Python Learning Plan in 2024

|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)

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C programming notes

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Python handwriting notes 📝

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Dynamic programming Goldmine ❤️

Dynamic Programming is one of the most important topic of any tech interview process. Found this really amazing blog on LeetCode covering important topics.

👉 DP for Beginners

Link : https://leetcode.com/discuss/general-discussion/662866/dp-for-beginners-problems-patterns-sample-solutions

👉 Dynamic Programming Patterns

Link: https://leetcode.com/discuss/general-discussion/458695/dynamic-programming-patterns

👉 knapsack problem

Link: https://leetcode.com/discuss/study-guide/1200320/Thief-with-a-knapsack-a-series-of-crimes

👉 How to solve DP - String?

Link : https://leetcode.com/discuss/general-discussion/651719/how-to-solve-dp-string-template-and-4-steps-to-be-followed

👉 Dynamic Programming Questions Thread

Link : https://leetcode.com/discuss/general-discussion/491522/dynamic-programming-questions-thread

👉 How to approach most of DP problems

Link : https://leetcode.com/problems/house-robber/solutions/156523/From-good-to-great.-How-to-approach-most-of-DP-problems

👉 Iterative DP solution using subset sum with explanation

Link : https://leetcode.com/problems/target-sum/solutions/97334/java-15-ms-c-3-ms-ons-iterative-dp-solution-using-subset-sum-with-explanation/

👉 Dynamic Programming Summary

Link : https://leetcode.com/discuss/general-discussion/592146/dynamic-programming-summary

👉 Categorization of Leetcode DP problem

Link : https://leetcode.com/discuss/general-discussion/1000929/solved-all-dynamic-programming-dp-problems-in-7-months

👉 Must do Dynamic programming Problems Category wise

Link : https://leetcode.com/discuss/general-discussion/1050391/Must-do-Dynamic-programming-Problems-Category-wise

👉 Dynamic programming is simple

Link : https://leetcode.com/discuss/study-guide/1490172/Dynamic-programming-is-simple

👉 Dynamic programming on subsets with examples

Link : https://leetcode.com/discuss/general-discussion/1125779/Dynamic-programming-on-subsets-with-examples-explained


👉 DP IS EASY

Link : https://leetcode.com/problems/target-sum/solutions/455024/DP-IS-EASY!-5-Steps-to-Think-Through-DP-Questions/

𝐉𝐨𝐢𝐧 𝐭𝐡𝐢𝐬 𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦 𝐆𝐫𝐨𝐮𝐩 𝐟𝐨𝐫 𝐏𝐫𝐞𝐦𝐢𝐮𝐦 𝐉𝐨𝐛𝐬/𝐍𝐨𝐭𝐞𝐬 :
https://t.iss.one/getjobss

Java Developer Interview ❤
It'll gonna be super helpful for YOU

𝗧𝗼𝗽𝗶𝗰 𝟭: 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗳𝗹𝗼𝘄 𝗮𝗻𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
- Please tell me about your project and its architecture, Challenges faced?
- What was your role in the project? Tech Stack of project? why this stack?
- Problem you solved during the project? How collaboration within the team?
- What lessons did you learn from working on this project?
- If you could go back, what would you do differently in this project?

𝗧𝗼𝗽𝗶𝗰 𝟮: 𝗖𝗼𝗿𝗲 𝗝𝗮𝘃𝗮
- String Concepts/Hashcode- Equal Methods
- Immutability
- OOPS concepts
- Serialization
- Collection Framework
- Exception Handling
- Multithreading
- Java Memory Model
- Garbage collection

Tech Community
👉 t.iss.one/Java_Programming_Notes

𝗧𝗼𝗽𝗶𝗰 𝟯: 𝗝𝗮𝘃𝗮-𝟴/𝗝𝗮𝘃𝗮-𝟭𝟭/𝗝𝗮𝘃𝗮𝟭𝟳
- Java 8 features
- Default/Static methods
- Lambda expression
- Functional interfaces
- Optional API
- Stream API
- Pattern matching
- Text block
- Modules

𝗧𝗼𝗽𝗶𝗰 𝟰: 𝗦𝗽𝗿𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸, 𝗦𝗽𝗿𝗶𝗻𝗴-𝗕𝗼𝗼𝘁, 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗲𝘀𝘁 𝗔𝗣𝗜
- Dependency Injection/IOC, Spring MVC
- Configuration, Annotations, CRUD
- Bean, Scopes, Profiles, Bean lifecycle
- App context/Bean context
- AOP, Exception Handler, Control Advice
- Security (JWT, Oauth)
- Actuators
- WebFlux and Mono Framework
- HTTP methods
- JPA
- Microservice concepts
- Spring Cloud

