{"id":2127,"date":"2018-09-29T10:01:07","date_gmt":"2018-09-29T10:01:07","guid":{"rendered":"http:\/\/sadievrenseker.com\/wp\/?p=2127"},"modified":"2018-11-24T15:07:08","modified_gmt":"2018-11-24T15:07:08","slug":"business-analytics-2018","status":"publish","type":"post","link":"https:\/\/sadievrenseker.com\/?p=2127","title":{"rendered":"Business Analytics 2018"},"content":{"rendered":"

\u0130stanbul City University<\/strong><\/p>\n

Course Name: Business Analytics<\/strong><\/p>\n

Course Code:<\/strong> MBA 548<\/p>\n

Language of Course:<\/strong> English<\/p>\n

Credit:<\/strong> 3<\/p>\n

Course Coordinator \/ Instructor:<\/strong> \u015eadi Evren \u015eEKER<\/p>\n

Contact:<\/strong> businessA@sadievrenseker.com<\/p>\n

Schedule:<\/strong> Sat 13.00 – 16.00<\/p>\n

Course Description: \u00a0<\/strong>This course is an introduction level course to data analsis, specialized on business processes and real life cases.<\/p>\n

This course will uncover you of the information analytics hones executed in the business globe. We will investigate such magic ranges Concerning illustration the explanatory process, how information will be created, stored, accessed, what’s more entryway the association meets expectations with information and makes nature’s turf in which analytics could prosper. The thing that you take in this span will provide for you An solid framework On the whole those territories that backing analytics What’s more will assistance you on preferred position yourself to victory inside your association. You\u2019ll create abilities What’s more An viewpoint that will settle on you All the more profitable speedier Also permit you should turned a profitable advantage should your association. This span additionally gives a support for setting off deeper under propelled investigative Furthermore computational methods, which you bring a chance to investigate On future courses of the information Analytics for benefits of the business specialization.<\/p>\n

This course is outlined with have wide bid over Numerous sorts from claiming learners. Anybody who is looking should get an Comprehension about how benefits of the business analytics is really performed for genuine associations will profit. This course will be essential pointed toward experts who have a bachelor\u2019s degree or A percentage introduction of the benefits of the business reality. The individuals for specialized foul degrees or a greater amount propelled business degrees like a mba will discover certain ranges simpler will absorb, What’s more might get most extreme esteem from those span. However, Indeed undergraduates to non-technical fields or propelled high-school people seeking after internships will have the capacity on take after mossycup oak ideas Also get quality from the span. Finally, Significantly experts who bring required profound encounters over systems will inclined discover esteem in this course.<\/p>\n

Course Objective:\u00a0<\/strong><\/p>\n

1.\u00a0\u00a0\u00a0\u00a0 Understanding of real life cases about data<\/p>\n

2.\u00a0\u00a0\u00a0\u00a0 Understanding of real life data related problems<\/p>\n

3.\u00a0\u00a0\u00a0\u00a0 Understanding of data analysis methodologies<\/p>\n

4.\u00a0\u00a0\u00a0\u00a0 Understanding of some basic data operations like: preprocessing, transformation or manipulation<\/p>\n

5.\u00a0\u00a0\u00a0\u00a0 Understanding of new technologies like bigdata, nosql, cloud computing<\/p>\n

6.\u00a0\u00a0\u00a0\u00a0 Ability to use some trending software in the industry<\/p>\n

7.\u00a0\u00a0\u00a0\u00a0 Introduction to data related problems and their applications<\/p>\n

Method:<\/strong><\/p>\n

List of course software:<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Excel,<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 KNIME,<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 RapidMiner<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 MS-SQL, SSAS, SSIS<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Oracle Database, ODI, BI<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Apache Cassandra<\/p>\n

This course is following hands on experience in all the steps. So attendance with laptop computers is necessary. Also the software list above, will be provided during the course and the list is subject to updates.<\/p>\n

Grading<\/strong><\/p>\n

 <\/p>\n

Reading, Attendence and Discussions:\u00a030%<\/p>\n

Homeworks:\u00a030%<\/p>\n

Project:\u00a040%<\/p>\n

Course Content:<\/strong><\/p>\n\n\n\n\n
Week 1: Introduction to Data, Problems and Real World Examples:<\/strong><\/p>\n

Some useful information:<\/p>\n

DIKW Pyramid:\u00a0DIKW pyramid – Wikipedia<\/a><\/p>\n

CRISP-DM:\u00a0Cross-industry standard process for data mining – Wikipedia<\/a><\/p>\n

