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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 10 Uses of Python with Examples

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Analytics Vidhya Last Updated : 11 Nov, 2024
6 min read

Python is a versatile and powerful programming language that has changed the face of software development with its ease of use, readability, and rich library support. Python has gained popularity amongst programmers and developers due to applications in various sectors. In this article, we will list the top ten real-world uses of Python, followed by practical Python examples, and emphasize the benefits of learning Python.

In this article, you will discover the various applications of Python programming, including what it is used for in web development, data analysis, automation, and machine learning. We will explore the diverse Python uses that make it a popular choice among developers. You will learn about the Python programming language uses that extend to scientific computing and data visualization. By the end, you’ll have a clear understanding of what is Python used for and how it can benefit your projects and career.

Table of contents

Why Learn Python?

Python, known for its simplicity and vast library support, is essential for web development, data analysis, and automation. Easy to learn, it offers clear syntax and versatility, enabling diverse applications from desktop programs to machine learning. Its rich libraries like Django and TensorFlow expedite development, enhancing productivity. Supported by a robust global community, Python fosters innovation and knowledge sharing. Widely adopted across industries, it offers diverse career opportunities. Its cross-platform compatibility ensures broad accessibility, making it ideal for efficient and scalable development across operating systems.

Check out our article on how much time it take to learn Python!

Top Uses of Python in the Real World

Python being a versatile programming language that offers a range of benefits to the real world. Here are top 10 uses of python. If you’re curious about what is python used for, the following examples highlight its extensive applications:

  1. Web Development
  2. Data Science
  3. Artificial Intelligence and Machine Learning
  4. Enterprise Applications
  5. Education Sector
  6. Web Scraping Applications
  7. Game Development
  8. Software Development
  9. Desktop GUI
  10. Operating Systems

Web Development

Owing to its ease of use and solid frameworks such as Django and Flask and Python’s simple syntax and extensive library, developers prefer the language to design scalable and dependable web applications. They employ uses of Python to leverage its adaptability for backend logic, database integration, and server-side scripting.

Python use in Web Development
Source: SparxIT

Example: One of the world’s most popular social media platforms’ Instagram‘, relies heavily on Python for its backend infrastructure.

Data Science

Python evolved as one of the most favored languages for data scientists and analysts because of its wealth of libraries such as NumPy, Pandas, and Matplotlib. These libraries help with a wide range of data processing, visualization, analysis, and machine learning applications. Moreover, Python is suitable for dealing with large datasets and sophisticated data processing operations.

wealth libraries

Example: Netflix, a leading streaming platform, utilizes Python for data analysis and recommendation systems.

Artificial Intelligence and Machine Learning

Python’s flexibility and extensive support for machine learning frameworks such as TensorFlow, Keras, and Scikit-learn have made it a popular language for AI and ML applications. It allows programmers to build intelligent systems, train sophisticated models, and apply these models in real-world applications. Python is a potent tool in AI and ML; its uses of Python pre-trained models are readily available and have strong community support.

10 reasons why python for machine learning

Example: Google’s DeepMind, known for its breakthroughs in artificial intelligence, heavily relies on Python for research and development.

Enterprise Applications

Building enterprise-level applications is a good fit for Python because of its simplicity and scalability. Its frameworks, such as pyramid and bottle, offer strong tools and components for the creation of effective software systems. Uses of Python’s wide library support has been beneficial for programmers who may communicate with databases, integrate complicated functionality, and manage security issues in corporate applications.

enterprise application examples
Source: Space Technologies

Example: Dropbox, a cloud storage and file sharing platform, leverages Python in its backend infrastructure.

Also, Check out this Article about Complete Python Tutorial to Learn Data Science

Education Sector

Python is the perfect language for teaching programming to pupils because it is user-friendly for beginners. The readability, clarity, and substantial library support make it simple for pupils to understand basic ideas. Python is a popular choice for educational applications because its visual and interactive capabilities allow students to learn programming while working on engaging projects.

python data types

Example: The Raspberry Pi Foundation promotes Python as the primary language for their educational single-board computers, encouraging students to explore coding and electronics.

Web Scraping Applications

Python is a great option for web scraping activities because of its strong packages, such as BeautifulSoup and Scrapy. Python allows developers to automate data collection procedures, obtain insights for numerous applications, and extract useful data from websites.

web scraping
Source: freecodecamp

Example: Price comparison websites often employ Python for web scraping to gather product information from multiple online retailers.

Game Development

Python is not typically associated with game development due to performance constraints. However, it is frequently used for prototyping, scripting, and building game logic. Python’s simplicity and libraries like Pygame make it an attractive option for developing 2D games and educational simulations.

python game development

Example: Eve Online, a popular massively multiplayer online game, employs different uses of Python for various in-game activities and server management.

Software Development

Python’s versatility extends beyond web development to general-purpose software development. Its readable syntax and extensive library support enable developers to create efficient and scalable software applications in less time. Python’s frameworks, like PyQT and Tkinter, facilitate the development of cross-platform desktop applications with rich graphical user interfaces (GUI).

Python use of Software development
Source: pexels

Example: Blender, a renowned 3D computer graphics software, relies on Python for scripting and automation.

Desktop GUI

Given its simplicity and cross-platform compatibility, Python is extensively used for designing desktop graphical user interfaces (GUI). Developers can effectively construct easy and visually appealing desktop apps for Windows, macOS, and Linux using frameworks such as PyQT and Tkinter.

Python in Desktop GUI
Source: Pexels

Example: The music streaming application, Spotify, utilizes Python for building its desktop client, which provides a seamless user experience.

Operating Systems

Although uses of Python are not commonly employed to build complete operating systems, it is often employed for system administration tasks. Python’s ease of use and powerful libraries, such as Fabric and Paramiko, simplify automation, configuration management, and network scripting.

Python uses of Operating Systems
Source: Pexels

Example: The popular Linux distribution, Ubuntu, relies on Python for various system administration and configuration tasks.

Conclusion

Python has proven to be an incredibly versatile and powerful programming language with a wide range of real-life applications. Its simplicity, readability, and vast ecosystem of libraries and frameworks make it a go-to choice for various industries and domains.

From web development to data analysis, machine learning to automation, python has become an indispensable tool for solving complex problems efficiently and effectively. Its user-friendly syntax and extensive community support have contributed to its widespread adoption and continuous growth.

You can start learning python with our free course on Introduction to Python!

Frequently Asked Questions

Q1. How is Python used in real life?

A. Python is used in a variety of jobs, such as data analysis, game creation, web development, and more, because of its versatility.

Q2. What are the three benefits of Python?

A. Python has three benefits: it’s easy to learn, flexible for a wide range of applications, and it offers a tonne of libraries that simplify development tasks.

Q3. Why is learning Python useful?

A. Python is extensively used in the business, has a big support community, and offers a wide range of libraries and frameworks for different applications; thus, learning it is beneficial.

Q4. Is Java better than Python?

A. Python and Java have different uses. Python is renowned for its simplicity, readability, and versatility, while Java is frequently employed for complex enterprise systems. The decision is based on the project’s particular specifications.

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10 Advantages of Python Over Other Programming Languages
10 Advantages of Python Over Other Programming Languages
Why Learn Python? 15 Reasons to Learn Python in 2024
Why Learn Python? 15 Reasons to Learn Python in 2024

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