The only thing you need to know about Python is this! The Python programming language has many framework and structural features for Web development, data science, crawlers, machine learning, automated operations and testing, etc.

Python may not be the ideal choice for web application development, but it is undeniable that Python is also being used by many organizations to evaluate large data sets “dataset”, to visualize data, to analyze data, or to prototype.

So in data science, Python is gaining traction among Internet developers.

The Big Bad Wolf is here to talk about Python’s role and power in data science.

Many of you may be unfamiliar with the term ‘data science’, but what exactly is’ data science ‘? How does it differ from existing disciplines such as’ information science ‘, ‘statistics’ and’ machine learning ‘?

The Big Bad Wolf, for a brief analysis, is literally the process of taking scientific data that already exists on the Internet and in our lives and making it available to us.

As an emerging discipline in recent years, data science mainly relies on two factors: one is the universality and diversity of data; The second is the commonness of data research. All walks of life in modern society are full of data. These data are of various types, not only including traditional structured data, such as amount and quantity, etc. It also includes web pages, text, images, videos, voice and other unstructured data.

Analysis of data is essentially solving inverse problems, and often inverse problems of random models, so many people might ask what is an ‘inverse problem’?

Simply put, a reverse question is a question that you can solve in the normal order, and now it’s a rhetorical question, and the first question that you solve in the normal order, that’s a reverse question. Therefore, there are many commonalities and similarities in the research of data science.

For example, both natural language processing and biomolecular models use hidden Markov processes and dynamic programming methods. The fundamental reason is that they deal with one-dimensional random signals. For example, the regularization method used in image processing and statistical learning is also the most commonly used mathematical model to deal with inverse problems.

As for data science, it mainly includes two aspects: simply speaking, it is to study science with data method and study data with scientific method.

The former includes bioinformatics, astroinformatics, digital earth and other fields; The latter includes statistics, machine learning, data mining, databases and other fields. These disciplines are an important part of data science, and only by integrating them organically can they form a complete picture of the whole data science.

Since you study data science, you should have a deep understanding of what it contains:

In the basic stack of data science, Python, statistical analysis, machine learning, and more.

The detailed illustration is as follows:

On the Internet, each language or domain has its own development environment, and for data science, the most convenient and frequently used environment is “Anaconda.”

“Anaconda” is a free open source distribution of Python and R for computational science (data science, machine learning, big data processing, and predictive analytics). Most importantly, Anaconda simplifies package management and deployment, with over 1,400 data science packages for Windows, Linux, and MacOS.

Its advantage is that it comes with Python and a number of third party libraries for data science, saving time and effort by installing all dependencies in one step.

In the data science structure diagram above, there are many specialized data science tool libraries, such as; NnumPy, Pandas, matplotlib, SciPy, sciKit-learn, etc. So the Wolf gave a brief introduction to these tool libraries:

NnumPy: A third party library for scientific computation based on Python that provides solutions for matrices, linear algebra, Fourier transforms, and more.

Pandas; Third party library for data analysis, data modeling, data visualization.

Matplotlib; Matlab third party library with Python, to draw some high quality mathematical two-dimensional graphics.

SciPy; SciPy is an open source Python algorithm library and math toolkit. The modules include optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal processing and image processing, solving ordinary differential equations, and other calculations commonly used in science and engineering.

Scikit – learn; Machine learning third-party library that implements many well-known machine learning algorithms.

And the big Bad Wolf has summarized the above several libraries of the official introduction document (translation), from the most basic about the tool library of the official introduction document to learn, it is a better choice. You can get the document link by replying “link” on wechat official account.

NumPy Quick Start

Pandas Quick Start

Matplotlib Tutorial matplotlib tutorial

SciPy Tutorial

Scikit-learn scikit-learn (SKlearn)

And if you want a quick tutorial on how to use them, check out the free Python Data Analysis – Basic Techniques course on MOOC. This course introduces you not only to the installation of Anaconda, but also to the core usage of the above libraries. Suitable for quick understanding and learning of data science and data analysis.

For more detailed methods and highlights of the library, you can read the authoritative and readable book, Data Analysis with Python, 2nd Edition, by the authors of the Library.

For the principles and theoretical knowledge of data science related technologies, you can read the book “Introduction to Data Science”, which is suitable for learning and understanding.

For a more in-depth understanding of Data Science, you can also refer to Comprehensive Learning Path — Data Science in Python.

Feel good remember to pay attention to share, big bad Wolf look forward to progress with you!

At the same time, you can also follow my wechat official account “Grey Wolf Hole Owner” for more Python project development technology sharing and Internet information!