İstanbul City University
Course Name: Business Data Analytics
Course Code: MBA 556
Language of Course: English
Credit: 3
Course Coordinator / Instructor: Şadi Evren ŞEKER
Contact: businessDA@sadievrenseker.com
Schedule: Thursday 19.00 – 22.00
Course Description: This 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 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’ll 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.
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’s 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.
Course Objective:
1. Understanding of real life cases about data
2. Understanding of real life data related problems
3. Understanding of data analysis methodologies
4. Understanding of some basic data operations like: preprocessing, transformation or manipulation
5. Understanding of new technologies like bigdata, nosql, cloud computing
6. Ability to use some trending software in the industry
7. Introduction to data related problems and their applications
Method:
List of course software:
· Excel,
· KNIME,
· RapidMiner
· MS-SQL, SSAS, SSIS
· Oracle Database, ODI, BI
· Apache Cassandra
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.
Grading
Reading, Attendence and Discussions: 30%
Homeworks: 30%
Project: 40%
Course Content:
| Week 1: Introduction to Data, Problems and Real World Examples:
Some useful information: DIKW Pyramid: DIKW pyramid – Wikipedia CRISP-DM: Cross-industry standard process for data mining – Wikipedia Slides from first week:week1 |
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| Week 2: Introduction to Descriptive Analytics
Splitting data into sets : Training, Test and Validation Sets First Problem Type: Classification Slides from second week: week 2 Homework #1 (Due Date: Nov. 23, 2017) : Download the data set of customers (click to download). 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 Rapid Miner data flow and decide if the below customers buy or not:
Write a brief explanation for your submission (which algorithm did you use, what are the results you have achieved and how) |
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| Analytical Problems and Analysis | ||||||||
| Business Model, conceptualization and frameworks | ||||||||
| Information – Action Value Chain | ||||||||
| Data Capturing and data sources: Thinking in Data | ||||||||
| Analytical Technologies: Data Storage | ||||||||
| Analytical Technologies: Big Data, Cloud and Evolution of Web | ||||||||
| Analytical Technologies: Relational Databases | ||||||||
| Analytical Technologies: Virtualization, In Memory and NoSQL | ||||||||
| Analytical Technologies: Introduction to SQL: Simple Queries | ||||||||
| Analytical Technologies: SQL – 2: Multiple Tables, Sub Queries | ||||||||
| Data Mining and Data Science Basics 1: Classification Problems | ||||||||
| Data Mining and Data Science Basics 2: Regression and Prediction | ||||||||
| Understanding Error | ||||||||
| Business Intelligence Tools and Applications |
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