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Python for Data Science – How to Development

  • April 27, 2020

Python for Data Science is a must-learn skill for professionals in the Data Analytics domain. With the growth in the IT industry, there is a booming demand for skilled Data Science and Python  has evolved as the most preferred programming language for data-driven development. Through this article, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.

What Is Data Science?

Data Science has emerged as a very promising career path for skilled development. The truest essence of Data Science lies in the problem-solving capabilities to provide insights and solutions driven by data. There is a lot of misconception when it comes to Data Science, the Data Science life cycle is one way to get a clearer perspective to understand what Data Science really is.

Data Science Life Cycle


Why Python For Data Science?

Python is no-doubt the best-suited language for a Data Scientist. I have listed down a few points which will help you understand why people go with Python for Data Science:

  1. Python is a free, flexible and powerful open-source language
  2. Python cuts development time in half with its simple and easy to read syntax.
  3. With Python, you can perform data manipulation, analysis, and visualization.
  4. Python provides powerful libraries for Machine learning applications and other scientific computations


Python Basics For Data Science:

  1. Variables: Variables refer to the reserved memory locations to store the values. In Python, you don’t need to declare variables before using them or even declare their type. 
  2. Data Type: Python supports numerous data types, which defines the operations possible on the variables and the storage method. The list of data types includes – Numeric, Lists, Strings, tuples, Sets, and Dictionary.
  3. Operators: Operators helps to manipulate the value of operands. The list of operators in Python includes- Arithmetic, Comparison, Assignment, Logical, Bitwise, Membership, and Identity.
  4. Conditional: Conditional statements help to execute a set of statements based on a condition. There are namely three conditional statements – If, Elif and Else.


Python Libraries For Data Science:

This is the part where the actual power of Python with Data Science comes into the picture. Python comes with numerous libraries for scientific computing, analysis, visualization, etc.

1.Numpy:

Numpy is a core library of Python for Data Science which stands for ‘Numerical Python’. It is used for scientific computing, which contains a powerful n-dimensional array object and provides tools for integrating C, C++, etc.

2. Pandas

Pandas is an important library in Python for Data Science. It is used for data manipulation and analysis.  It is well suited for different data such as tabular, ordered and unordered, matrix data, etc. 

3. Seaborn

Seaborn is a statistical plotting library in Python. So whenever you’re using Python for Data Science, you will be using matplotlib (for 2D visualizations) and Seaborn, which has its beautiful default styles and a high-level interface to draw statistical graphics.

4. MatplotLib

Matplotlib is a powerful library for visualization in Python. It can be used in Python scripts, shell, web application servers, and other GUI toolkits. You can use different types of plots and how multiple plots work using Matplotlib.

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