Available now! Telegram Research 2025 โ the year's key insights 
Computer Science Interview Books
Open in Telegram
Best Resource & Notes for Coding interview preparation Admin: @love_data Buy ads: https://telega.io/c/InterviewBooks
Show more๐ Analytical overview of Telegram channel Computer Science Interview Books
Channel Computer Science Interview Books (@interviewbooks) in the English language segment is an active participant. Currently, the community unites 40 032 subscribers, ranking 4 597 in the Education category and 9 779 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 40 032 subscribers.
According to the latest data from 30 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 76 over the last 30 days and by 10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.65%. Within the first 24 hours after publication, content typically collects 1.52% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 461 views. Within the first day, a publication typically gains 609 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
- Thematic interests: Content is focused on key topics such as learning, link:-, element, sql, stack.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โBest Resource & Notes for Coding interview preparation
Admin: @love_data
Buy ads: https://telega.io/c/InterviewBooksโ
Thanks to the high frequency of updates (latest data received on 01 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
40 032
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| Date | Subscriber Growth | Mentions | Channels | |
| 01 July | +15 |
Channel Posts
Leetcode is a tool to learn
Neetcode is a tool to learn
CodeChef is a tool to learn
Codeforces is a tool to learn
HackerRank is a tool to learn
GeeksForGeeks is a tool to learn
It doesn't matter:
- which platform you are using
- or how many problems you solve
All that matters is how strong you grasp concepts and adapt to problems.
Don't chase the status of 500 or 1000 problems solved.
Chase proper learning & training your mind for problem-solving.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
| 2 | โ
Programming Important Terms You Should Know ๐ป๐
Programming is the backbone of tech, and knowing the right terms can boost your learning and career.
๐ง Core Programming Concepts
โข Programming: Writing instructions for a computer to perform tasks.
โข Algorithm: Step-by-step procedure to solve a problem.
โข Flowchart: Visual representation of a programโs logic.
โข Syntax: Rules that define how code must be written.
โข Compilation: Converting source code into machine code.
โข Interpretation: Executing code line-by-line without compiling first.
โ๏ธ Basic Programming Elements
โข Variable: Storage location for data.
โข Constant: Fixed value that cannot change.
โข Data Type: Type of data (int, float, string, boolean).
โข Operator: Symbol performing operations (+, -, *, /, ==).
โข Expression: Combination of variables, operators, and values.
โข Statement: A single line of instruction in a program.
๐ Control Flow Concepts
โข Conditional Statements: Execute code based on conditions (if, else).
โข Loops: Repeat a block of code (for, while).
โข Break Statement: Exit a loop early.
โข Continue Statement: Skip the current loop iteration.
โข Switch Case: Multi-condition decision structure.
๐ฆ Functions Modular Programming
โข Function: Reusable block of code performing a task.
โข Parameter: Input passed to a function.
โข Return Value: Output returned by a function.
โข Module: File containing reusable functions or classes.
โข Library: Collection of pre-written code.
๐งฉ Object-Oriented Programming (OOP)
โข Class: Blueprint for creating objects.
โข Object: Instance of a class.
โข Encapsulation: Bundling data and methods together.
โข Inheritance: One class acquiring properties of another.
โข Polymorphism: Same function behaving differently in different contexts.
โข Abstraction: Hiding complex implementation details.
๐ Data Structures
โข Array: Collection of elements stored sequentially.
โข List: Ordered collection that can change size.
โข Stack: Last In First Out (LIFO) structure.
โข Queue: First In First Out (FIFO) structure.
โข Hash Table / Dictionary: Key-value data storage.
โข Tree: Hierarchical data structure.
โข Graph: Network of connected nodes.
โก Advanced Programming Concepts
โข Recursion: Function calling itself.
โข Concurrency: Multiple tasks running simultaneously.
โข Multithreading: Multiple threads within a program.
โข Memory Management: Allocation and deallocation of memory.
โข Garbage Collection: Automatic memory cleanup.
โข Exception Handling: Handling runtime errors using try, catch, except.
๐ Software Development Concepts
โข Framework: Pre-built structure for building applications.
โข API: Interface allowing different software to communicate.
โข Version Control: Tracking code changes using tools like Git.
