Python Tutorial: Data Science vs. Web Development

Python Tutorial: Data Science vs. Web Development

In this "Python Tutorial: Data Science vs. Web Development" to provide a comparison on the two completely different purposes of using Python language and help understand that it is not necessary to know Python as a web programming language for doing data science in Python.

Python programming has various frameworks and features to expand in web application development, graphical user interfaces, data analysis, data visualization, etc. Python programming language might not be an ideal choice for web application development, but is extensively used by many organizations for evaluating large datasets, for data visualization, for running data analysis or prototyping. Python programming language is gaining traction amongst users for data science whilst being outmoded as a web programming language. The idea of this blog post is to provide a comparison on the two completely different purposes of using Python language and help understand that it is not necessary to know Python as a web programming language for doing data science in Python.

Python for Data Science :

Organizations of all sizes and industries — from the top financial institutions to the smallest big data start-ups are using Python programming language to run their business.

Python language is among the popular data science programming languages not only with the top big data companies but also with the tech start up crowd. Python language ranks among the top 10 programming languages to learn in 2019.

Python language comes in the former category and is finding increased adoption in numerical computations, machine learning and several data science applications. Python language can do anything, excluding performance dependent and low level stuff. The best bet to use Python programming language is for data analysis and statistical computations. Learning Python programming for web development requires programmers to master various web frameworks like Django that can help the build websites whereas learning Python for data science requires data scientists to learn the usage of regular expressions, get working with the scientific libraries and master the data visualization concepts. With completely different purposes, programmers or professionals who are not knowledgeable about web programming concepts with Python language can easily go ahead and pursue data science in Python programming language without any difficulty.

Python is a 23-year-old powerful expressive dynamic programming language where a programmer can write the code once and execute it without using a separate compiler for the purpose. Python in web development supports various programming paradigms such as structured programming, functional programming and object oriented programming. Python language code can be easily embedded into various existing web application that require a programming interface. However, Python language is a preeminent choice for academic, research and scientific applications which need faster execution and precise mathematical calculations.

Python web programming requires programmers to learn about the various python web development frameworks, which can be intimidating because the documentation available for the python web development frameworks might be somewhat difficult to understand. However, it is undeniable that to develop a dynamic website or a web application using Python language, learning a web framework is essential.

Python Web Development Frameworks

There are several Python web application frameworks available for free like-

Django
Django is the python web development framework for perfectionists with deadlines. Python web development with django is best suited for developing database driven web applications with attractive features like automatic admin interface and a templating system. For web development projects that don’t require extensive features, Django may be an overkill because of its confusing file system and strict directory structure. Some companies that are using python web development with django are The New York Times, Instagram, and Pinterest.

Flask
It is a simple and lightweight solution for beginners who want to get started with developing single-page web applications. This framework does not support for validation, data abstraction layer and many other components that various other frameworks include. It is not a full stack framework and is used only in the development of small websites.

CherryPy
It emphasizes on Pythonic conventions so that programmers can build web applications just the way they would do it using object oriented Python programming. CherryPy is the base template for other popular full stack frameworks like TurboBears and Web2py.

There are so many other web frameworks like Pyramid, Bottle, and Pylons etc. but regardless of the fact, whichever web framework a python programmer uses, the challenge is that he/she needs to pay close attention to detailing on the tutorials and documentation.

Why Web Development with Python is an impractical choice?

Python programming language probably is an impractical choice for being chosen as a web programming language –

Python for web development requires non-standard and expensive hosting particularly when programmers use popular python web frameworks for building websites. With PHP language being so expedient for web programming, most of the users are not interested in investing in Python programming language for web development.

Python language for web development is not a commonly demanded skill unlike demand for other web development languages like PHP, Java or Ruby on Rails. Python for Data science is gaining traction and is the most sought after skill companies are looking for in data scientists, with its increased adoption in machine learning and various other data science applications.
Python for web development has come a long way but it does not have a steep learning curve as compared to other web programming languages like PHP.
Why Python for Data Science is the best fit?

