Data Analytics for ICT.

Slides:

Advertisements

Similar presentations

Advertisements

© 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac.

Introduction to CS170. CS170 has multiple sections Each section has its own class websites URLs for different sections: Section 000:

Collaborative Filtering in iCAMP Max Welling Professor of Computer Science & Statistics.

COMP 14 Introduction to Programming Miguel A. Otaduy Summer Session I, 2004 MTWRF 9:45-11:15 am Sitterson Hall 014.

Data Mining Adrian Tuhtan CS157A Section1.

Machine Learning Usman Roshan Dept. of Computer Science NJIT.

+ JAVA Programming CCSA Introduction this course is a hands-on course in programming with the Java language for students who have completed a course.

Math-254 Numerical Methods.

Data Mining. 2 Models Created by Data Mining Linear Equations Rules Clusters Graphs Tree Structures Recurrent Patterns.

CSCI 347 – Data Mining Lecture 01 – Course Overview.

C O M P U T E R G R A P H I C S Guoying Zhao 1 / 16 Computer Graphics Course Introduction.

Chapter 7 DATA, TEXT, AND WEB MINING Pages , 311, Sections 7.3, 7.5, 7.6.

Chapter 13 Genetic Algorithms. 2 Data Mining Techniques So Far… Chapter 5 – Statistics Chapter 6 – Decision Trees Chapter 7 – Neural Networks Chapter.

J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 2.

Dept. of Computing Science, University of Aberdeen1 CS4031/CS5012 Data Mining and Visualization Yaji Sripada.

Internet Information Retrieval Sun Wu. Course Goal To learn the basic concepts and techniques of internet search engines –How to use and evaluate search.

Kislaya Prasad, PhD. Artificial Intelligence Ubiquitous Sensors 2.

General Information 439 – Data Mining Assist.Prof.Dr. Derya BİRANT.

AN INTRODUCTION TO JAVA LANGUAGE CT Credit Hours : (3 Lecture, 2 Lab)

SEC835 Security in Databases and Web applications Presentation.

EE 401 Control Systems Analysis and Design Course Overview and Introduction Monday 26 Aug 2013 EE 401: Control Systems Analysis and Design Slide 1 of 10.

Operations Research Models and Methods Advanced Operations Research Reference: Operations Research Models and Methods, Operations Research Models and Methods,

Sotarat Thammaboosadee, Ph.D. EGIT563- Data Mining Course Outline.

Introduction.  Instructor: Cengiz Örencik   Course materials:  myweb.sabanciuniv.edu/cengizo/courses.

1 SBM411 資料探勘陳春賢. 2 Lecture I Class Introduction.

Chapter 3 Building Business Intelligence Chapter 3 DATABASES AND DATA WAREHOUSES Building Business Intelligence 6/22/2016 1Management Information Systems.

Audit Analytics --An innovative course at Rutgers Qi Liu Roman Chinchila.

BUS 660 Week 6 Assignment Research Paper To purchase this material link 660/BUS-660-Week-6-Assignment-Research- Paper.

DEVRY ENGL 216 Entire Course NEW Includes Final Exam Check this A+ tutorial guideline at

BUS 352 Week 5 Assignment Final Paper (E-Business Analysis of Nike) Check this A+ tutorial guideline at

Usman Roshan Dept. of Computer Science NJIT

APPLIED MANAGEMENT SCIENCE IN AGRICULTURAL SYSTEMS I

Information Storage and Retrieval Fall Lecture 1: Introduction and History.

ECE 533 Digital Image Processing

Computer Application in Engineering Design

Make Predictions Using Azure Machine Learning Studio

Syed Sohail Ahmed Assistant Professor, UET Taxila

Farahwahida Mohd LRS13, L2 FITM

Math-254 Numerical Methods.

Adrian Tuhtan CS157A Section1

GSCM 588 MENTOR Education Your Life--gscm588mentor.com ENV 340 STUDY.

SCI 220 Teaching Effectively-- snaptutorial.com

SCI 220 Education on your terms/snaptutorial.com.

SCI 220 Education on your terms/tutorialrank.com.

SCI 220 Education on your terms/tutorialrank.com.

SCI 220 Become Exceptional/ newtonhelp.com. SCI 220 Week 1 WileyPLUS Weekly Exam For more course tutorials visit SCI 220 Week 1 WileyPLUS.

Special Topics in Data Mining Applications Focus on: Text Mining

Introduction to Azure Machine Learning Studio

دانشكده برق دانشگاه علم و صنعت ايران

Teacher name | course number

כריית מידע -- מבוא ד"ר אבי רוזנפלד.

Three major projects: There will be a midterm covering the material presented during the first half of the semester a final covering the material presented.

כריית נתונים.

CS 456 Interactive Software.

Pima Medical Institute Online Education

Course Summary ChengXiang “Cheng” Zhai Department of Computer Science

MHN502E Operations Research I

An Introduction to Java Language

Pima Medical Institute Online Education

Computer Graphics Course Introduction.

An Introduction to Java Language

Dept. of Computer Science University of Liverpool

Folsom High School Class: AP Psychology Grade: 11th & 12th

Welcome! Knowledge Discovery and Data Mining

Folsom High School Class: Econ/AP Government

ENV 100T PAPER Dreams Come True / env100paper.com.

Usman Roshan Dept. of Computer Science NJIT

Azure Machine Learning

Presentation transcript:

Data Analytics for ICT

Course Content Weekly Schedule: Topics: Week 1: Introduction to data analytics and its usage in ICT Week 2: Basic Data Analysis techniques, Linear Regression, introduction to MATLAB and R software environment Week 3: Logistics Regression, Regression techniques for Predicting Customer Growth Week 4: Decision Tree, Classification and Regression Tree (CART) and Random Forest, modeling based on Decision Tree Week 5: Mid Term1 Week 6: Clustering - Hierarchical clustering and k-mean clustering, Data Visualization Week 7: Building models with Recommender system – collaborative filtering, content filtering, triangle closing Weekly Schedule: Topics: Week 8: Text Analytics - Bag-of-words, Sentiment analysis, Topic modeling, Term frequency- Inverse document frequency (TF-IDF) Week 9: Turning information on Internet into Knowledge using Text Analytics Week 10: Mid Term 2 Week 11: Building predictive models using Text Analytics for detecting threats, vandalism and email spams Week 12: Predictive models for detecting Web visitors and customer interests Week 13: Optimization techniques - Linear Optimization, Integer Optimization Week 14: Using models to optimize online sales while maximizing user satisfaction

Course Repository Lab Visit: 1 visit after every 4 classes Assessment: Midterm Exam 1: 25% (Written Exam + Assignment) Midterm Exam 2: 25% (Written Exam + Assignment) Term Final: 40% (Written Exam + Assignment) Presentation: 10% (Paper presentation by each students) References: Data Analytics Made Accessible: 2017 edition by Anil Maheshwari. Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman, Wiley, 2017. R in Action: Data Analysis and Graphics with R by Robert Kabacoff, Manning Publications, 2015. Required Software: R MATLAB

Introduction to data analytics The LinkedIn story What can we tell from data? Is it possible to link the type of fraud activity with DNA? Is it possible to tell customer credit risk history? Is it possible to tell metal health from online avtivities?