𝗧𝗼𝗽𝗶𝗰 𝟱: 𝗛𝗶𝗯𝗲𝗿𝗻𝗮𝘁𝗲/𝗦𝗽𝗿𝗶𝗻𝗴-𝗱𝗮𝘁𝗮 𝗝𝗽𝗮/𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 (𝗦𝗤𝗟 𝗼𝗿 𝗡𝗼𝗦𝗤𝗟)
- JPA Repositories
- Relationship with Entities
- SQL queries on Employee department
- Queries, Highest Nth salary queries
- Relational and No-Relational DB concepts
- CRUD operations in DB
- Joins, indexing, procs, function

𝗧𝗼𝗽𝗶𝗰 𝟲: 𝗖𝗼𝗱𝗶𝗻𝗴
- DSA Related Questions
- Sorting and searching using Java API.
- Stream API coding Questions

Tech Jobs and Internships
t.iss.one/getjobss

𝗧𝗼𝗽𝗶𝗰 𝟳: 𝗗𝗲𝘃𝗼𝗽𝘀 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗼𝗻 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗧𝗼𝗼𝗹𝘀
- These types of topics are mostly asked by managers or leads who are heavily working on it, That's why they may grill you on DevOps/deployment-related tools, You should have an understanding of common tools like Jenkins, Kubernetes, Kafka, Cloud, and all.

𝗧𝗼𝗽𝗶𝗰𝘀 𝟴: 𝗕𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲
- The interviewer always wanted to ask about some design patterns, it may be Normal design patterns like singleton, factory, or observer patterns to know that you can use these in coding.

PDFs and Notes 📝
t.iss.one/Java_Programming_Notes

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Here are seven popular programming languages and their benefits:

1. Python:
- Benefits: Python is known for its simplicity and readability, making it a great choice for beginners. It has a vast ecosystem of libraries and frameworks for various applications such as web development, data science, machine learning, and automation. Python's versatility and ease of use make it a popular choice for a wide range of projects.

2. JavaScript:
- Benefits: JavaScript is the language of the web, used for building interactive and dynamic websites. It is supported by all major browsers and has a large community of developers. JavaScript can also be used for server-side development (Node.js) and mobile app development (React Native). Its flexibility and wide range of applications make it a valuable language to learn.

3. Java:
- Benefits: Java is a robust, platform-independent language commonly used for building enterprise-level applications, mobile apps (Android), and large-scale systems. It has strong support for object-oriented programming principles and a rich ecosystem of libraries and tools. Java's stability, performance, and scalability make it a popular choice for building mission-critical applications.

4. C++:
- Benefits: C++ is a powerful and efficient language often used for system programming, game development, and high-performance applications. It provides low-level control over hardware and memory management while offering high-level abstractions for complex tasks. C++'s performance, versatility, and ability to work closely with hardware make it a preferred choice for performance-critical applications.

5. C#:
- Benefits: C# is a versatile language developed by Microsoft and commonly used for building Windows applications, web applications (with ASP.NET), and games (with Unity). It offers a modern syntax, strong type safety, and seamless integration with the .NET framework. C#'s ease of use, robustness, and support for various platforms make it a popular choice for developing a wide range of applications.

6. R:
- Benefits: R is a language specifically designed for statistical computing and data analysis. It has a rich set of built-in functions and packages for data manipulation, visualization, and machine learning. R's focus on data science, statistical modeling, and visualization makes it an ideal choice for researchers, analysts, and data scientists working with large datasets.

7. Swift:
- Benefits: Swift is Apple's modern programming language for developing iOS, macOS, watchOS, and tvOS applications. It offers safety features to prevent common programming errors, high performance, and interoperability with Objective-C. Swift's clean syntax, powerful features, and seamless integration with Apple's platforms make it a preferred choice for building native applications in the Apple ecosystem.

These are just a few of the many programming languages available today, each with its unique strengths and use cases.

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Complete Roadmap to learn SQL in 2024 👇👇

1. Basic Concepts
- Understand databases and SQL.
- Learn data types (INT, VARCHAR, DATE, etc.).

2. Basic Queries
- SELECT: Retrieve data.
- WHERE: Filter results.
- ORDER BY: Sort results.
- LIMIT: Restrict results.

3. Aggregate Functions
- COUNT, SUM, AVG, MAX, MIN.
- Use GROUP BY to group results.

4. Joins
- INNER JOIN: Combine rows from two tables based on a condition.
- LEFT JOIN: Include all rows from the left table.
- RIGHT JOIN: Include all rows from the right table.
- FULL OUTER JOIN: Include all rows from both tables.

5. Subqueries
- Use nested queries for complex data retrieval.

6. Data Manipulation
- INSERT: Add new records.
- UPDATE: Modify existing records.
- DELETE: Remove records.

7. Schema Management
- CREATE TABLE: Define new tables.
- ALTER TABLE: Modify existing tables.
- DROP TABLE: Remove tables.