โข Debugging: Finding and fixing code errors.
โข Testing: Verifying that code works correctly.
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| 6 | Technical Questions Wipro may ask on their interviews
1. Data Structures and Algorithms:
ย ย - "Can you explain the difference between an array and a linked list? When would you use one over the other in a real-world application?"
ย ย - "Write code to implement a binary search algorithm."
2. Programming Languages:
ย ย - "What is the difference between Java and C++? Can you provide an example of a situation where you would prefer one language over the other?"
ย ย - "Write a program in your preferred programming language to reverse a string."
3. Database and SQL:
ย ย - "Explain the ACID properties in the context of database transactions."
ย ย - "Write an SQL query to retrieve all records from a 'customers' table where the 'country' column is 'India'."
4. Networking:
ย ย - "What is the difference between TCP and UDP? When would you choose one over the other for a specific application?"
ย ย - "Explain the concept of DNS (Domain Name System) and how it works."
5. System Design:
ย ย - "Design a simple online messaging system. What components would you include, and how would they interact?"
ย ย - "How would you ensure the scalability and fault tolerance of a web service or application?" | 3 742 |
| 7 | 30-days learning plan to cover data science fundamental algorithms, important concepts, and practical applications ๐๐
### Week 1: Introduction and Basics
Day 1: Introduction to Data Science
- Overview of data science, its importance, and key concepts.
Day 2: Python Basics for Data Science
- Python syntax, variables, data types, and basic operations.
Day 3: Data Structures in Python
- Lists, dictionaries, sets, and tuples.
Day 4: Data Manipulation with Pandas
- Introduction to Pandas, Series, DataFrame, basic operations.
Day 5: Data Visualization with Matplotlib and Seaborn
- Creating basic plots (line, bar, scatter), customizing plots.
Day 6: Introduction to Numpy
- Arrays, array operations, mathematical functions.
Day 7: Data Cleaning and Preprocessing
- Handling missing values, data normalization, and scaling.
### Week 2: Exploratory Data Analysis and Statistical Foundations
Day 8: Exploratory Data Analysis (EDA)
- Techniques for summarizing and visualizing data.
Day 9: Probability and Statistics Basics
- Descriptive statistics, probability distributions, and hypothesis testing.
Day 10: Introduction to SQL for Data Science
- Basic SQL commands for data retrieval and manipulation.
Day 11: Linear Regression
- Concept, assumptions, implementation, and evaluation metrics (R-squared, RMSE).
Day 12: Logistic Regression
- Concept, implementation, and evaluation metrics (confusion matrix, ROC-AUC).
Day 13: Regularization Techniques
- Lasso and Ridge regression, preventing overfitting.
Day 14: Model Evaluation and Validation
- Cross-validation, bias-variance tradeoff, train-test split.
### Week 3: Supervised Learning
Day 15: Decision Trees
- Concept, implementation, advantages, and disadvantages.
Day 16: Random Forest
- Ensemble learning, bagging, and random forest implementation.
Day 17: Gradient Boosting
- Boosting, Gradient Boosting Machines (GBM), and implementation.
Day 18: Support Vector Machines (SVM)
- Concept, kernel trick, implementation, and tuning.
Day 19: k-Nearest Neighbors (k-NN)
- Concept, distance metrics, implementation, and tuning.
Day 20: Naive Bayes
- Concept, assumptions, implementation, and applications.
Day 21: Model Tuning and Hyperparameter Optimization
- Grid search, random search, and Bayesian optimization.
### Week 4: Unsupervised Learning and Advanced Topics
Day 22: Clustering with k-Means
- Concept, algorithm, implementation, and evaluation metrics (silhouette score).
Day 23: Hierarchical Clustering
- Agglomerative clustering, dendrograms, and implementation.
Day 24: Principal Component Analysis (PCA)
- Dimensionality reduction, variance explanation, and implementation.
Day 25: Association Rule Learning
- Apriori algorithm, market basket analysis, and implementation.
Day 26: Natural Language Processing (NLP) Basics
- Text preprocessing, tokenization, and basic NLP tasks.
Day 27: Time Series Analysis
- Time series decomposition, ARIMA model, and forecasting.
