Topics in Data Science with Practical Examples

193 Pages·2018·15.7 MB·English

by  Abdolreza Abhari

Topics in Data Science with Practical
Examples
Topics in Data Science with Practical
Examples
Abdolreza Abhari
Copyright © 2018 Abdolreza Abhari
All rights reserved.
ISBN-13: 978-1727124842
ISBN-10: 1727124847
Acknowledgment
I
collected the material of this book after several years of teaching
data science course in the university for graduate and advanced
undergraduate students. Here I want to thank the following people
for their help in preparing the different chapters of this book:
Shahin Rahbari Asl
Maryam Gholizadeh-Ansari
Sukhjinder Kaur Gill
Maninder Singh Raniyal
Nicholas Buhagiar
Swapna Trafder
Jaynam Bhojak
Abhishek Gupta
Farisa Hossain
Ronak Maru
Dhrumil Patel
Hassan Jamil
If you want to use the softcopy of the practical examples or if you
have comments, corrections, suggestions, or any concerns, please
contact the author at:
Dr. Abdolreza Abhari
Professor, Department of Computer Science
Ryerson University
Toronto, Ontario, Canada M5B 2K3
www.scs.ryerson.ca/~aabhari
[email protected]
[email protected]
Table of Contents
Chapter 1 Data Management
Chapter 2 Data Mining
Chapter 3 Massive Data Sets, Hadoop, and MapReduce
Chapter 4 Regression Analysis
Chapter 5 Neural Networks
Chapter 6 Machine Learning
Chapter 7 Recurrent Neural Networks
Chapter 8 Text Processing (Natural Language Processing)
Chapter 9 Recommendation Systems and Netflix Challenge
Chapter 10 Analyzing Social Graphs
CHAPTER 1
Data Management
Introduction to Database Management System
What Is Data Science?
D
ata Science deals with data collection and its management on
a large scale. For example, data analytics—which is statistical
modeling and machine learning of the collected data—data
visualization, and representation of results are all different parts of
data science. The applications of data science can be in social
media analysis, web mining, marketing, information retrieval, text
analysis, and natural language processing. Data science intersects
with following areas (figure 1.1):
Figure 1.1. Intersection of data science with other areas
The central point of data science is dealing with data sets. A data
set is a combination of discrete entities of similar data that may be
used individually or in combination or be managed as a whole. Data
can be structured (such as database tables), unstructured (like a
document), or even be in form of social graphs showing different
stages of adding users to a Facebook fan web page as shown in
figure 1.2.
Figure 1.2. Example of social data graphs: Evolution of Facebook web
page fans in 200 days.
Source: Illustration from Pakzad, F., and A Abhari. “Characterization of user
networks in Facebook.” Proceedings of the 2010 Spring Simulation
Multiconference.
Data Modeling: Entity-Relationship Model
In this chapter we focus on structured data or tables that will be used
in relational databases. To make the database conceptual schema,
all tables of the database should be designed. This step is called
logical design, and for that purpose we use the entity-relationship
(ER) model that was introduced by Peter Chen in 1976. ER model
consists of entities, relationships, and attributes. There is no single
generally accepted notation for ER model. Here we use the simplest
one. You may also find symbols different from the ones we use.
Entity is a “thing in the real world with an independent existence.”
Two types of entities are strong/regular entity (simply called entity)
and weak entity. Strong entity types called owner or dominant entity
types exist on their own. Weak entity types are called dependent or
subordinate entity types. The existence of weak entity depends on
the existence of another strong entity. For example, the following
diagram shows a weak entity called dependent and a strong entity
called employee. Every dependent must be associated with an
employee.
If an employee is deleted from the database, the dependent must
also be deleted. We can delete a dependent without affecting
employee. A weak entity type is indicated by double outlined box:
A relationship associates entities with one another. The degree of
a relationship type is the number of participating entity types.
Examples of relationships can be as follows: