Tech Update 💻🖥️

Tech Update 💻🖥️

5/27/2025, 12:29:13 PM

*Operators in Python* Operators are special symbols or keywords that perform operations on variables and values. *Types of Operators in Python:* *1. Arithmetic Operators* Used for basic math: + (add), - (subtract), * (multiply), / (divide), // (floor divide), % (modulus), ** (power) a = 10 b = 3 print(a + b) # 13 print(a ** b) # 1000 *2. Comparison Operators* Used to compare two values: ==, !=, >, <, >=, <= x = 5 print(x == 5) # True print(x != 3) # True *3. Logical Operators* Used to combine conditional statements: and, or, not age = 20 print(age > 18 and age < 25) # True *4. Assignment Operators* Used to assign values to variables: =, +=, -=, *=, /=, etc. score = 10 score += 5 # score is now 15 *Mini Project: Build a Simple Calculator* Let’s apply what we’ve learned! *Task: Build a calculator that asks the user to enter two numbers and an operator, then prints the result.* *Approach* 1. Take two numbers from the user. 2. Ask for an operator (+, -, *, /). 3. Perform the operation based on what the user entered. 4. Print the result, or shows "Invalid operator!" if the input is wrong. *Python Code*: num1 = float(input("Enter first number: ")) op = input("Enter operator (+, -, *, /): ") num2 = float(input("Enter second number: ")) if op == "+": print(num1 + num2) elif op == "-": print(num1 - num2) elif op == "*": print(num1 * num2) elif op == "/": print(num1 / num2) else: print("Invalid operator!")

Tech Update 💻🖥️

5/13/2025, 3:34:24 PM

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Tech Update 💻🖥️

5/24/2025, 2:33:16 PM

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Tech Update 💻🖥️

5/29/2025, 6:49:52 PM

Famous Programming Languages and Their Frameworks 1. Python: Frameworks: Django Flask Pyramid Tornado 2. JavaScript: Frameworks (Front-End): React Angular Vue.js Ember.js Frameworks (Back-End): Node.js (Runtime) Express.js Nest.js Meteor 3. Java: Frameworks: Spring Framework Hibernate Apache Struts Play Framework 4. Ruby: Frameworks: Ruby on Rails (Rails) Sinatra Hanami 5. PHP: Frameworks: Laravel Symfony CodeIgniter Yii Zend Framework 6. C#: Frameworks: .NET Framework ASP.NET ASP.NET Core 7. Go (Golang): Frameworks: Gin Echo Revel 8. Rust: Frameworks: Rocket Actix Warp 9. Swift: Frameworks (iOS/macOS): SwiftUI UIKit Cocoa Touch 10. Kotlin: Frameworks (Android): Android Jetpack Ktor 11. TypeScript: Frameworks (Front-End): Angular Vue.js (with TypeScript) React (with TypeScript) 12. Scala: Frameworks: Play Framework Akka 13. Perl: Frameworks: Dancer Catalyst 14. Lua: Frameworks: OpenResty (for web development) 15. Dart: Frameworks: Flutter (for mobile app development) 16. R: Frameworks (for data science and statistics): Shiny ggplot2 17. Julia: Frameworks (for scientific computing): Pluto.jl Genie.jl 18. MATLAB: Frameworks (for scientific and engineering applications): Simulink 19. COBOL: Frameworks: COBOL-IT 20. Erlang: Frameworks: Phoenix (for web applications) 21. Groovy: Frameworks: Grails (for web applications) ENJOY LEARNING 👍👍

Tech Update 💻🖥️

2/27/2025, 6:27:33 AM

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Tech Update 💻🖥️

2/27/2025, 5:02:27 PM

*Key Python topics which you must revise before your data analytics interview-* 1. Data Manipulation with Pandas • Creating, merging, and joining DataFrames. • Handling missing data, filtering, and sorting. • GroupBy operations, pivot tables, and aggregations. • Time series analysis. 2. Data Visualization • Matplotlib: Basic plots (line, bar, scatter, etc.), customization, subplots. • Seaborn: Advanced visualization, pair plots, heatmaps, categorical plots. • Plotly: Interactive visualizations. 3. Numpy • Array operations, indexing, slicing, and reshaping. • Vectorized operations and broadcasting. • Mathematical functions and linear algebra. 4. SQL Integration • Using pandas with SQL databases (e.g., pd.read_sql). • Querying and manipulating data from databases. 5. Exploratory Data Analysis (EDA) • Descriptive statistics, correlation, and data profiling. • Identifying trends, outliers, and patterns in data. 6. Statistical Analysis • Hypothesis testing, p-values, and confidence intervals. • A/B testing and experimental design. • Regression analysis (linear, logistic). 7. Data Cleaning • Handling missing or incorrect data. • Data normalization, standardization, and encoding categorical variables. 8. Machine Learning Basics • Understanding scikit-learn pipelines. • Implementing simple models (e.g., Linear Regression, Decision Trees). • Feature selection and model evaluation (cross-validation, metrics). 9. Regular Expressions (Regex) • Pattern matching and text manipulation in Python. 10. Scripting and Automation • Writing reusable scripts for data processing. • File handling and interacting with APIs. Hope you'll like it Like this post if you need more resources like this 👍❤️

Tech Update 💻🖥️

2/23/2025, 7:29:06 AM

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Tech Update 💻🖥️

2/25/2025, 5:58:33 AM

Complete Roadmap to Become a Java ♨️ Developer: 1. Core Java (fundamentals of Java programming language) 2. Maven 3. Spring Core, Spring MVC, and Spring AOP 4. Spring Boot and REST APls (Restful Web Services) 5. Spring Data - JPA, Hibernate (with H2, MySQL, MongoDB and Redis) 6. Testing (JUnit 5, JPA Test, MockMVC, etc.) 7. Spring Security 8. Microservices and Spring Cloud 9. Docker and Kubernetes 10. Deployment of Spring Boot Apps on Cloud (AWS) 11. Optional - Serverless, Batch processing etc

Tech Update 💻🖥️

2/18/2025, 7:49:09 AM

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