Slides from first week:week1<\/a><\/td>\n<\/tr>\n

Week 2: Introduction to Descriptive Analytics<\/strong><\/p>\n

Splitting data into sets : Training, Test and Validation Sets<\/a><\/p>\n

First Problem Type: Classification<\/a><\/p>\n

Slides from second week: week 2<\/a><\/p>\n

Homework #1 (Due Date: Nov. 23, 2017) :<\/strong> Download the data set of customers (click to download<\/a>). In the data set you can see, each record is holding the salary and age of the customer and their action in the store (buy: they buy a product, notbuy: they don’t buy any product). Create your own Knime data flow and predict the salary of people below:<\/p>\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Salary<\/td>\nAge<\/td>\n<\/tr>\n
<\/td>\n51<\/td>\n<\/tr>\n
<\/td>\n22<\/td>\n<\/tr>\n
<\/td>\n33<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Write a brief explanation for your submission (which algorithm did you use, what are the results you have achieved and how)<\/td>\n<\/tr>\n

Analytical Problems and Analysis<\/td>\n<\/tr>\n
Business Model, conceptualization and frameworks<\/td>\n<\/tr>\n
Information \u2013 Action Value Chain<\/td>\n<\/tr>\n
Data Capturing and data sources: Thinking in Data<\/td>\n<\/tr>\n
Analytical Technologies: Data Storage<\/td>\n<\/tr>\n
Analytical Technologies: Big Data, Cloud and Evolution of Web<\/td>\n<\/tr>\n
Analytical Technologies: Relational Databases<\/td>\n<\/tr>\n
Analytical Technologies: Virtualization, In Memory and NoSQL<\/td>\n<\/tr>\n
Analytical Technologies: Introduction to SQL: Simple Queries<\/td>\n<\/tr>\n
Analytical Technologies: SQL \u2013 2: Multiple Tables, Sub Queries<\/td>\n<\/tr>\n
Data Mining and Data Science Basics 1: Classification Problems<\/td>\n<\/tr>\n
Data Mining and Data Science Basics 2: Regression and Prediction<\/td>\n<\/tr>\n
Understanding Error<\/td>\n<\/tr>\n
Business Intelligence Tools and Applications<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0Knime WorkFlows:<\/strong><\/p>\n

Ensemble Fusion Workflow (click to download data and knime workflow)<\/a><\/p>\n

 <\/p>\n

Announcement about Projects<\/p>\n

presentations are at 22 December<\/p>\n

    \n
  1. Project proposal will include : Project idea and the data briefly and if you want to go within a group or not and if it is a group than provide the names and ids of group members (2 paragraphs), will be sent to the course email until next week 1st<\/sup>of december.<\/li>\n
  2. Deliverables (21st<\/sup>of December) : Knime workflow, report (including your approach and solution together with project definition and data description), presentation<\/li>\n
  3. Presentations will be about 5 \u2013 10 minutes (22nd<\/sup>of December): Explain your problem, describe your data set and demonstration of your solution and benefits , from the business approach. (in minimum 3 slides and a knime workflow (of course working)).<\/li>\n
  4. businessA@sadievrenseker.com<\/li>\n
  5. <\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    \u0130stanbul City University Course Name: Business Analytics Course Code: MBA 548 Language of Course: English Credit: 3 Course Coordinator \/ Instructor: \u015eadi Evren \u015eEKER Contact: businessA@sadievrenseker.com Schedule: Sat 13.00 – 16.00 Course Description: \u00a0This course is an introduction level course to data analsis, specialized on business processes and real life cases. This course will uncover you of the information analytics … Continue Reading →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2127","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/sadievrenseker.com\/index.php?rest_route=\/wp\/v2\/posts\/2127","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sadievrenseker.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sadievrenseker.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sadievrenseker.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sadievrenseker.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2127"}],"version-history":[{"count":4,"href":"https:\/\/sadievrenseker.com\/index.php?rest_route=\/wp\/v2\/posts\/2127\/revisions"}],"predecessor-version":[{"id":2144,"href":"https:\/\/sadievrenseker.com\/index.php?rest_route=\/wp\/v2\/posts\/2127\/revisions\/2144"}],"wp:attachment":[{"href":"https:\/\/sadievrenseker.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2127"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sadievrenseker.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2127"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sadievrenseker.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2127"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}