Python For Data Science

What is Data Science

You may have searched products online and get all relevant results or write something on your mobile keyboard, and it suggests you the exact word that has been used in sentences. How YouTube suggests you all your favorite songs on your home screen is possible because of all the data science. That's why today's blog is dedicated to Data Science and how to implement data science concepts with Python.

Introduction to Python for data science
Python is an explanatory, high-level, open-source programming language. Python is also a multi-pattern programming language that follows all OOPs concepts. The syntax is easy to learn, makes it more convenient programming. In Python, you can analyze, visualize, and manipulate data. Python has a robust library for scientific computation and machine learning.

Data science Concepts with Python:

Pandas:

In python programming, we can do data manipulation with the help of the Pandas frame. Pandas mean Panel Data. It is multidimensional data involving measurements over time. Pandas created in 2015 by Wes McKinney.

• Features of Panel Data
• Series object and data Frame
• Handling Missing Data
• Data Alignment
• Group by Functionality
• Slicing, Indexing, Subsetting
• Merging and Joining
• Reshaping
• Hierarchical Labeling of Axes
• Robust input-output tool

Machine Learning: Machine learning is the subset of artificial intelligence where we give the ability to the machine to perform a task for us, eg.

• Have you ever shopped online on amazon? When you search for a product, you will see a similar list of products below your favorite product.
• Another example is amazon Alexa when you give a command Alexa performs that task like, Alexa on the lights, Alexa Play a song, .
• Traffic prediction in google maps.

Why Learn Python For Data Science?
Python is an easy data analysis tool. Python is a compelling programming language. You can use it to build practically any piece of software you can also apply for data science. You can model your sales data. Text mining on your latest twitter feeds. Build the next movie recommendation engine. It’s all possible by Python.

Python is open-source, and there’s a bunch of programmers across the world that have written amazing extensions to Python, to help you achieve great things. So the best way to learn Python is by doing It.

Using Python for data science

Numerous programming languages are used for data science like SAS, JAVA, R, and Matlab. But many developers choose Python over the other programming languages because of its easy syntax. So here are the features that make Python suitable for learning data science.

Its syntax closely resembles the english language.
Python fits for many platforms. It means Python allows you to perform cross-language operations seamlessly. It is supported by many platforms ranging from windows, Linux, macintosh Solaris, and many more.
In Python, you can perform data manipulation, analysis, and visualization.
Python has a robust library for machine learning applications & other scientific operations.

How to learn Python for data science:

Python is a bit easy to learn as compared to other programming languages. Here are a few tips that will make it more comfortable for you.

Learn the Basics of Python:
Python is a significant programming language, so every programmer should have to know about it. Numerous Programmers utilize this language to make algorithms, create websites, , and perform other significant projects. In some case, attempting to learn Python can be frustrating, challenging, and troublesome, particularly in case you don’t know how to move toward it. There are several resources available on the web to master Python.

Learn Python’s data science library:

Python library carries built-in modules that grant you access to enter in the python functionality. Python gathered a lot of reputation for data analysis so it is must for you to keep the knowledge of
python libraries

so there are a few necessary libraries of python is

• Pandas – pandas: powerful Python data analysis toolkit
• Numpy – Python Numpy Tutorial
• Matplotlib – Pyplot tutorial – Matplotlib 2.0.2 documentation for deep learning algorithms,
• Scikit Learn – sci-kit-learn Tutorials
• Tensorflow – Tutorials | TensorFlow
• Keras – Keras Tutorial: The Ultimate Beginner’s Guide to Deep Learning in Python
• Theano – Tutorial – Theano 0.9.0 documentation

Find projects to Work.
To learn Python, you have to follow several ways, like reading books, watching videos, and taking classes. But projects will help you to consolidate your ideas once you get a good grasp of syntaxes and libraries start work on your small projects.

Learn advanced data science techniques: 
Once you finish with the basics, go with the advanced python for data science techniques, for example.

• Linear Regression 
• Bayesian Statistics 
• Neural Networks
• Relevancy Algorithm  
• Supervised Learning 
• Logistic Regression 
• Jackknife Regression 
• Density Estimation
• Collaborative Filtering 
• (Geo-) Spatial Modeling  
• Ensembles 
• Lift Modeling 
• Cross-Validation
• Rule System 
• Indexation / Cataloguing 
• Naive Bayes 
• Attribution Modeling 
• Monte-Carlo Simulation 
• Confidence Interval 
• Test of Hypotheses 
• Pattern Recognition 
• Model Fitting
• Yield Optimization
• Imputation 
• Clustering –
• Principal Component Analysis – (PCA)
• Time Series 
• Decision Trees 
• Random Numbers 
• Survival Analysis 
• Arbitrage 
• Game Theory 
• Linkage Analysis 

Conclusion: 
In this blog, we’ve discussed python for data science and how Python is a great choice to learn data science. What are the best practices to learn data science with python? Python is the only language that makes data science quite easy for beginners. if you are a computer science student and looking for python programming help so get the best help at Coursementor