Data Analysis Foundation Courseware
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Data Analysis Foundation Courseware
Author: Van Haren Learning Solutions a.o.
language: en
Publisher: Van Haren
Release Date: 2024-01-03
This Data Analysis Foundation Courseware enable a Data Analysis Foundation course, providing a comprehensive introduction to data concepts and the essence of data analysis, subsequently delving into the fundamental principles of Data Analysis, such as statistics and probability. Moreover, this course concentrates on extensively used data analysis techniques like regression and a step-by-step guide to executing them. Data Analysis refers to the systematic application of statistical and mathematical techniques to gain insights, explore, and scrutinize data in order to identify patterns and draw meaningful conclusions that assist businesses in making informed decisions based on data. This process typically encompasses various stages, including data collection, exploration, cleansing, pre-processing, and data organization. Frequently, data analysis is an ongoing, iterative procedure wherein data is continuously collected and analyzed simultaneously. There are two primary approaches to data analysis. Quantitative techniques involve working with numerical data and employ statistical measures, percentages, and calculations. These methods utilize algorithms, mathematical analysis tools, and software to manipulate data and reveal concealed business value. For instance, quantitative data analysis is employed to evaluate market data, aiding a company in determining an appropriate price for a new product. Qualitative data analysis entails working with non-numerical data, specifically categorical variables. Qualitative data analysis is also applicable in various business processes, including identifying themes and patterns and addressing research inquiries, aiming to enhance a product.
Data Analysis Foundation Courseware
''This Data Analysis Foundation Courseware enable a Data Analysis Foundation course, providing a comprehensive introduction to data concepts and the essence of data analysis, subsequently delving into the fundamental principles of Data Analysis, such as statistics and probability. Moreover, this course concentrates on extensively used data analysis techniques like regression and a step-by-step guide to executing them. Data Analysis refers to the systematic application of statistical and mathematical techniques to gain insights, explore, and scrutinize data in order to identify patterns and draw meaningful conclusions that assist businesses in making informed decisions based on data. This process typically encompasses various stages, including data collection, exploration, cleansing, pre-processing, and data organization. Frequently, data analysis is an ongoing, iterative procedure wherein data is continuously collected and analyzed simultaneously. There are two primary approaches to data analysis. Quantitative techniques involve working with numerical data and employ statistical measures, percentages, and calculations. These methods utilize algorithms, mathematical analysis tools, and software to manipulate data and reveal concealed business value. For instance, quantitative data analysis is employed to evaluate market data, aiding a company in determining an appropriate price for a new product. Qualitative data analysis entails working with non-numerical data, specifically categorical variables. Qualitative data analysis is also applicable in various business processes, including identifying themes and patterns and addressing research inquiries, aiming to enhance a product. ata analysis. Quantitative techniques involve working with numerical data and employ statistical measures, percentages, and calculations. These methods utilize algorithms, mathematical analysis tools, and software to manipulate data and reveal concealed business value. For instance, quantitative data analysis is employed to evaluate market data, aiding a company in determining an appropriate price for a new product. Qualitative data analysis entails working with non-numerical data, specifically categorical variables. Qualitative data analysis is also applicable in various business processes, including identifying themes and patterns and addressing research inquiries, aiming to enhance a product. ata analysis. Quantitative techniques involve working with numerical data and employ statistical measures, percentages, and calculations. These methods utilize algorithms, mathematical analysis tools, and software to manipulate data and reveal concealed business value. For instance, quantitative data analysis is employed to evaluate market data, aiding a company in determining an appropriate price for a new product. Qualitative data analysis entails working with non-numerical data, specifically categorical variables. Qualitative data analysis is also applicable in various business processes, including identifying themes and patterns and addressing research inquiries, aiming to enhance a product. ata analysis. Quantitative techniques involve working with numerical data and employ statistical measures, percentages, and calculations. These methods utilize algorithms, mathematical analysis tools, and software to manipulate data and reveal concealed business value. For instance, quantitative data analysis is employed to evaluate market data, aiding a company in determining an appropriate price for a new product. Qualitative data analysis entails working with non-numerical data, specifically categorical variables. Qualitative data analysis is also applicable in various business processes, including identifying themes and patterns and addressing research inquiries, aiming to enhance a product. value. For instance, quantitative data analysis is employed to evaluate market data, aiding a company in determining an appropriate price for a new product. Qualitative data analysis entails working with non-numerical data, specifically categorical variables. Qualitative data analysis is also applicable in various business processes, including identifying themes and patterns and addressing research inquiries, aiming to enhance a product.
ITIL® 4 Foundation Courseware - English
Author: Van Haren Learning Solutions a.o.
language: en
Publisher: Van Haren
Release Date: 2019-02-28
Besides the ITIL® 4 Foundation Courseware - English (ISBN: 978 94 018 0394 6) publication you are advised to obtain the ITIL® 4 - A Pocket Guide (ISBN: 978 94 018 0439 4). The course is designed as an introduction to ITIL 4 and enables you to understand a new way to look at IT Service Management through a Service Value System (SVS). ITIL 4 provides an end-to-end picture of what means to contribute to business value, and also integrates concepts from models such as Lean IT, Agile and DevOps. This course is for those who are involved in the delivery of IT services and need an understanding of best practice in IT Service Management. Student must pass a 60 minute, 40 question closed book multiple choice, examination with a passing score of 65% in order to receive this certification. You can write the exam at any time and at any place after the course. The test is done via your own computer proctored via webcam. Candidates wishing to be trained and pass the exam for this qualification would be recommended to have a general awareness of IT and appreciation of their own business environment. You’ll learn: • Understand the key concepts of service management • Understand how the ITIL guiding principles can help an organization adopt and adapt service management • Understand the four dimensions of service management • Understand the purpose and components of the ITIL service value system • Understand the activities of the service value chain, and how they interconnect • Know the purpose and key terms of 18 ITIL practices • Understand 7 ITIL practices ITIL® is a registered trade mark of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. This product is only for courseware partners, affiliates or designated students. Any course should always include an exam.