Understand how to involve learners in the assessment process 1. evaluate how to involve the learner n the assessment process 2. Analyse the role of peer and self assessment in the assessment process Learners flourish with the help of well-trained teachers who know how to clarify the learning objectives‚ assess the leaner’s‚ and make changes along the way that can support the maximum learning potential of each student. To do this one must carefully align the objectives with the planned assessments
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IT433 Data Warehousing and Data Mining — Data Preprocessing — 1 Data Preprocessing • Why preprocess the data? • Descriptive data summarization • Data cleaning • Data integration and transformation • Data reduction • Discretization and concept hierarchy generation • Summary 2 Why Data Preprocessing? • Data in the real world is dirty – incomplete: lacking attribute values‚ lacking certain attributes of interest‚ or containing only aggregate data • e.g.‚ occupation=“ ”
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an era of big data‚ this data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives; however‚ there are a number of challenges that must be addressed to allow us to exploit the full potential of big data. This paper focuses on challenges faced by online retailers when making use of big data. With the provided examples of online retailers Amazon and eBay‚ this paper addressed the key challenges of big data analytics including data capture and
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|Case Study: Data for Sale | |Management Information System | | | |
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Data Models Consider a simple student registration. Specifically we want to support the tasks of students registering for or withdrawing from a class. To do this‚ the system will need to record data about what entities? What specific data about the entities will need to be stored? What is the cardinality between students and courses? Diagram the data model. While‚ considering a student class registration system for registering or withdrawing a system must have the capability to record data in
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LECTURE 1 DATA TYPES Our interactions (inputs and outputs) of a program are treated in many languages as a stream of bytes. These bytes represent data that can be interpreted as representing values that we understand. Additionally‚ within a program we process this data that can be interpreted as representing values that we understand. Additionally‚ within a program we process this data in various way such as adding them up or sorting them. This data comes in different forms. Examples include: your
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BCSCCS 303 R03 DATA STRUCTURES (Common for CSE‚ IT and ICT) L T P CREDITS 3 1 0 4 UNIT - I (15 Periods) Pseudo code & Recursion: Introduction – Pseudo code – ADT – ADT model‚ implementations; Recursion – Designing recursive algorithms – Examples – GCD‚ factorial‚ fibonnaci‚ Prefix to Postfix conversion‚ Tower of Hanoi; General linear lists – operations‚ implementation‚ algorithms UNIT - II (15 Periods)
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Enhancing Customer Data Enhanced Customer Data Repository is a secure and fully supported data repository with problem determination tools and functions. It updates problem management records (PMR) and maintains full data life cycle management. · combination of all the internal structured business data (CRM‚ ERP‚ POS and all the internal system data) and external unstructured data ( Social media data‚ feedback surveys‚ Audios‚ Videos‚ streaming data‚ Call center data‚ images) · unmanageable volumes
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WEEK-3 Data Abstraction Destructors • Destructors are functions without any type • The name of a destructor is the character ’~’ followed by class name – For example: ~clockType(); • A class can have only one destructor – The destructor has no parameters • Destructor automatically executes when the class object goes out of scope C++ Programming: Program Design Including Data Structures‚ Sixth Edition 2 Data Abstract‚ Classes‚ and Abstract Data Types • Abstraction – i
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Data mining Data mining is simply filtering through large amounts of raw data for useful information that gives businesses a competitive edge. This information is made up of meaningful patterns and trends that are already in the data but were previously unseen. The most popular tool used when mining is artificial intelligence (AI). AI technologies try to work the way the human brain works‚ by making intelligent guesses‚ learning by example‚ and using deductive reasoning. Some of the more popular
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