Data science from scratch: the #1 data science guide for everything a data scientist needs to know: Python, linear algebra, statistics, coding, applications, neural networks, and decision trees

✍ Scribed by Steven Cooper

• Publisher: Steven Cooper
• Year: 2018
• Tongue: English
• Leaves: 157
• Edition: 1
• Category: Library

✦ Synopsis

★☆If you are looking to start a new career that is in high demand, then you need to continue reading!★☆​​​​​​​

Data scientists are changing the way big data is used in different institutions.
Big data is everywhere, but without the right person to interpret it, it means nothing.
So where do business find these people to help change their business?
You could be that person!
It has become a universal truth that businesses are full of data.
With the use of big data, the US healthcare could reduce their health-care spending by $300 billion to $450 billion.
It can easily be seen that the value of big data lies in the analysis and processing of that data, and that's where data science comes in.

✦ Table of Contents

Preface
Introduction
Data Science and its Importance
What is it Exactly?
Why It Matters
What You Need
The Advantages to Data Science
Data Science and Big Data
Key Difference Between Data Science and Big Data
Data Scientists
The Process of Data Science
Responsibilities of a Data Scientist
Qualifications of Data Scientists
Would You Be a Good Data Scientist?
The Importance of Hacking
The Importance of Coding
Writing Production-Level Code
Python
SQL
R
SAS
Java
Scala
Julia
How to Work with Data
Data Cleaning and Munging
Data Manipulation
Data Rescaling
Python
Installing Python
Python Libraries and Data Structures
Conditional and Iteration Constructs
Python Libraries
Exploratory Analysis with Pandas
Creating a Predictive Model
Machine Learning and Analytics
Linear Algebra
Vectors
Matrices
Statistics
Discrete Vs. Continuous
Statistical Distributions
PDFs and CDFs
Testing Data Science Models and Accuracy Analysis
Some Algorithms and Theorems
Decision Trees
Neural Networks
Scalable Data Processing
Batch Processing Systems
Apache Hadoop
Stream Processing Systems
Apache Storm
Apache Samza
Hybrid Processing Systems
Apache Spark
Apache Flink
Data Science Applications
Conclusion
About the author
References
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