Python programming is the core technology that powers big data, finance, statistics and number crunching with English like syntax. The recent growth of the rich Python data science ecosystem with multiple packages for Machine learning, natural language processing, data visualization, data exploration, data analysis and data mining is resulting in Pythonification of the data science community. Today, Python data science language has all the nuts and bolts for cleaning, transforming, processing and crunching big data. Python is the most in-demand skill for data scientist job role. A data scientist with python programming skills in New York earns an average salary of $180,000

Why data scientists love doing data science in Python language?

Data Scientists like to work in a programming environment that can quickly prototype by helping them jot down their ideas and models easily. They like to get their stuff done by analysing huge datasets to draw conclusions. Python programming is the most versatile and capable all-rounder for data science applications as it helps data scientists do all this productively by taking optimal minimal time for coding, debugging, executing and getting the results.

The real value of a great enterprise data scientist is to use various data visualizations that can help communicate the data patterns and predictions to various stakeholders of the business effectively, otherwise it is just a zero-sum game. Python has almost every aspect of scientific computing with high computational intensity which makes it a supreme choice for programming across different data science applications, as programmers can do all the development and analysis in one language. Python for data science links between various units of a business and provides a direct medium for data sharing and processing language.

  1. Python has a unified design philosophy that focuses on ease of use, readability and easy learning curve for data science.
  2. Python has high scalability and is much faster when compared to other languages like Stata, Matlab.
  3. There are more and more data visualization libraries and cool application programming interfaces being added for inclusion of graphics to depict the results of data analysis.
  4. Python has a large community with good number of data science or data analytics libraries like Sci-Kit learn, NumPy, Pandas, and Statsmodels, SciPy etc. which have rich functionality and have been tested extensively. Data analysis libraries in Python language are growing over time.
Python Programming for Number Crunching and Scientific Computing in Data Science

Data analysis and Python programming language go hand in hand. If you have taken a decision to learn Data Science in Python language, then the next question in your mind would be –What are the best data science in Python libraries that do most of the data analysis task? Here are top data analysis libraries in Python used by enterprise data scientists across the world-

NumPy
It is the foundation base for the higher level tools built in Python programming language. This library cannot be used for high level data analysis but in-depth understanding of array oriented computing in NumPy helps data scientists use the Pandas library effectively.

SciPy
SciPy is used for technical and scientific computing with various modules for integration, special functions, image processing, interpolation, linear algebra, optimizations, ODE solvers and various other tasks. This library is used to work with NumPy arrays with various efficient numerical routines.

Pandas
This is the best library for doing data munging as this library makes it easier to handle missing data, supports automatic data alignment, supports working with differently indexed data gathered from multiple data sources.

SciKit
This is a popular machine learning library with various regression, classification and clustering algorithms with support for gradient boosting, vector machines, naïve Bayes, and logistic regression. This library is designed to interoperate with NumPy and SciPy.

Matplotlib
It is a 2D plotting library with interactive features for zooming and panning for publication quality figures in different hard copy formats and in interactive environments across various platforms.

Machine Learning, Data Science and Deep Learning with Python

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks. Introducing Tensorflow, Using Tensorflow, Introducing Keras, Using Keras, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Learning Deep Learning, Machine Learning with Neural Networks, Deep Learning Tutorial with Python

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks

Explore the full course on Udemy (special discount included in the link): http://learnstartup.net/p/BkS5nEmZg

In less than 3 hours, you can understand the theory behind modern artificial intelligence, and apply it with several hands-on examples. This is machine learning on steroids! Find out why everyone’s so excited about it and how it really works – and what modern AI can and cannot really do.

In this course, we will cover:
• Deep Learning Pre-requistes (gradient descent, autodiff, softmax)
• The History of Artificial Neural Networks
• Deep Learning in the Tensorflow Playground
• Deep Learning Details
• Introducing Tensorflow
• Using Tensorflow
• Introducing Keras
• Using Keras to Predict Political Parties
• Convolutional Neural Networks (CNNs)
• Using CNNs for Handwriting Recognition
• Recurrent Neural Networks (RNNs)
• Using a RNN for Sentiment Analysis
• The Ethics of Deep Learning
• Learning More about Deep Learning

At the end, you will have a final challenge to create your own deep learning / machine learning system to predict whether real mammogram results are benign or malignant, using your own artificial neural network you have learned to code from scratch with Python.

