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Index
Introduction
Chapter One: Introduction to Data Science
Introduction to Data Science
Why Use Data Science?
Who is a Data Scientist?
Differences Between Data Science and Business Intelligence
Lifecycle
Chapter Two: Pros and Cons of Data Science
Pros
Cons
Chapter Three: Statistics for Data Science
Descriptive Statistics
Inferential Statistics
Chapter Four: Skills Required for Data Science
Technical Skills
Non-Technical Skills
Chapter Five: Tools Required for Data Science
R Programming Language
Python Programming Language
Hadoop
Structured Query Language (SQL)
SAS
Apache Spark
D3.js
Chapter Six: Application of Data Science
Risk and Fraud Detection
Healthcare
Genetics and Genomics
Drug Development
Internet Search
Website Recommendations
Advanced Image Recognition
Virtual Assistance
Speech Recognition
Planning Routes for Airplanes
Gaming
Augmented Reality
Chapter Seven: Algorithms and Statistics
Naïve Bayes
Linear Regression
Logistic Regression
Neural Networks
Applying Math to Data Science Models
Chapter Eight: Data Science in a Sector
Communicate the Insights Captured Through Data
Leverage Cloud-Based Solutions
Chapter Nine: Data Cleaning Using R
Step One: Exploratory Analysis of Data
Step Two: Visual Exploratory Analysis
Step Three: Correct Errors
Chapter Ten: Develop a Predictive Model Using R
Chapter Eleven: Create Data Visualization to Communicate the Insight
Chapter Twelve: Document Your Insights from the Data
Conclusion
References
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