Authoring Books with R Markdown
Preface
0.1
What is this book? Why to read it?
0.2
Structure of the Book
0.3
What Can This Course Offer You?
0.4
Schedule
0.5
Notes
0.6
Convention
Acknowledgements
1
Introduction
1.1
What is Data Science?
1.1.1
Data science as Discovery of Data Insight
1.1.2
Data science as Development of Data Product
1.2
What is Data Scientist?
1.2.1
The Requisite Skill Set
1.2.2
How to Become a Data Scientist?
1.3
Process of Doing Data Science
Step 1: Understand the Problem - Define Objectives
Step 2: Undertand Data
Step 3: Data Preprocess
Step 4: Analyze Data
Step 5: Results Interpretation and Evaluation
Step 6: Data Report and Communication)
1.4
Tools used in Doing a Data Science Project
R
Python
SQL
Hadoop
Tableau
Weka
1.5
Applications of Data Science
Data Science in Healthcare
Data Science in E-commerce
Data Science in Manufacturing
Data Science as Conversational Agents
Data Science in Transport
Summary
Exercise 1
2
Get Your Tools Ready
2.1
Brief introductiuon about R and RStudio
2.1.1
Features of R Programming
2.1.2
R Scripts
2.1.3
R Graphical User Interface (RGui)
2.1.4
RStudio
2.2
Downlaod and Install R and RStudio
2.2.1
R Download and Installation
2.2.2
RStudio Download and Installation
2.2.3
Familiar with RStudio interface
2.3
Bootsup your RStudio
2.4
Instructions
Code
Tips
Actions
Exercise
Exercise 2
3
Understand Problem
3.1
Kaggle Competion
3.2
Titianic at Kaggel
3.3
The Titanic problem
The challenge
The data
Submission
3.4
Reflection
Exercises 3
4
Understand Data
4.1
Load data
4.2
Assess Data Quantity
4.3
Data Attributes Assessment
4.3.1
General Attributes Description
4.3.2
Actual Attributes Types Examination
4.3.3
Actual Data Attributes Value Examination
4.4
Data recods level assessment
Summary
Exercises 4
5
Data PreProcess
5.1
Dealt with Miss values and Errors (Age, )
5.2
Attributes selection (prediction power)
5.3
Attribute reengineering ( title from name, treval famail _ relatives, alone from ParCh and SibSb)
5.4
Assemble final datasets for modelling
6
Data Analysis
7
Result Interpretation
8
Data Report
Reference
8.1
R Markdown
8.2
Including Plots
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Do Data Science in 10 Hours
5.1
Dealt with Miss values and Errors (Age, )