I. Ozkan, PhD 
 Professor 
 MIS 
 Cankaya
University
 
iozkan@cankaya.edu.tr
  
Fall 2023
PRELIMINARY OPEN DISCUSSION
 
“Whether you’re seeking an entry-level or leadership role, it’s increasingly apparent that to be successful in today’s job market, it is critical that you are able to analyze data and communicate the findings in a way that is easily understood.”
“Data analytics also gives companies the power to make faster, better-informed business decisions—and avoid spending money on ineffective strategies, inefficient operations, misguided marketing campaigns, or unproven concepts for new products and services.”
“Businesses can also use data to inform their strategies and drive targeted marketing campaigns to help ensure promotions engage the right audiences.”
“Another major benefit to data analytics is the ability to use insights to increase operational efficiencies. By collecting large amounts of customer data and feedback, businesses can deduce meaningful patterns to optimize their products and services.”
“When it comes to innovation, data analytics allows businesses to understand their current target audience, anticipate and identify product or service gaps, and develop new offerings to meet these needs.”
Try following for data analytics reosureces:
Google: “Data Analytics books”
Google: “Data Analytics using R book”
Google: “Data Analytics with R book” etc.
Must Visit Online Books Web Site
For Linear Models.
Introduction to
Econometrics with R
Using R for Introductory Econometrics, pdf
Trees, Linear Models, Non-Linear Models
Applied Predictive
Modeling By Max Kuhn and Kjell Johnson
Others
Pattern
Recognition and Machine Learning, Bishop, Christopher
 …
In-depth introduction to Statistical Learning - Video Lectures
 … Try Google Some Keywords
An Extra for R:
UCLA Institute for Digital Research and Education
Introduction: Why and What?
R
Data - Model and Analysis
A short discussion about causality
Decision Trees
K-Nearest Neighbors Algorithm
More Similarity Measures (What is Neighbor)
Regression: An Intro
Clustering
- Support Vector Machines (This part will be left to the end of the
course: If time permits)
Getting started with Data
Causality
Learning: Supervised, Unsupervised (and ?Reinforcement)
Scalar/Nominal/Ordinal/Time
Series/Cross-Sectional Data
Data and Models
Linear Models
Black-Box Models
Performance Measures
Decision Trees
Entropy, Information Gain
Nearest Neighbors
[Some] Selected Similarity Measures