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.
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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