Separate the reality of modern AI from the hype – by learning about deep learning, well, deeply. You will need some familiarity with Python and linear algebra to follow along, but if you have that experience, you will find that neural networks are not as complicated as they sound. And how they actually work is quite elegant!

This is hands-on tutorial with real code you can download, study, and run yourself.

How to get started with Python for Deep Learning and Data Science

How to get started with Python for Deep Learning and Data Science

A step-by-step guide to setting up Python for Deep Learning and Data Science for a complete beginner

A step-by-step guide to setting up Python for Deep Learning and Data Science for a complete beginner

You can code your own Data Science or Deep Learning project in just a couple of lines of code these days. This is not an exaggeration; many programmers out there have done the hard work of writing tons of code for us to use, so that all we need to do is plug-and-play rather than write code from scratch.

You may have seen some of this code on Data Science / Deep Learning blog posts. Perhaps you might have thought: “Well, if it’s really that easy, then why don’t I try it out myself?”

If you’re a beginner to Python and you want to embark on this journey, then this post will guide you through your first steps. A common complaint I hear from complete beginners is that it’s pretty difficult to set up Python. How do we get everything started in the first place so that we can plug-and-play Data Science or Deep Learning code?

This post will guide you through in a step-by-step manner how to set up Python for your Data Science and Deep Learning projects. We will:

  • Set up Anaconda and Jupyter Notebook
  • Create Anaconda environments and install packages (code that others have written to make our lives tremendously easy) like tensorflow, keras, pandas, scikit-learn and matplotlib.

Once you’ve set up the above, you can build your first neural network to predict house prices in this tutorial here:

Build your first Neural Network to predict house prices with Keras

Setting up Anaconda and Jupyter Notebook

The main programming language we are going to use is called Python, which is the most common programming language used by Deep Learning practitioners.

The first step is to download Anaconda, which you can think of as a platform for you to use Python “out of the box”.

Visit this page: https://www.anaconda.com/distribution/ and scroll down to see this:

This tutorial is written specifically for Windows users, but the instructions for users of other Operating Systems are not all that different. Be sure to click on “Windows” as your Operating System (or whatever OS that you are on) to make sure that you are downloading the correct version.

This tutorial will be using Python 3, so click the green Download button under “Python 3.7 version”. A pop up should appear for you to click “Save” into whatever directory you wish.

Once it has finished downloading, just go through the setup step by step as follows:

Click Next

Click “I Agree”

Click Next

Choose a destination folder and click Next

Click Install with the default options, and wait for a few moments as Anaconda installs

Click Skip as we will not be using Microsoft VSCode in our tutorials

Click Finish, and the installation is done!

Once the installation is done, go to your Start Menu and you should see some newly installed software:

You should see this on your start menu

Click on Anaconda Navigator, which is a one-stop hub to navigate the apps we need. You should see a front page like this:

Anaconda Navigator Home Screen

Click on ‘Launch’ under Jupyter Notebook, which is the second panel on my screen above. Jupyter Notebook allows us to run Python code interactively on the web browser, and it’s where we will be writing most of our code.

A browser window should open up with your directory listing. I’m going to create a folder on my Desktop called “Intuitive Deep Learning Tutorial”. If you navigate to the folder, your browser should look something like this:

Navigating to a folder called Intuitive Deep Learning Tutorial on my Desktop

On the top right, click on New and select “Python 3”:

Click on New and select Python 3

A new browser window should pop up like this.

Browser window pop-up

Congratulations — you’ve created your first Jupyter notebook! Now it’s time to write some code. Jupyter notebooks allow us to write snippets of code and then run those snippets without running the full program. This helps us perhaps look at any intermediate output from our program.

To begin, let’s write code that will display some words when we run it. This function is called print. Copy and paste the code below into the grey box on your Jupyter notebook:

print("Hello World!")

