Python Data Science_ The Ultimate Guide on What You Need to Know to Work with Data Using Python.pdf

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Python Data Science
How to Work with Data with Python
Programming Language The
Ultimate Guide on What You Need
to Know to Work with Data Using
Python
BY
Oliver Soranson
Table of Contents
Introduction
Chapter 1: Why Python Works So Great for Data Science
The Basics of Python
What Is Data Science All About?
Chapter 2: The Basics of Python That Everyone Needs to Know
The Statements
The Comments
What are Classes?
The Operators
Assigning a Value to a Variable
The Control Flow
The Python Functions
Chapter 3: The NumPy Library and How It Can Help with Data Science
Chapter 4: Manipulating Data with the Pandas Library
Installing the Pandas Library
The Benefits of Using Pandas
Viewing and Inspecting the Data
Chapter 5: Collecting and Manipulating Data
Where Should I Collect Data From?
Unstructured vs. Structured Data
Collecting the Data
How Long Should I Collect Data For?
Chapter 6: Data Cleaning and Preparation
What Is Data Preparation?
Why Do I Need Data Preparation?
What Are the Steps for Data Preparation?
Handling the Missing Data
Chapter 7: What is Data Wrangling?
What Is Data Wrangling?
Data Wrangling with Pandas
Our Goals with Data Wrangling
The Key Steps with Data Wrangling
What to Expect with Data Wrangling?
Chapter 8: Taking Our Results and Plotting Them to Visualize What We
Learned
The History of Data Visualization
Why Is This Data Visualization So Important?
How is Data Visualization Being Used?
Laying the Groundwork for Data Visualization
Which Visual is the Right One for My Project?
Chapter 9: Data Aggregation and Group Operations
What Is Data Aggregation?
Chapter 10: What Is the Time Series?
Understanding the Time Series
Chapter 11: What Is Machine learning and How It Fits with Data
Science
What is Machine learning?
The Benefits of Machine learning
Supervised Machine learning
Unsupervised Machine learning
Reinforcement Machine learning
Chapter 12: Other Libraries That Can Help with Python
IPython
Jupyter
Scikit-Learn
TensorFlow
Chapter 13: Practical Examples of Python Data Science
K-Means Clustering
Neural Networks
Conclusion
Introduction
Congratulations on purchasing
Data Science Python
and thank you for doing
so. The following chapters will discuss all the steps that we need to use to
start our Data Science project and finally get some insights and good
information out of all that data we have been collecting. Even better, we are
going to take a look at how we can complete this project with the help of the
Python programming language!
To start this guidebook, we are going to take a look at some of the basics that
come with the Python language, and how it can work so well with the process
of Data Science. We will also add in some information about what Data
Science is all about, and how Python and Data Science can come together to
provide us with amazing results in the process. We can then spend some
more time on the Python language and what comes with it before moving on
to more about getting started with Data Science.
Next on the list is a look at some of the best libraries that we can use to help
handle our Data Science project. We will start out with a look at the NumPy
library, and the Pandas library, since these are the two most commonly used
programming libraries to help with the different parts of a Data Science
project. We can later expand out to some of the other options, like Jupyter
and TensorFlow as needed.
With this information under our belt and some of the Python libraries set up
and ready to go, it is time to take a look at some of the different parts of the
puzzle we can explore in the Data Science project. We will look at the basics
of collecting and preparing the data, working with data cleaning and
preparation, what is data wrangling, how to take all of our information and
plot it to make a visual, how to work with data aggregation and group
operations, and a look at what the time series is and how it relates back to our
work in Data Science.
To end this guidebook, we are going to take a look at a few other topics as
well, ones that will ensure that we get a full understanding of Data Science
and the steps that we need to take to make this process work. We will take an
in-depth look at Machine learning and how it fits in with Data Science, and
even explore some of the practical examples of Python Data Science at work
so we can finally see the results that we want.

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