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GATE 2011 results will be announced on March 15, 2011 at 10:00 hrs. at GATE offices of IISc and seven IITs. GATE 2011 results will also be available on the websites of GATE offices GATE 2011 score is valid for TWO YEARS from the date of announcement of the GATE 2011 results. GATE 2011 results may be made available on payment basis to interested organizations (educational institutions, R & D laboratories, industries, etc.) in India and abroad based on a Memorandum of Understanding (MOU) between IIT Madras and the requesting organization. Details in this regard can be obtained from the Chairman, GATE, IIT Madras. The machine-gradable Optical Response Sheets (ORS) are graded and scrutinized with extreme care. There is no provision for regrading and retotalling. No photocopies of the machine-gradable Optical Response Sheets (ORS) will be made available. No correspondence in this regard will be entertained.

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English Sprachkurs Unsere Sprachkurse sind modern, lebendig und auf die Bedürfnisse der Teilnehmer konzipiert.

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The CAT 2010 vouchers can be obtained from selected Axis Bank branches between Monday, 30 August – Monday, 27 September 2010 for Rs. 1400/- (Rs. 700/- for SC/ST candidates)

You don’t need to buy the CAT Bulliten from any bank, but a prospective candidate can buy a scratch-voucher from the designated bank branches, and, later registers online for CAT 2010.Also See CAT 2010 Exam Date

Voucher once sold cannot be refunded under any circumstance including but not limited to loss of voucher, inability to read voucher number due to mishandling of the voucher by candidate, candidate being ineligible for taking CAT, CAT not being a pre-requisite for a programme, and rejection of candidate registration due to incomplete and/or wrong information. If the voucher is lost, voucher number is not readable due to mishandling of the voucher by candidate, and/or candidate thinks he/she has made mistake(s) in filling-out information during the online registration, then the candidate can purchase a new voucher and re-register online

Axis Bank branches: Visit the website www.catiim.in for exact locations of Axis bank branches and their associated outlets where voucher sale is arranged.

Registration for CAT: On purchasing the CAT 2010 voucher, prospective candidates must log on to the website www.catiim.in to register for CAT 2010.

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Mumbai University-Third Year -Semester VII Information Technology Syllabus (Revised)Final Year PROJECT Semester 7 Mumbai University

Guidelines:

1. Project Topic:

• To proceed with the project work it is very important to select a right topic. Project can be undertaken on any subject addressing IT programme. Research and development projects on problems of practical and theoretical interest should be encouraged.

• Project work must be carried out by the group of at least two students and maximum three and must be original.

• Students can certainly take ideas from anywhere, but be sure that they should evolve them in the unique way to suit their project requirements.

• The project work can be undertaken in a research institute or organization/company/any business establishment.

• Student must consult internal guide along with external guide (if any) in selection of topic. Out of the total projects 35 percent may be allowed as to be industry projects, 65 percent projects must be in house.

• Head of department and senior staff in the department will take decision regarding projects.

• Student has to submit weekly progress report to the internal guide and where as internal guide has to keep track on the progress of the project and also has to maintain attendance report. This progress report can be used for awarding term work marks.

• In case of industry projects, visit by internal guide will be preferred.

• Make sure that external project guides are BE graduates.

2. Project Report Format:

At the end of semester a project report should preferably contain at least following details:-

• Abstract

• Introduction

• Aims and objectives

• Literature Surveyed

• Existing system (if any)

• Problem Statement

• Scope

• Proposed System

• Methodology (your approach to solve the problem)

• Analysis

• Details of Hardware & Software

• Design details

• Implementation Plan for next semester

3. Term Work:

Distribution of marks for term work shall be as follows:

Weekly Attendance on Project Day 05 Marks Project Report (Spiral Bound) 10 Marks Term End Presentation (Internal) 10 Marks The final certification and acceptance of TW ensures the satisfactory performance on the above three aspects.

4. Final Assessment:

Project – I examination should be conducted by two examiners appointed by university. Students have to give demonstration and seminar on the Project – I.

Mumbai University-Fourth / Final Year -Semester VII Information Technology Syllabus (Revised) DATA WAREHOUSING AND MINING & BUSINESS INTELLIGENCE

1. Introduction to Data Mining: Motivation for Data Mining, Data Mining-Definition & Functionalities, Classification of DM systems, DM task primitives, Integration of a Data Mining system with a Database or a Data Warehouse, Major issues in Data Mining.

2. Data Warehousing – (Overview Only): Overview of concepts like star schema, fact and dimension tables, OLAP operations, From OLAP to Data Mining.

3. Data Preprocessing: Why? Descriptive Data Summarization, Data Cleaning: Missing Values, Noisy Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Data Discretization and Concept hierarchy generation for numerical and categorical data.

4. Mining Frequent Patterns, Associations, and Correlations: Market Basket Analysis, Frequent Itemsets, Closed Itemsets, and Association Rules, Frequent Pattern Mining, Efficient and Scalable Frequent Itemset Mining Methods, The Apriori Algorithm for finding Frequent Itemsets Using Candidate Generation, Generating Association Rules from Frequent Itemsets, Improving the Efficiency of Apriori, Frequent Itemsets without Candidate Generation using FP Tree, Mining Multilevel Association Rules, Mining Multidimensional Association Rules, From Association Mining to Correlation Analysis, Constraint-Based Association Mining.

5. Classification & Prediction: What is it? Issues regarding Classification and prediction:

• Classification methods: Decision tree, Bayesian Classification, Rule based

• Prediction: Linear and non linear regression

Accuracy and Error measures, Evaluating the accuracy of a Classifier or Predictor.