Your notebook should look like this:

Entering in code into our Jupyter Notebook

Now, press Alt-Enter on your keyboard to run that snippet of code:

Press Alt-Enter to run that snippet of code

You can see that Jupyter notebook has displayed the words “Hello World!” on the display panel below the code snippet! The number 1 has also filled in the square brackets, meaning that this is the first code snippet that we’ve run thus far. This will help us to track the order in which we have run our code snippets.

Instead of Alt-Enter, note that you can also click Run when the code snippet is highlighted:

Click Run on the panel

If you wish to create new grey blocks to write more snippets of code, you can do so under Insert.

Jupyter Notebook also allows you to write normal text instead of code. Click on the drop-down menu that currently says “Code” and select “Markdown”:

Now, our grey box that is tagged as markdown will not have square brackets beside it. If you write some text in this grey box now and press Alt-Enter, the text will render it as plain text like this:

If we write text in our grey box tagged as markdown, pressing Alt-Enter will render it as plain text.

There are some other features that you can explore. But now we’ve got Jupyter notebook set up for us to start writing some code!

Setting up Anaconda environment and installing packages

Now we’ve got our coding platform set up. But are we going to write Deep Learning code from scratch? That seems like an extremely difficult thing to do!

The good news is that many others have written code and made it available to us! With the contribution of others’ code, we can play around with Deep Learning models at a very high level without having to worry about implementing all of it from scratch. This makes it extremely easy for us to get started with coding Deep Learning models.

For this tutorial, we will be downloading five packages that Deep Learning practitioners commonly use:

  • Set up Anaconda and Jupyter Notebook
  • Create Anaconda environments and install packages (code that others have written to make our lives tremendously easy) like tensorflow, keras, pandas, scikit-learn and matplotlib.

The first thing we will do is to create a Python environment. An environment is like an isolated working copy of Python, so that whatever you do in your environment (such as installing new packages) will not affect other environments. It’s good practice to create an environment for your projects.

Click on Environments on the left panel and you should see a screen like this:

Anaconda environments

Click on the button “Create” at the bottom of the list. A pop-up like this should appear:

A pop-up like this should appear.

Name your environment and select Python 3.7 and then click Create. This might take a few moments.

Once that is done, your screen should look something like this:

Notice that we have created an environment ‘intuitive-deep-learning’. We can see what packages we have installed in this environment and their respective versions.

Now let’s install some packages we need into our environment!

The first two packages we will install are called Tensorflow and Keras, which help us plug-and-play code for Deep Learning.

On Anaconda Navigator, click on the drop down menu where it currently says “Installed” and select “Not Installed”:

A whole list of packages that you have not installed will appear like this:

Search for “tensorflow”, and click the checkbox for both “keras” and “tensorflow”. Then, click “Apply” on the bottom right of your screen:

A pop up should appear like this:

Click Apply and wait for a few moments. Once that’s done, we will have Keras and Tensorflow installed in our environment!

Using the same method, let’s install the packages ‘pandas’, ‘scikit-learn’ and ‘matplotlib’. These are common packages that data scientists use to process the data as well as to visualize nice graphs in Jupyter notebook.

This is what you should see on your Anaconda Navigator for each of the packages.

Pandas:

Installing pandas into your environment

Scikit-learn:

Installing scikit-learn into your environment

Matplotlib:

Installing matplotlib into your environment

Once it’s done, go back to “Home” on the left panel of Anaconda Navigator. You should see a screen like this, where it says “Applications on intuitive-deep-learning” at the top:

Now, we have to install Jupyter notebook in this environment. So click the green button “Install” under the Jupyter notebook logo. It will take a few moments (again). Once it’s done installing, the Jupyter notebook panel should look like this:

Click on Launch, and the Jupyter notebook app should open.

Create a notebook and type in these five snippets of code and click Alt-Enter. This code tells the notebook that we will be using the five packages that you installed with Anaconda Navigator earlier in the tutorial.

import tensorflow as tf

import keras

import pandas

import sklearn

import matplotlib

If there are no errors, then congratulations — you’ve got everything installed correctly:

A sign that everything works!

If you have had any trouble with any of the steps above, please feel free to comment below and I’ll help you out!

*Originally published by Joseph Lee Wei En at *medium.freecodecamp.org

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