8. Indexes
- Understand how to create and use indexes to optimize queries.

9. Views
- Create and manage views for simplified data access.

10. Transactions
- Learn about COMMIT and ROLLBACK for data integrity.

11. Advanced Topics
- Stored Procedures: Automate complex tasks.
- Triggers: Execute actions automatically based on events.
- Normalization: Understand database design principles.

12. Practice
- Use platforms like LeetCode, HackerRank, or learnsql for hands-on practice.

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30 days roadmap to learn Python for Data Analysis 😄👇

Free Resources to Learn Python for Data Analysis: https://t.iss.one/pythonanalyst/102

Days 1-5: Introduction to Python
1. Day 1: Install Python and a code editor (e.g., Anaconda, Jupyter Notebook).
2. Day 2-5: Learn Python basics (variables, data types, and basic operations).

Days 6-10: Control Flow and Functions
6. Day 6-8: Study control flow (if statements, loops).
9. Day 9-10: Learn about functions and modules in Python.

Days 11-15: Data Structures
11. Day 11-12: Explore lists, tuples, and dictionaries.
13. Day 13-15: Study sets and string manipulation.

Days 16-20: Libraries for Data Analysis
16. Day 16-17: Get familiar with NumPy for numerical operations.
18. Day 18-19: Dive into Pandas for data manipulation.
20. Day 20: Basic data visualization with Matplotlib.

Days 21-25: Data Cleaning and Analysis
21. Day 21-22: Data cleaning and preprocessing using Pandas.
23. Day 23-25: Exploratory data analysis (EDA) techniques.

Days 26-30: Advanced Topics
26. Day 26-27: Introduction to data visualization with Seaborn.
27. Day 28-29: Introduction to machine learning with Scikit-Learn.
30. Day 30: Create a small data analysis project.

Use platforms like Kaggle to find datasets for projects & GeekforGeeks to practice coding problems.

Best Resource to learn Python

Python Interview Questions with Answers

Freecodecamp Python Course with FREE Certificate

Python for Data Analysis and Visualization

Python course for beginners by Microsoft

Python course by Google

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System Design Interview Preparation

System Design Interview Books:
Essential reads for understanding system design concepts and interview questions.

Grokking the System Design Interview by Design Guru:
A practical guide to system design with real-world scenarios.

Designing Data-Intensive Applications:
Learn about the architecture of data systems and how to design data-heavy applications.

Complete roadmap to learn data science in 2024 👇👇

1. Learn the Basics:
- Brush up on your mathematics, especially statistics.
- Familiarize yourself with programming languages like Python or R.
- Understand basic concepts in databases and data manipulation.

2. Programming Proficiency:
- Develop strong programming skills, particularly in Python or R.
- Learn data manipulation libraries (e.g., Pandas) and visualization tools (e.g., Matplotlib, Seaborn).

3. Statistics and Mathematics:
- Deepen your understanding of statistical concepts.
- Explore linear algebra and calculus, especially for machine learning.

4. Data Exploration and Preprocessing:
- Practice exploratory data analysis (EDA) techniques.
- Learn how to handle missing data and outliers.

5. Machine Learning Fundamentals:
- Understand basic machine learning algorithms (e.g., linear regression, decision trees).
- Learn how to evaluate model performance.

6. Advanced Machine Learning:
- Dive into more complex algorithms (e.g., SVM, neural networks).
- Explore ensemble methods and deep learning.

7. Big Data Technologies:
- Familiarize yourself with big data tools like Apache Hadoop and Spark.
- Learn distributed computing concepts.

8. Feature Engineering and Selection:
- Master techniques for creating and selecting relevant features in your data.

9. Model Deployment:
- Understand how to deploy machine learning models to production.
- Explore containerization and cloud services.

10. Version Control and Collaboration:
- Use version control systems like Git.
- Collaborate with others using platforms like GitHub.

11. Stay Updated:
- Keep up with the latest developments in data science and machine learning.
- Participate in online communities, read research papers, and attend conferences.

12. Build a Portfolio:
- Showcase your projects on platforms like GitHub.
- Develop a portfolio demonstrating your skills and expertise.

Best Resources to learn Data Science

Intro to Data Analytics by Udacity

Machine Learning course by Google

Machine Learning with Python

Data Science Interview Questions

Data Science Project ideas

Data Science: Linear Regression Course by Harvard

Machine Learning Interview Questions

Free Datasets for Projects

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The reason you're not feeling motivated is because you don't have a clear goal.

You do have a goal, but it's only that you want to make a lot of money. With just that, you'll only experience FOMO (fear of missing out), not money.

Hard work is your responsibility, but you need to set small and immediate goals. For example, if you're studying DSA, it's not something you can complete in one day. A goal for now should be to master one topic thoroughly until you can solve all medium-level questions, and slowly, you'll crack it.

This is crucial at every stage of life.

Motivation will come when you start achieving small things, and eventually, everything will fall into place one day. ♥️

Use cases of top programming languages