Python Data Science Handbook | Learn Real Python – Data Science

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If you are aspiring to learn Python Data Science, then this is the best platform for you all. Know the introduction and libraries of Python for Data Science. Get the basics of Real Python and also advanced Data Science. Go through the below sections to know the tools of Python Data Science.

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Python Data Science

For most of the researchers, Python language is the first class tool because of its libraries for manipulating, gaining insight and storing from data. Several resources are available for individual pieces of data science stack like NumPy, Scikit-Learn, Pandas, IPython and other tools.

Most of the data crunchers & scientists who are familiar with writing and reading python code will find this desk reference excellent to solve day to day issues like cleaning data, transforming and manipulating. Also, the other tasks like visualizing different data types, using various data to build machine learning or statistical models. This data science must have reference for scientific computing in Python. Python Datascience is a must learn language for professionals in Data Analytics domain.

With the advance growth in the IT industry, there is a also the booming demand for the skilled Data Scientists where Python is evolved as the mot preferable language.

Why Learn Python for Data Science?

Python is undoubtedly best-suited language for the Data Scientists. Here are the few important points which help you to determine the uses and the preferences of the people to go with Python Data Science.

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

Basics of Python for Data Science

It is essential to know the basics of python for data science. To learn the language perfectly, the programmers should know the basics such as.

  • Variables

Variables is the reserved memory location to store the data values. In Python language, you don’t need to declare the variables before you are using them or even declaring their type.

  • Data Types

Python supports various data types, which defines operations that are possible on the storage methods and variables. The data types list includes Lists, Dictionary, Numeric, Strings, Tuples and Sets.

  • Operators

Operators helps to employ the operands value. The Operators list in Python includes Assignment, Bitwise, Identity, Comparison, Logical, Arithmetic, Membership.

  • Conditional Statements

These conditional statements helps to execute the set of statements based on the particular condition. There are three conditional statements like Elif, Else, If.

  • Loop

Loops are helpful to iterate through small pieces of code. There are 3 types of loops namely – while, nested loops and for.

  • Functions

Functions are mainly used to divide your program code into useful blocks which allows you to order the code. It helps in making the code more readable, reuse and saves more time.

Libraries of Python for Data Science

The libraries play a main  part where the actual power of Python language with data science comes into picture. Python comes with the numerous libraries for visualization, scientific computing etc. Some of them are as follows.

  • NumPy

NumPy is the core library of Python Data Science which stands for “Numerical Python”. This library NumPy is used for Scientific Computing which has a powerful n-dimensional array object and add tools for integrating C, C++ etc. This library can also be used as the multi-dimensional container for the generic data where you can achieve various special functions and Numpy operations.

  • Matplotlib

Matplotlib is the very powerful library for the visualization in Python language. This library can be used in Shell, Web Application serves, Python Scripts and other GUI toolkits. You can use different plot types and also how multiple plots work while using Matplotlib.

  • Scikit-learn

Scikit learn library is one of the main attractions, where you can appliance machine learning using Python Data Science. Scikit learn is a free library which consists efficient and simple tools for mining purposes and data analysis. With the help of scikit learn, you can appliance various algorithms such as time series algorithm, logistic regression. Know the various techniques to learn Python Data Science before proceeding ahead.

  • Seaborn

The statistical plotting library in Python is Seaborn. Therefore, you will be using Seaborn and matplotlib (for 2D visulaization), whenever you are using Python for data science which has its beautiful styles and high level interface to draw graphics.

  • Pandas

Pandas is the Python’s important library for data science. This library is used for Analysis and data manipulation. It is also suited for different data such as ordered, tabular, unordered time series, data matrix etc.

Python Data Science Example Scenarios

Python Data Science is the process of acquiring knowledge and observation from a huge and distinct set of data through analyzing, processing and organizing the data. This Python for Data Science involves most of the disciplines like statistical and mathematical, extracting data and modelling from its source and applying its data visualization techniques. Moreover, it also involves handling big data technologies to gather both unstructured and structured data. Check some of the example scenarios where Python for Data Science is used.

  • Recommendation systems

As online shopping has become more prevalent, the platforms of e-commerce are able to get shopping preferences as well as performance of products in the market. Based on the needs and requirements of the shoppers, recommendation systems are created which creates models that predict the shoppers needs and  show them the products which they want to buy.

  • Financial Risk management

Based on the customer past defaults, habits, socio-economic indicators and other financial commitments, the financial risk involving credits and loans are analysed in the better way by using Python Data Science. This type of data is generated in different formats from different sources. Organizing the data and getting insight into customers profile needs the aid of Data Science. The outcome is curtail loss for the financial organization by averting bad debt.

  • Improvement in Health Care services

Even in the health care industry, there is various data which is classified into patient information, technical data, legal rules, drug information and technical data. All the data needs to be analysed in a harmonized manner to produce understanding that will save cost both for the care receiver and the health care provider.

  • Computer Vision

The processing of large image data from multiple objects of same category can be possible through the advancement  in knowing an image by a computer. For example, Recognition of face. These sets are data are modelled and also the algorithms are created to apply the new model to the newer image to gain complete satisfaction. Creation of these type of models and processing huge data sets needs a lot of tools in Python Data Science.

Hope this article has cleared all your doubts regarding Python for Data Science. For further information and also for the free Python Online Training in India, bookmark this site. Therefore, you can get all the updates regarding Python language which helps you to clear all your doubts and make you perfect in all the concepts. If you have any doubts please do not hesitate to contact us from the below comment box.

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