Data science using python

Python for Data Science Tutorial

Data Science is applied to gather multiple data sets to collect information and project the insight and interpret it to make an effective business decision. However, being a data scientist requires you to learn some of the best and most highly used programming languages, such as Java, C++, R, Python, etc. Among these, Python has been considered the preferred choice among data scientists throughout the globe.

In this Python for Data Science Tutorial, we’ll explore exciting world of Python and its wide-ranging applications in data science. We will also explore a variety of data science techniques used in data science using the Python programming language.

Prerequisites: In order to make the most of this tutorial, it is recommended to have a basic understanding of Python programming language. .

Why Use Python For Data Science?

Python is in demand for the past few years and the recent survey also suggested the same, Python leads the chart among the top programming languages in both the TIOBE index & PYPL Index. However, to support this, there are 5 concrete reasons behind this,

1. Easy To Learn: Being an open-source platform, Python has a simple and intuitive syntax that is easy to learn and read. This makes it a great language for beginners to learn data science.

2. Cross-Platform: Being a developer, you don’t need to worry about the data types. The reason is, Python allows developers to run the code on Windows, Mac OS X, UNIX, and Linux.

3. Portable: Being an easy & beginner’s friendly programming language, Python is highly portable in nature which means that a developer can run their code on different machines without making any further changes.

4. Extensive Library: Python has several powerful libraries that make data analysis and visualization easy. Pandas is a library for data manipulation and analysis, NumPy is a library for numerical computation, and Matplotlib is a library for data visualization.

5. Community Support: Python has a large and active community that supports and contributes to the development of various libraries and tools for data science. This community has created many useful libraries, including Pandas, NumPy, matplotlib, and SciPy, which are widely used in data science.

However, there are a lot more reasons to opt for Python for Data Science such as OOP, expressive language, the ability to allocate memory dynamically, etc. and that’s the reason for using Python Programming Language for Data Science applications.

Benefit Of Using Python For Data Science

In summary, Python is a popular language for data science because it is easy to learn, has a large and active community, offers powerful libraries for data analysis and visualization, and has excellent machine-learning libraries.

In terms of application areas, Data scientists prefer Python for the following modules:

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Engineers coming from academia and industry are saying deep learning frameworks the scientific packages available with Python have made Python incredibly productive and versatile. Python deep learning frameworks have undergone significant evolution, and they are quickly evolving.

Learning Curve of Python

• Getting Started with Python:
  • Why Python?
  • Python installation
  • Python Interpreter
  • Virtual Environment
  • Ipython
  • Jupyter Notebook
  • Variables in Python
  • Comments in Python
  • Python Keywords
  • Arithmetic Operators
  • Comparison Operators
  • Logical Operators
  • Bitwise Operators
  • Assignment Operators
  • Identity Operators and Membership Operators
  • Python Data Types
  • Numbers
    • Int()
    • float()
    • complex()
    • String Slicing
    • String lower() and upper() methods
    • Various String methods
    • String Concatenation
    • String Format()
    • Escape Sequences in Python
    • Universal Character set(Unicode) Strings
    • Byte Objects vs String in Python
    • if & else
    • Nested if
    • for Loop
    • while loops
    • range()
    • enumerate()
    • Continue
    • Break
    • Nested Loop
    • Python Data Structures
    • Lists
    • List Slicing
    • Tuples
    • Sets
    • Dictionary
    • File handling with Python
    • Reading data from text-file
    • Writing data to text-file
    • JSON with Python
    • Reading and Writing JSON to a File in Python
    • Create functions in Python
    • In-built functions in Python
      • Python Built-in Functions
      • Lambda
      • map()
      • Introduction of OOPs
      • Python OOPs Concepts
      • Python Classes and Objects
      • Encapsulation in Python
      • Class Instance Attributes in Python
      • Python Class Members
      • Class method vs Static method
      • Python Inheritance
      • Types of Inheritance Python
      • Inheritance, examples of object, issubclass and super
      • Polymorphism in Python
      • Abstract Classes in Python
      • Exception Handling
      • Python Decorators

      Python Libraries for Data Analysis

      Python Libraries for Data Visualization:

      Python Libraries for Image Processing:

      Machine Learning:

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      FAQs on Python for Data Science Tutorial

      1. What to do after learning Python?

      • Web Development
      • Mentor/Teacher
      • Full Stack Developer
      • QA Engineer
      • HTML, CSS, or JavaScript to brush up your web development skills
      • Start with Machine Learning, Deep Learning, NLP, etc. to start your carrier as ML Engineer

      2. How Python is used in Data Science?

      Backed up by large community support, Python entails more than 137,000 libraries which means you can possibly do anything alone with Python and one must focus on grasping the popular tools & frameworks that can make their work easier. Talking of Data Science, libraries such as NumPy, TensorFlow, SciPy, Pandas, Keras, PyTorch, etc are among them for different purposes and makes data science work lot more easier.

      3. Is Python sufficient for Data Science?

      Python alone is not sufficient for Data Science for sure. However, it can help you to start your journey but as per market demand and growing technology, it is mandatory to have a hands-on practice that includes machine learning, statistics, data visualization, data analysis, web scraping, numeric computation, etc.

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      Python for Data Science Tutorial

      

      Data is the new Oil. This statement shows how every modern IT system is driven by capturing, storing and analysing data for various needs. Be it about making decision for business, forecasting weather, studying protein structures in biology or designing a marketing campaign. All of these scenarios involve a multidisciplinary approach of using mathematical models, statistics, graphs, databases and of course the business or scientific logic behind the data analysis. So we need a programming language which can cater to all these diverse needs of data science. Python shines bright as one such language as it has numerous libraries and built in features which makes it easy to tackle the needs of Data science.

      In this tutorial we will cover these the various techniques used in data science using the Python programming language.

      Audience

      This tutorial is designed for Computer Science graduates as well as Software Professionals who are willing to learn data science in simple and easy steps using Python as a programming language.

      Prerequisites

      Before proceeding with this tutorial, you should have a basic knowledge of writing code in Python programming language, using any python IDE and execution of Python programs. If you are completely new to python then please refer our Python tutorial to get a sound understanding of the language.

      Execute Python Programs

      For most of the examples given in this tutorial you will find Try it option, so just make use of it and enjoy your learning.

      Try following example using Try it option available at the top right corner of the below sample code box

      #!/usr/bin/python print "Hello, Python!"

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