Do you want to learn Python programming and enter the world of data science and researching? Do you know that you can use Python programming language for developing mobile applications, video games and also for various scientific researching and data mining?
If your answer of these questions is “ YES! “, than this Python post will perfectly match to you and all you need to do is to keep reading…
This Python post is incredibly useful and really understandable step-by-step guide for those who want to start with programming using the Python for their projects and make their researching activities much easier. This guide will introduce you to the basics of this programming language and you will have the chance to learn how to use and code in Python with matched libraries, easily and effectively. So, let's dive into this data science programming language...
1. Starting with Python
Python is a widely used general-purpose, high level programming language. It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation. It was mainly developed with emphasis on code readability and its syntax form allows programmers to express concepts in fewer lines of code. That save the programmer’s time and keep their ideas fresh and tidy.
Python is a programming language that lets you work quickly and integrate systems more efficiently. There are two major Python versions- Python 2 and Python 3. Both are quite different.
Before starting with Python programming, you need to pick an interpreter to interpret and run the programs properly. There are many interpreters available for free to run Python scripts like Integrated Development Environment or in short IDLE, which is installed when you install the Python software on your computer.
If you are a Linux operating system user, Python programming language and its interpreter comes bundled with the operating system package and its distribution.
There are no separate compilation and execution steps in Python programming, like C and C++.
This programming language is directly run the program from the source code. Python converts the source code into an intermediate form called “bytecodes” which is then translated into native language of specific computer to run it. You don’t need to initialize the libraries before you start with coding because they are managed and linked by themselves.
Python programs can be developed and executed on multiple operating system platforms like Linux, Windows, Mac, Solaris and so on. It is free and open source programming language and that means that you can manage and organized for your needs and wishes.
2. Python usage in data science
In short, understanding Python is one of the valuable skills needed for a data science career. Though it hasn’t always been, Python is the programming language of choice for data science.
Data science experts expect this trend to continue with increasing development in the Python ecosystem. While your journey to learn Python programming may be just beginning, it is nice to know that employment opportunities are abundant and growing fast as well. According to today’s marketplace analytics, the average salary for a Data Scientist is $127,900.
So, the future is bright for data science, and Python is just one piece of the proverbial pie. Fortunately, learning Python and other programming fundamentals is as attainable as ever. If you apply yourself and dedicate meaningful time to learning Python, you have the potential to not only pick up a new skill, but potentially bring your career to a new level.
This first step is where you will learn Python programming basics. You will also want an introduction to data science. One of the important tools you should start using early in your journey is Jupyter Notebook, which comes prepackaged with Python libraries to help you learn these two things.
Some programming languages live in the heart of data science. Python is one of those languages. It is an integral ingredient for Data Science and vice versa. And actually, it would take prominently long to explain why.
Python provides great functionality to deal with mathematics, statistics and scientific function. When it comes to data science application, it provides extensive libraries to deal with. Not to mention it is open-source, interpreted, high-level tool.
Most importantly, Python is widely used in the scientific and research communities because of its ease of use and its simple syntax makes it easy to adapt for people who even do not have an engineering background.
3. Libraries in Python
Python language is already assisting developers in creating PC games, mobile apps and other enterprise applications. Python with more than 137,000 libraries helps in various ways. In this data science world, where consumers demand relevant information in their buying journey, companies also require data scientists to avail valuable insights by processing massive data sets. So, here are the most popular Python libraries...
1. NumPy
NumPy is the first choice among developers and data scientists who are aware of the technologies which are dealing with data-oriented stuff. It is a Python package available for performing scientific computations. It is registered under the BSD license. Through NumPy, you can leverage n-dimensional array objects, C, C++, Fortran program based integration tools, functions for performing complex mathematical operations like Fourier transformation, linear algebra, random number etc.
One can also use NumPy as a multi-dimensional container to treat generic data. Thus, you can effectively integrate your database by choosing varieties of operations to perform with. NumPy is installed under the Tensor Flow and other complex machine learning platforms empowering their operations internally.
2. Theano
Theano is another useful Python library assists data scientists in performing large multi-dimensional arrays related computing operations. It is more like TensorFlow but the only difference is, it is not that efficient.
