 Название: 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
Автор: Steven Cooper
Издательство: Amazon Digital Services LLC
ASIN: B07FM4Z3FH
Год: 2018
Страниц: 168
Язык: английский
Формат: epub, mobi, pdf (conv)
Размер: 10.17 MB
The main goal of this book is to help people take the best actionable steps possible towards a career in data science. The need for data scientists is growing exponentially as the internet, and online services continue to expand.
This book will help you:
- Know more about the fundamental principles of data science and what you need to become a skilled data scientist.
- Have an elementary grasp of data science concepts and tools that will make this work easier to do.
- Have achieved a technical background in data science and appreciate its power.
This book is for those who are interested in data science. There are a lot of skills that a data scientist needs, such as coding, intellectual mindset, eagerness to make new discoveries, and much more.
It’s important that you are interested in this because you are obsessed with this kind of work. Your driving force should not be money. If it is, then this book is not for you.
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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