650
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Database Design - 2nd Edition covers database systems and database design concepts. …
Database Design - 2nd Edition covers database systems and database design concepts. New to this edition are SQL info, additional examples, key terms and review exercises at the end of each chapter.
This course addresses information technology fundamentals, including project management and software processes, …
This course addresses information technology fundamentals, including project management and software processes, data modeling, UML, relational databases and SQL. Topics covered include internet technologies, such as XML, web services, and service-oriented architectures. This course provides an introduction to security and presents the fundamentals of telecommunications and includes a project that involves requirements / design, data model, database implementation, website, security and data network. No prior programming experience required.
Database Security is the utmost key part for any type of database, …
Database Security is the utmost key part for any type of database, .for example financial information, personal information, employee information and enterprise information. This book will cover following topics such as creating and altering database user, password profiling, various privileges and virtual private database. All the topics are implemented by using oracle 11g software. Especially for readers this book will give clarity about database security concepts such as Authorization, Authentication and Access control. The practical part using oracle provides how to carry out database security concepts technically for the reader.
This course relies on primary readings from the database community to introduce …
This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend.
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