Information Systems Management Research Project ON Data Warehousing and Data Mining Submitted in Partial fulfilment of requirement of award of MBA degree of GGSIPU‚ New Delhi Submitted By: Swati Singhal (12015603911) Saba Afghan (11415603911) 2011-2013
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Centre for Management Technology Knowledge Park I Gr. Noida Acknowledgements If words are considered to be signs of gratitude then let these words Convey the very same My sincere gratitude to ICICI BANK for providing me with an opportunity to work with BANK and giving necessary directions on doing this project to the best of my abilities. I am highly indebted to Mr.Remay Verma‚ Branch Manager and Mr. Ravi Parmar (RM) company project guide‚ who has provided me with
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applying theory to economic practice. After completing this module‚ students will be familiar with: the procedure of hypothesis testing; the possible outcomes in hypothesis testing; the difference between significant and nonsignificant statistical findings. After completing this module‚ students will be able to: define what is meant by a hypothesis and hypothesis testing; understand the logic of hypothesis testing and describe the steps of hypothesis testing procedure;
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No. 3. May 2013 Issue. Pp. 71 – 88 Performance Appraisal of Indian Public Sector Banks Parvesh Kumar Aspal * and Naresh Malhotra** The strength of economy of any country basically hinges on the strength and efficiency of financial system‚ which‚ in turn‚ depends upon a sound banking system. The regulators have recommended bank’s supervision through CAMEL rating model to assess the performance of banks‚ which is better than the earlier systems. The prime objective of CAMEL model of rating
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STATISTICAl ANALYSIS In the evolution of any scientific discipline there is a period in which attempts are made to develop mathematical theories in order to account for and explain the observations generated by the phenomena with which the discipline is concerned. During this period‚ the qualitative and verbal theories are replaced‚ or supplemented‚ by quantitative and mathematical theories which express‚ in the form of some type of equation or equations‚ a postulated mechanism or model that can
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insight into the usage of data warehousing and data mining techniques to enhance the productivity of the business. The study of the processes is analysed so as to get the need of adaptation according to inherent demands of these industries in near future. The main topics we are discussing here are: a) Data warehousing b) Data Mining c) ETL d) Data Mart An attempt has been made to analyse different ways of using these for the enhancement in the different field. Data warehousing and current
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DATA WAREHOUSES & DATA MINING Term-Paper In Management Support System [pic] Submitted By: Submitted To: Chitransh Naman Anita Ma’am A22-JK903 Lecturer 10900100 MSS ABSTRACT :- Collection of integrated‚ subject-oriented‚ time-variant and non-volatile data in support of managements decision making process. Described as the "single point of truth"‚ the "corporate memory"‚ the sole historical register of virtually all transactions
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Statistical Reasoning in Everyday Life This chapter taught me the importance of understanding statistical data and how to evaluate it with common sense. Almost everyday we are subjected to statistical data in newspapers and on TV. My usual reaction was to accept those statistics as being valid. Which I think is a fair assessment for most people. However‚ reading this chapter opens my eyes to the fact that statistical data can be very misleading. It shows how data can be skewed to support a certain
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Population Sample 3.3. Research Instruments 3.4. Reliability and Validity 3.5. Data Collection Procedures 3.6. Data Analysis 4. Findings and Discussion 5. Conclusion and Recommendation 6. References Page 2–3 3 3 3 3-4 4-5 5 6 6 6-7 8 - 11 12 12 13 13 - 14 14 14 - 15 15 - 31 31 - 33 34 - 37 1|Page 1.0 Background of The Study Our research introduces the phenomenon on the satisfaction of service quality in CIMB Bank. It consists of the background of study‚ problem statement‚ objectives of the
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Censored data & Truncated data Censoring occurs when an observation or a measurement is outside the range and people don’ t know the certain value. The value is always above or below the range that people set. However‚ truncated data means that because of the limits‚ such as time‚ or space‚ people lose some data. Truncation is to cut off the data. In other words‚ we have collected and use the data‚ but the data is not in the range we have. It is called censored data. We don’t use the data because
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