The world of data science is huge and varied and a rich and diverse career path is available to anyone with a keen eye on attention to detail and a penchant for problem-solving. Mastering the art often starts with a deep understanding of spreadsheets and toe be able to build pivot tables and to produce charts from complex data is the starter tool for many a data analyst.
Big data is a term that arrived around 2005 and is a term coined to describe data that is so large in size that it is difficult to simply analyze with simple tools such as a spreadsheet, which is limited to the number of rows available. Tech giants such as Netflix, Facebook, Amazon, Uber and Oracle are collecting huge amounts of data and are hiring data engineers at a very fast rate. It is currently a very good time to enter the realm of data analysis.
One great place to start out on this journey is to read about the roles and skills involved in this field. There have been many publications produced in the realm of data science. The following are some of the best ones that we have found.
The Data Engineering cookbook by Andreas Kretz
How do I become a data engineer? It’s a question that perplexes many as the role is relatively new and unknown. This eBook by Andreas Kertz has interviews, case studies, podcasts and code examples. A complete package, enabling anyone as a starting point on the path to becoming a data engineer.
The best thing about this – it is free! It will introduce you to the world of data engineering without cost.
The Data Science Handbook by Carl Shan, Henry Wang, Willian Chen and Max Song
This data science handbook contains key interviews with 25 of the world’s top data scientists.
The book contains key insights and interviews with data scientists working on projects within Facebook, LinkedIn, Intuit and The New York Times. All companies with a busy data mining department.
The book also contains interviews with data science professionals from large startups such as Uber, Airbnb.
There are also sections with insights from industry leaders such as Kevin Novak and Riley Newman. These head the data science teams and Uber and Airbnb. Clare Corthell is a rising star in the data science realm, who crafted and developed her own open-source data science masters program.
Python Data Science Handbook by Jake Vanderplas
Python is one of the world’s most popular programming languages. This makes it heavily used in the world of data science, with REST API calls and data manipulation with JSON and XML into databases such as SQL or MySQL and NoSQL for big data.
Within this PDF you will learn :
- Python and Jupyter – provides computational environments for data analysis using Python
- NumPy – learn of the numpy ndarray for efficient data storage and manipulation of large data arrays within Python
- Pandas – learn of the features and the DataFrame for efficiently storing and manipulating labelled/columnar data in Python
- Matplotlib – learn of the capabilities of a flexible range of data visuals within Python
- Scikit-Learn – Learn of clean and efficient Python implementations of important machine learning algorithms for advanced capabilities with Python code
The sysadmin Handbook by Redgate
This book contains over fifty data science-related articles as an essential reference for any Systems Administrator. The realms of a System administrator and data science gets blurred in this very informative PDF.
R and Data Mining: Examples and Case Studies by Yanchang Zhao
This ebook assists researchers in the arena of data mining and analysts within the industry alike. This invaluable resource is used by many universities that run courses on data mining and its related subjects.
Social Media Mining: An Introduction by Reza Zafarani, Mohammad Ali Abbasi and Huan Liu
Social Media mining is renowned for integrating social media, social network analysis and data mining to provide a coherent and rich platform for students, data practitioners, researchers and project managers to get to grips with the basics of the potential of social media platforms with regards to data mining. This eBook brings forward the unique problems faced by social media companies in manipulating data. Introduces fundamental concepts, emerging issues and algorithms for network analysis and data analysis.
Statistical Learning with Sparsity: The Lasso and Generalizations by Trevor Hastie, Robert Tibshirani and Martin Wainwright
This eBook brings all of the major areas of statistical learning together. Within each topic, the authors provide a concise introduction to the basic problem, consider all conventional methodology, identifying each deficiency and result in a recommended method based on sparsity.
Initially, the discussions begin with regularized models which are based on equations. These are then followed by example applications, resulting in a bibliography section that details the historical development of the given method.
Convex Optimization 1st Edition by Stephen Boy
This eBook is not aimed at the data analyst beginner. The book is an advanced look at the concepts of Convex Optimization which is used by almost all Deep Learning and Machine learning algorithms. This is an advanced feature used by Data Scientists to produce complex predictions of future results based on data gathered in the past.
Deep Learning with Python by Francois Chollet
In this book, the author, Francois Chollet is a Google AI researcher and author and has created an alternative way to learn Deep Learning and dives deep into the techniques and concepts using Python to build solutions for complex problems faced within Data mining.