197 pages: Charts & graphs; 23 cm.
Chapter 1: Introduction to Data Analytics
Difference Between Data Analytics and Data Analysis -- Necessary Skills for Becoming a Data Scientist -- Python Libraries for Data Analysis
Chapter 2: About NumPy Arrays and Vectorized Computation
A fundamental random walk
Chapter 3: An introduction to Pandas by using Data Analytics
Chapter 4: Data Visualization
Class-wise colored Scatter Plot -- Scatter Plot -- Histogram
Chapter 5: Data Analysis applications and interacting with Databases
Chapter 6: Predictive Modelling
Loading A Dataset -- Transforming And Preparing Data -- Understanding Data -- Selecting Your Variables Properly -- Career Applications -- Most Common Work Problems
Although Python is more known as a programming languge, it has become a consistently popular tool for data analytics. In the recent years, several libraries have reached maturity thereby permitting Stata and R users to take advantage of the flexibility, performance, and beauty of Python without having to sacrifice the functionalities gathered by the older programs over the years. In this book, we will take a look at introducing the social science and data analysis applications of Python. This book is particularly tailored for those users that have little or no programming experience of note. It will be especially useful for these programmers who wish to get things done and have a lot of experience in programs such as Stata and R. The greatest reason for learning Python also happens to be the hardest to explain to someone who is just beginning his work in Python. Python is superbly designed in terms of structure and syntax; it is an intuitive; however, very powerful general-purpose programming language. Python's main advantage lies in how easy it is. For these kinds of things, you need an easy langauge. A harder one will generally take quite a large toll on your ability to program and analyze data.
Python data analytics : the in depth beginner's guide