6. Cluster Analysis: What is it? Types of Data in cluster analysis, Categories of clustering methods, Partitioning methods – K-Means, K-Mediods. Hierarchical Clustering- Agglomerative and Divisive Clustering, BIRCH and ROCK methods, DBSCAN, Outlier Analysis

7. Mining Stream and Sequence Data: What is stream data? Classification, Clustering Association Mining in stream data. Mining Sequence Patterns in Transactional Databases.

8. Spatial Data and Text Mining: Spatial Data Cube Construction and Spatial OLAP, Mining Spatial Association and Co-location Patterns, Spatial Clustering Methods, Spatial Classification and Spatial Trend Analysis. Text Mining Text Data Analysis and Information Retrieval, Dimensionality Reduction for Text, Text Mining Approaches.

9. Web Mining: Web mining introduction, Web Content Mining, Web Structure Mining, Web Usage mining, Automatic Classification of web Documents.

10. Data Mining for Business Intelligence Applications: Data mining for business Applications like Balanced Scorecard, Fraud Detection, Clickstream Mining, Market Segmentation, retail industry, telecommunications industry, banking & finance and CRM etc.

Text Books:

1. Han, Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann 2nd Edition

2. P. N. Tan, M. Steinbach, Vipin Kumar, “Introduction to Data Mining”, Pearson Education

Reference Books:

1. MacLennan Jamie, Tang ZhaoHui and Crivat Bogdan, “Data Mining with Microsoft SQL Server 2008”, Wiley India Edition.

2. G. Shmueli, N.R. Patel, P.C. Bruce, “Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner”, Wiley India.

3. Michael Berry and Gordon Linoff “Data Mining Techniques”, 2nd Edition Wiley Publications.

4. Alex Berson and Smith, “Data Mining and Data Warehousing and OLAP”, McGraw Hill Publication.

5. E. G. Mallach, “Decision Support and Data Warehouse Systems”, Tata McGraw Hill.

6. Michael Berry and Gordon Linoff “Mastering Data Mining- Art & science of CRM”, Wiley Student Edition

7. Arijay Chaudhry & P. S. Deshpande, “Multidimensional Data Analysis and Data Mining Dreamtech Press

8. Vikram Pudi & Radha Krishna, “Data Mining”, Oxford Higher Education.

9. Chakrabarti, S., “Mining the Web: Discovering knowledge from hypertext data”,

10. M. Jarke, M. Lenzerini, Y. Vassiliou, P. Vassiliadis (ed.), “Fundamentals of Data Warehouses”, Springer-Verlag, 1999.

Term Work: Term work shall consist of at least 10 experiments covering all topics Term work should consist of at least 6 programming assignments and one mini project in Business Intelligence and two assignments covering the topics of the syllabus. One written test is also to be conducted. Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks

Test (at least one) 10 Marks The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work. Suggested Experiment List

1. Students can learn to use WEKA open source data mining tool and run data mining algorithms on datasets.

2. Program for Classification – Decision tree, Naïve Bayes using languages like JAVA

3. Program for Clustering – K-means, Agglomerative, Divisive using languages like JAVA

4. Program for Association Mining using languages like JAVA

5. Web mining

6. BI projects: any one of Balanced Scorecard, Fraud detection, Market Segmentation etc.

7. Using any commercial BI tool like SQLServer 2008, Oracle BI, SPSS, Clementine, and XLMiner etc.

ORAL EXAMINATION An oral examination is to be conducted based on the above syllabus.

This is a Sponsored Post written by me on behalf of Heineken. All opinions are 100% mine.

Recently I had a situation when going to a party. A friend of mine had drunk a bit more than he could handle. Well, it’s kind of an awkward situation when you have to tell your friend that he has drunk too much. Not that you can’t tell him normally but he’s drunk! Another friend of mine had a cool app for the exact situation. He told this person to blow in his iphone. The iphone acted like an actual breath analyzer. And we were shell shocked!

It was actually an iphone application from Heineken. What has to be done is you feed in the level you want to show, tell your friend to blow in, and it will display the same level. So it’s not an actual breath analyzer, but a moral stimulator kind of thing where a person can be told that he has drunk too much. To check it out Download the Heineken Breathalyzer iPhone app.

What’s cool in this app is that you can do a lot more things like tag your friend as one of the characters defined by Heineken which normally people turn into after drinking more. You can share it at places and tell your friends more clearly about their facts.

They have also got a nice website where you can try and identify the different characters your friends might turn into after one too many to drink. Visit The Heineken Know The Signs website.

The important fact here to understand is the effort Heineken is taking to spread the message. They tried the same before in a similar campaign “Know The Signs – Enjoy Heineken Responsibly” before and achieved a great success. This time they have come only to take the message even further. Based on their research and opinion from the people they have made the campaign even better than before. They have added some new characters to their old ones.

It is a positive move by them which we should definitely support. Knowing the facts that the number of deaths due to alcoholism has raised twofold in the last 15years. Over 17,000 people in the U.S. die in alcohol-related motor vehicle crashes each year, representing 41% of all traffic-related deaths (NHTSA). Approximately 1.5 million drivers are arrested every year for driving under the influence of alcohol or narcotics.

The KnowTheSigns campaign videos have reached over 750,000 views within one week. Hopefully these large numbers will contribute to an interactive responsible drinking message. With these videos and tools, the users can explore the impact ‘one too many to drink’ can have on people around them. It also enables Heineken to develop a conversation between friends with the iPhone “Breathalyzer” and the social network application ‘Tag of Shame’.

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