Here are 20 project ideas to sharpen your coding skills and build your portfolio! 👇

💡 1. RESTful API for a Todo App
📚 2. Library Management System

🏬 3. E-commerce API with Payment Gateway
🛠 4. User Authentication System

🎵 5. Music Streaming Service
💬 6. Chat Application Backend

📦 7. Inventory Management API
📄 8. Blog Platform with Comments

🏥 9. Hospital Management System
📈 10. Analytics Dashboard

📬 11. Email Campaign API
🚗 12. Ride-Sharing Backend

📦 13. Warehouse Management System
🎓 14. Online Learning Platform

🌐 15. Social Media Backend
💳 16. Subscription Billing System

🛡 17. Role-Based Access Control
🍽 18. Restaurant Ordering API

🏨 19. Hotel Booking System
📝 20. Form Builder API

Best Programming Resources: https://topmate.io/coding/886839

All the best 👍👍

Frontend Development Resources: https://whatsapp.com/channel/0029VaxfCpv2v1IqQjv6Ke0r

Full stack Project Ideas 💡

Underrated Telegram Channel for Data Analysts 👇👇
https://t.iss.one/sqlspecialist

Here, you will get free tutorials to learn SQL, Python, Power BI, Excel and many more

Hope you guys will like it 😄

Numpy Cheatsheet 📱

Python vs R

The Pragmatic Programmer- A collection of tips to improve the development process in a pragmatic way📕

Learn JavaScript in 14 Days:

Part 1:

💻 Day 1 - Learn JavaScript Basics:
Start with understanding variables, data types, and basic syntax.

📊 Day 2 - Master Operators and Expressions:
Get comfortable using arithmetic, comparison, and logical operators.

⚖️ Day 3 - Dive into Conditional Statements:
Learn how to use if, else if, else, and switch for decision-making.

♻️ Day 4 - Explore Loops:
Understand how for, while, and do-while loops work.

🔧 Day 5 - Work with Functions:
Learn how to define and call functions, pass parameters, and return values.

📦 Day 6 - Introduction to Arrays:
Explore how to create arrays and manipulate them with methods like push(), pop(), and map().

📜 Day 7 - Object Basics:
Learn how to create and work with JavaScript objects, properties, and methods.

Like for part 2 ❤️

Do not forget to React ❤️ to this Message for More Content Like this

Thanks All For Joining❤️🙏

Python Code to remove Image Background
—————————————————————-
from rembg import remove
from PIL import Image

image_path = 'Image Name' ## ---> Change to Image name

output_image = 'ImageNew' ## ---> Change to new name your image

input = Image.open(image_path)

output = remove(input)

output.save(output_image)

Top 7 FREE Courses By Udacity 👇👇

Introduction to Python Programming

Intro to Java: Functional Programming

SQL for Data Analysis

Intro to Data Analysis

Developing Android Apps with Kotlin

Intro to JavaScript

Intro to Machine Learning

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ENJOY LEARNING 👍👍

Work Smarter 💪

Coding and Aptitude Round before interview

Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.

Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.

Resources for Prep:

For algorithms and data structures prep,Leetcode and Hackerrank are good resources.

For aptitude prep, you can refer to IndiaBixand Practice Aptitude.

With respect to data science challenges, practice well on GLabs and Kaggle.

Brilliant is an excellent resource for tricky math and statistics questions.

For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.

Things to Note:

Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!

In case, you are finished with the test before time, recheck your answers and then submit.

Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.

🚨30 FREE Dataset Sources for Data Science Projects🔥

Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/

US Government Dataset: https://www.data.gov/

Open Government Data (OGD) Platform India: https://data.gov.in/

The World Bank Open Data: https://data.worldbank.org/

Data World: https://data.world/

BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics

The Humanitarian Data Exchange (HDX): https://data.humdata.org/

Data at World Health Organization (WHO): https://www.who.int/data

FBI’s Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/

AWS Open Data Registry: https://registry.opendata.aws/

FiveThirtyEight: https://data.fivethirtyeight.com/

IMDb Datasets: https://www.imdb.com/interfaces/

Kaggle: https://www.kaggle.com/datasets

UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php

Google Dataset Search: https://datasetsearch.research.google.com/

Nasdaq Data Link: https://data.nasdaq.com/

Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html

Reddit - Datasets: https://www.reddit.com/r/datasets/

Open Data Network by Socrata: https://www.opendatanetwork.com/

Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/

Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/

IEEE Data Port: https://ieee-dataport.org/

Wikipedia: Database: https://dumps.wikimedia.org/

BuzzFeed News: https://github.com/BuzzFeedNews/everything

Academic Torrents: https://academictorrents.com/

Yelp Open Dataset: https://www.yelp.com/dataset

The NLP Index by Quantum Stat: https://index.quantumstat.com/

Computer Vision Online: https://www.computervisiononline.com/dataset

Visual Data Discovery: https://www.visualdata.io/

Roboflow Public Datasets: https://public.roboflow.com/

Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets

4 Projects to Add to Your Resume:

1. To-Do List App
2. E-commerce Product Catalog
3. Weather App
4. GitHub Repository Viewer

𝗧𝗼𝗽 𝟭𝟱 𝗚𝗮𝗺𝗲 𝗗𝗲𝘃 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀👾🎮

1. C++: AAA games (Unreal)
2. C#: Unity, indie game
3. JavaScript: Web game
4. Java: Android game
5. Python: Prototypes (Pygame)
6. Lua: Scripting (Roblox)
7. Swift: iOS games
8. Objective-C: Legacy iOS/macOS
9. Rust: System-level (Amethyst)
10. Go: Multiplayer servers
11. HTML5 + JS: Simple 2D games
12. Kotlin: Android apps
13. Haxe: Cross-platform 2D
14. TypeScript: Scalable web games
15. Ruby: Lightweight 2D games