Coding Projects

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.

7 Beginner Machine Learning Projects To Complete This Weekend 📊 1. Predicting Titanic Survival Predict who survived using age, gender & class. Learn data cleaning + logistic regression basics. Click here 📈 2. Predicting Stock Prices Forecast stock trends with ARIMA or LSTM. Practice feature engineering & MSE evaluation. click here 📧 3. Email Spam Classifier Build an NLP model to detect spam. Use TF-IDF + Naive Bayes/SVM for accuracy. Click here 🔢 4. Handwritten Digit Recognition Train a CNN on MNIST to identify digits (0–9). Explore image preprocessing & deep learning. Click here 🎬 5. Movie Recommendation System Suggest movies using collaborative filtering (SVD) & MovieLens dataset. Measure with RMSE. Click here 🔄 6. Customer Churn Prediction Find which customers may leave. Handle imbalanced data & use logistic regression/random forest. Click here 👤 7. Face Detection Use OpenCV Haar cascades to detect faces in images/videos. Learn filtering & edge detection. Find here

👆40 Project Ideas For Web Developers

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Deep Learning Project Ideas

Coding is tricky. Coding in interviews feels even harder. It’s intimidating, uncertain and hard to prepare. Here are 4 ways to do it! 1. Interview Cake: I think it is some of the best prep available and it is targeted toward weaknesses many data scientists have in algorithms and data structures: https://www.interviewcake.com/ 2. Leetcode: While developed for software engineering interviews, it has a LOT of useful content for learning algorithms. For data science, I'd suggest focusing on Easy/Medium: https://leetcode.com/ 3. Cracking the Coding Interview: Amazing book, sometimes referred to as CTCI. A classic and one you should have: https://cin.ufpe.br/~fbma/Crack/Cracking%20the%20Coding%20Interview%20189%20Programming%20Questions%20and%20Solutions.pdf 4. Daily Coding Problem: The book and the website are awesome. Work on a daily problem. This was my go to resource for when I was looking to stay sharp: https://www.dailycodingproblem.com/

5 Python Projects for Beginners 👆

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Random Module in Python 👆

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R Programming Roadmap | |-- Fundamentals | |-- Basics of Programming | | |-- Introduction to R | | |-- Setting Up Development Environment (RStudio) | | | |-- Syntax and Structure | | |-- Basic Syntax | | |-- Variables and Data Types | | |-- Operators and Expressions | |-- Control Structures | |-- Conditional Statements | | |-- If-Else Statements | | | |-- Loops | | |-- For Loop | | |-- While Loop | | |-- Repeat Loop | | | |-- Exception Handling | | |-- Try-Catch Block | | |-- Warnings and Errors | |-- Functions and Scope | |-- Defining Functions | | |-- Function Syntax | | |-- Parameters and Arguments | | |-- Return Statement | | | |-- Scope | | |-- Global and Local Scope | | |-- Environments | |-- Data Structures | |-- Vectors | | |-- Creating Vectors | | |-- Vectorized Operations | | | |-- Lists | | |-- Creating and Manipulating Lists | | | |-- Matrices | | |-- Creating Matrices | | |-- Matrix Operations | | | |-- Data Frames | | |-- Creating Data Frames | | |-- Manipulating Data Frames | | | |-- Factors | | |-- Creating and Using Factors | |-- Data Manipulation | |-- dplyr | | |-- Select, Filter, Arrange, Mutate, Summarize | | |-- Piping (%>%) | | | |-- tidyr | | |-- Gather and Spread | | |-- Separate and Unite | |-- Data Visualization | |-- Base R Graphics | | |-- Plot, Hist, Boxplot, Barplot | | | |-- ggplot2 | | |-- Grammar of Graphics | | |-- Creating Plots (Scatter, Line, Bar, Histogram) | | |-- Customizing Plots (Themes, Labels, Legends) | |-- Statistical Analysis | |-- Descriptive Statistics | | |-- Mean, Median, Mode | | |-- Standard Deviation, Variance | | | |-- Inferential Statistics | | |-- Hypothesis Testing (t-tests, ANOVA) | | |-- Correlation and Regression Analysis | |-- Advanced R | |-- Date and Time | | |-- Working with Dates and Times | | |-- lubridate Package | | | |-- String Manipulation | | |-- Stringr Package | | |-- Regular Expressions | |-- Programming Concepts | |-- Apply Family of Functions | | |-- lapply, sapply, tapply, vapply | | | |-- Debugging | | |-- Debugging Tools (browser, debug, trace) | | | |-- Object-Oriented Programming (OOP) | | |-- S3 and S4 Systems | | |-- Reference Classes (R5) | |-- Libraries and Packages | |-- CRAN and Bioconductor | | |-- Installing and Using Packages | | | |-- Popular Packages | | |-- Data Manipulation (dplyr, tidyr) | | |-- Data Visualization (ggplot2, lattice) | | |-- Machine Learning (caret, randomForest) | |-- Reporting and Documentation | |-- RMarkdown | | |-- Creating RMarkdown Documents | | |-- Including Code Chunks | | |-- Generating Reports (HTML, PDF, Word) | |-- Deployment and Reproducibility | |-- Version Control with Git | | |-- Integrating RStudio with GitHub | | | |-- Reproducible Research | | |-- Workflow Practices | | |-- Using renv for Package Management | |-- Working with Big Data | |-- Data.table Package | | |-- Efficient Data Manipulation | | | |-- SparkR | | |-- Using Apache Spark with R | | |-- Handling Large Datasets Free R Programming Courses https://imp.i115008.net/gbJr5r https://bit.ly/33LsOqo https://bit.ly/3shVAJ9 Join @free4unow_backup for more free courses ENJOY LEARNING 👍👍

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)

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Python Project Ideas 👆

Data Science Essential Libraries ✅

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📖 SQL aggregate functions

Important skills every self-taught developer should master: 💻 HTML, CSS & JavaScript — the foundation of web development ⚙️ Git & GitHub — track changes and collaborate effectively 🧠 Problem-solving — break down and debug complex issues 🗄️ Basic SQL — manage and query data efficiently 🧩 APIs — fetch and use data from external sources 🧱 Frameworks — like React, Flask, or Django to build faster 🧼 Clean Code — write readable, maintainable code 📦 Package Managers — like npm or pip for managing libraries 🚀 Deployment — host your projects for the world to see Web Development Resources: https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z