Python Certification Training for Data Scienc ...
- 52k Enrolled Learners
- Live Class
Python for Data Science is a must learn for professionals in the Data Analytics domain. With the growth in the IT industry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language. Through this article, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.
Before we begin, let me just list out the topics I’ll be covering through the course of this article.
You can go through this Python for data science video lecture where our Python Training expert is discussing each & every nitty-gritty of the technology.
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:
And do you know the best part? Data Scientist is one of the highest paid jobs who earn around $130,621 per year as per Indeed.com.
Python was created by Guido Van Rossum in 1989. It is an interpreted language with dynamic semantics. It is free to access and run on all platforms. Python is:
1) Object Oriented
2) High-Level Language
3) Easy to Learn
4) Procedure Oriented
Let me guide you through the process of installing Jupyter on your system. Just follow the below steps:
Step 1: Go to the link: http://jupyter.org/
Step 2: You can either click on “Try in your browser” or “Install the Notebook”.
Well, I would recommend you to install Python and Jupyter using Anaconda distribution. Once you have installed Jupyter, it will open on your default browser by typing “Jupyter Notebook” in command prompt. Let us now perform a basic program on Jupyter.
name=input("Enter your Name:") print("Hello", name)
Now to run this, press “Shift+Enter” and view the output. Refer to the below screenshot:
In case you are facing any issues with the installation or Jupyter basics, you can go through the below video. It will also take you to various fundamentals of Python, along with a practical demonstrating the various libraries such as Numpy, Pandas, Matplotlib and Seaborn. Hope you like it! :)
Now is the time when you get your hands dirty in Python programming. But for that, you should have a basic understanding of the following topics:
Variables: Variables refers 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.
Data Types: 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.
Operators: Operators helps to manipulate the value of operands. The list of operators in Python includes- Arithmetic, Comparison, Assignment, Logical, Bitwise, Membership, and Identity.
Conditional Statements: Conditional statements helps to execute a set of statements based on a condition. There are namely three conditional statements – If, Elif and Else.
Loops: Loops are used to iterate through small pieces of code. There are three types of loops namely – While, for and nested loops.
Functions: Functions are used to divide your code into useful blocks, allowing you to order the code, make it more readable, reuse it & save some time.
For more information and practical implementations, you can refer to this blog: Python Tutorial.
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. Some of them are listed below:
Problem Statement: You are given a dataset which comprises of comprehensive statistics on a range of aspects like distribution & nature of prison institutions, overcrowding in prisons, type of prison inmates etc. You have to use this dataset to perform descriptive statistics and derive useful insights out of the data. Below are few tasks:
For data loading, write the below code:
import pandas as pd import matplotlib.pyplot as plot %matplotlib inline file_name = "prisoners.csv" prisoners = pd.read_csv(file_name) prisoners
Now to use describe method in Pandas, just type the below statement:
Next in Python with data science article, let us perform data manipulation.
And finally, let us perform some visualization in Python for data science article. Refer the below code:
import numpy as np xlabels = prisoners['STATE/UT'].values plot.figure(figsize=(20, 3)) plot.xticks(np.arange(xlabels.shape), xlabels, rotation = 'vertical', fontsize = 18) plot.xticks plot.bar(np.arange(prisoners.values.shape),prisoners['total_benefited'],align = 'edge')
I hope my blog on “Python for data science” was relevant for you. To get in-depth knowledge, check out our interactive, live-online Edureka Python Data Science Certification Training here, that comes with 24*7 support to guide you throughout your learning period.
Got a question for us? Please mention it in the comments section of this “Python for data science” article and we will get back to you as soon as possible.