Book Image

Python: End-to-end Data Analysis

By : Ivan Idris, Luiz Felipe Martins, Martin Czygan, Phuong Vo.T.H, Magnus Vilhelm Persson
Book Image

Python: End-to-end Data Analysis

By: Ivan Idris, Luiz Felipe Martins, Martin Czygan, Phuong Vo.T.H, Magnus Vilhelm Persson

Overview of this book

Data analysis is the process of applying logical and analytical reasoning to study each component of data present in the system. Python is a multi-domain, high-level, programming language that offers a range of tools and libraries suitable for all purposes, it has slowly evolved as one of the primary languages for data science. Have you ever imagined becoming an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? If yes, look no further, this is the course you need! In this course, we will get you started with Python data analysis by introducing the basics of data analysis and supported Python libraries such as matplotlib, NumPy, and pandas. Create visualizations by choosing color maps, different shapes, sizes, and palettes then delve into statistical data analysis using distribution algorithms and correlations. You’ll then find your way around different data and numerical problems, get to grips with Spark and HDFS, and set up migration scripts for web mining. You’ll be able to quickly and accurately perform hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. Finally, you will delve into advanced techniques such as performing regression, quantifying cause and effect using Bayesian methods, and discovering how to use Python’s tools for supervised machine learning. The course provides you with highly practical content explaining data analysis with Python, from the following Packt books: 1. Getting Started with Python Data Analysis. 2. Python Data Analysis Cookbook. 3. Mastering Python Data Analysis. By the end of this course, you will have all the knowledge you need to analyze your data with varying complexity levels, and turn it into actionable insights.
Table of Contents (6 chapters)

Data is raw information that can exist in any form, usable or not. We can easily get data everywhere in our lives; for example, the price of gold on the day of writing was $ 1.158 per ounce. This does not have any meaning, except describing the price of gold. This also shows that data is useful based on context.

With the relational data connection, information appears and allows us to expand our knowledge beyond the range of our senses. When we possess gold price data gathered over time, one piece of information we might have is that the price has continuously risen from $1.152 to $1.158 over three days. This could be used by someone who tracks gold prices.

Knowledge helps people to create value in their lives and work. This value is based on information that is organized, synthesized, or summarized to enhance comprehension, awareness, or understanding. It represents a state or potential for action and decisions. When the price of gold continuously increases for three days, it will likely decrease on the next day; this is useful knowledge.

The following figure illustrates the steps from data to knowledge; we call this process, the data analysis process and we will introduce it in the next section:

Introducing Data Analysis and Libraries

In this chapter, we will cover the following topics:

Data is getting bigger and more diverse every day. Therefore, analyzing and processing data to advance human knowledge or to create value is a big challenge. To tackle these challenges, you will need domain knowledge and a variety of skills, drawing from areas such as computer science, artificial intelligence (AI) and machine learning (ML), statistics and mathematics, and knowledge domain, as shown in the following figure:

Data analysis and processing

Let's go through data analysis and its domain knowledge:

Data analysis is a process composed of the following steps:

There are numerous data analysis libraries that help us to process and analyze data. They use different programming languages, and have different advantages and disadvantages of solving various data analysis problems. Now, we will introduce some common libraries that may be useful for you. They should give you an overview of the libraries in the field. However, the rest of this book focuses on Python-based libraries.

Some of the libraries that use the Java language for data analysis are as follows:

Here are a few libraries that are implemented in C++:

Other libraries for data processing and analysis are as follows:

Here, I could not list all libraries for data analysis. However, I think the above libraries are enough to take a lot of your time to learn and build data analysis applications. I hope you will enjoy them after reading this book.

Python is a multi-platform, general-purpose programming language that can run on Windows, Linux/Unix, and Mac OS X, and has been ported to Java and .NET virtual machines as well. It has a powerful standard library. In addition, it has many libraries for data analysis: Pylearn2, Hebel, Pybrain, Pattern, MontePython, and MILK. In this book, we will cover some common Python data analysis libraries such as Numpy, Pandas, Matplotlib, PyMongo, and scikit-learn. Now, to help you get started, I will briefly present an overview of each library for those who are less familiar with the scientific Python stack.


One of

Pandas is a

Matplotlib PyMongo

MongoDB The scikit-learn library