It is getting used for distributed and parallel computing based tasks. Through it, you can optimize, express or evaluate you array-enabled mathematical operations. It is tightly coupled with NumPy powered by implemented numpy.ndarray function.
Due to GPU based infrastructure, it holds the capability to process operations in faster ways than CPU. It stands fit for speed and stability optimizations delivering us the expected outcomes.
3. Keras
Keras is one of the most powerful Python libraries which allow high-level neural networks APIs for integration. Theses APIs execute over the top of TensorFlow, Theano and CNTK. Keras was created for reducing challenges faced in complex researches allowing them to compute faster. For one who is using deep learning libraries for their work, Keras is the best option.
It allows fast prototyping, supports recurrent and convolution networks individually and also their combination, execution over GPU and CPU. Keras provides a user-friendly environment reducing your effort in cognitive load with simple APIs giving us the required results. Due to its modular nature, one can use varieties of modules from neural layers, optimizers, activation functions etc.., for developing a new model.
It is an open source library written in Python. For data scientists having trouble adding new modules, Keras is a good option where they can simply add a new module as classes and functions.
4. Pandas
Pandas referred as a Python Data Analysis Library. This library is another open source Python library for availing high-performance data structures and analysis tools. It is developed over the Numpy package. It contains DataFrame as its main data structure.
With DataFrame you can store and manage data from tables by performing manipulation over rows and columns. Methods like square bracket notations reduce person’s effort in data analysis tasks like square bracket notations. Here, you will get tools for accessing data in-memory data structures performing read and write tasks even if they are in multiple formats such as CSV, SQL, HDFS or excel etc.
5. SciKit-Learn
Scikit-Learn is a simple tool for data analysis and mining-related tasks. It is open-source and licensed under the BSD. Anyone can access or reuse it in various contexts. SciKit is developed over the Numpy, Scipy, and Matplotlib. It is being used for classification, regression and clustering o manage spam, image recognition, drug response, stock pricing, customer segmentation etc. It also allows dimensionality reduction, model selection and pre-processing.
6. Matplotlib
This 2D plotting library of Python is very famous among data scientists for designing varieties of figures in multiple formats which is compatible across their respected platforms. One can easily use it in their Python code, IPython shells or Jupyter notebook, application servers. With Matplotlib, you can make histograms, plots, bar charts, scatter plots etc.
4. Python data visualization
Scroll through the Python Package Index and you will find libraries for practically every data visualization need, from GazeParser for eye movement research to pastalog for real time visualizations of neural network training. And while many of these libraries are intensely focused on accomplishing a specific task, some can be used no matter what your field.
Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed.
Python offers multiple great graphing libraries that come packed with lots of different features. No matter if you want to create interactive, live or highly customized plots python has an excellent library for you.
Matplotlib is the O.G. of Python data visualization libraries. Despite being over a decade old, it's still the most widely used library for plotting in the Python community. Because Matplotlib was the first Python data visualization library, many other libraries are built on top of it or designed to work in tandem with it during analysis. Some libraries like pandas and Seaborn are “wrappers” over Matplotlib. They allow you to access a number of Matplotlib’s methods with less code.
5. Machine Learning & Artificial Intelligence
Machine learning gives computers the ability to learn without being explicitly programmed. It is a sub-field of computer science.
The idea came from work in artificial intelligence. Machine learning explores the study and construction of algorithms which can learn and make predictions on data. Such algorithms follow programmed instructions, but can also make predictions or decisions based on data.
Machine learning is done where designing and programming explicit algorithms cannot be done. Examples include spam filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), search engines and computer vision.
Machine learning is a field of computer science. It is also a type of Artificial Intelligence that enables the programmers to write programs in a more simple way. It focuses more on developing programs that teach computers to change when exposed to new data and to grow. Its goal is to understand and follow the methods by using algorithms to do that task automatically without any human assistance. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".
6. Summary
Don’t be overthinking anymore and make your needs and expectations a cute reality thanks to this awesome Python step-by-step guide for how to start with Python programming and help yourself in today’s data scientific world. Keep working hard for your goals and never give up. You are the only creator of your success. Remember that.
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