Day 28: Introduction to Deep Learning
- Neural networks, perceptron, backpropagation, and implementation.
Day 29: Convolutional Neural Networks (CNNs)
- Concept, architecture, and applications in image processing.
Day 30: Recurrent Neural Networks (RNNs)
- Concept, LSTM, GRU, and applications in sequential data.
Best Resources to learn Data Science ๐๐
kaggle.com/learn
t.me/datasciencefun
developers.google.com/machine-learning/crash-course
topmate.io/coding/914624
t.me/pythonspecialist
freecodecamp.org/learn/machine-learning-with-python/
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| 11 | ๐ Key Skills for Aspiring Tech Specialists
๐ Data Analyst:
- Proficiency in SQL for database querying
- Advanced Excel for data manipulation
- Programming with Python or R for data analysis
- Statistical analysis to understand data trends
- Data visualization tools like Tableau or PowerBI
- Data preprocessing to clean and structure data
- Exploratory data analysis techniques
๐ง Data Scientist:
- Strong knowledge of Python and R for statistical analysis
- Machine learning for predictive modeling
- Deep understanding of mathematics and statistics
- Data wrangling to prepare data for analysis
- Big data platforms like Hadoop or Spark
- Data visualization and communication skills
- Experience with A/B testing frameworks
๐ Data Engineer:
- Expertise in SQL and NoSQL databases
- Experience with data warehousing solutions
- ETL (Extract, Transform, Load) process knowledge
- Familiarity with big data tools (e.g., Apache Spark)
- Proficient in Python, Java, or Scala
- Knowledge of cloud services like AWS, GCP, or Azure
- Understanding of data pipeline and workflow management tools
๐ค Machine Learning Engineer:
- Proficiency in Python and libraries like scikit-learn, TensorFlow
- Solid understanding of machine learning algorithms
- Experience with neural networks and deep learning frameworks
- Ability to implement models and fine-tune their parameters
- Knowledge of software engineering best practices
- Data modeling and evaluation strategies
- Strong mathematical skills, particularly in linear algebra and calculus
๐ง Deep Learning Engineer:
- Expertise in deep learning frameworks like TensorFlow or PyTorch
- Understanding of Convolutional and Recurrent Neural Networks
- Experience with GPU computing and parallel processing
- Familiarity with computer vision and natural language processing
- Ability to handle large datasets and train complex models
- Research mindset to keep up with the latest developments in deep learning
๐คฏ AI Engineer:
- Solid foundation in algorithms, logic, and mathematics
- Proficiency in programming languages like Python or C++
- Experience with AI technologies including ML, neural networks, and cognitive computing
- Understanding of AI model deployment and scaling
- Knowledge of AI ethics and responsible AI practices
- Strong problem-solving and analytical skills
๐ NLP Engineer:
- Background in linguistics and language models
- Proficiency with NLP libraries (e.g., NLTK, spaCy)
- Experience with text preprocessing and tokenization
- Understanding of sentiment analysis, text classification, and named entity recognition
- Familiarity with transformer models like BERT and GPT
- Ability to work with large text datasets and sequential data
๐ Embrace the world of data and AI, and become the architect of tomorrow's technology! | 2 821 |
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| 14 | If you aspire to work in top product companies, hereโs my advice:
๐ For SDE-1 or SWE positions, focus on:
โ๏ธ Continuously upskilling and improving your abilities.
โ๏ธ Developing strong problem-solving skills.
โ๏ธMastering DSA โ trust me, youโll be tested on it, so aim to excel.
Also, learn how to design scalable systems and understand how to build solutions that can handle growth in users and data.
๐ For higher-level roles (SDE-2 and SDE-3), focus on:
โ๏ธ DSA + System Design (both LLD and HLD).
โ๏ธ Building your leadership skills, as youโll need to lead teams and projects.
๐ธI know itโs challenging to do this while working full-time, but youโll need to carve out time to consistently upskill yourself.
Remember, your learning plan should be sensible and well-organized.
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| 15 | ๐ Coding Projects & Ideas ๐ป
Inspire your next portfolio project โ from beginner to pro!
๐๏ธ Beginner-Friendly Projects
1๏ธโฃ To-Do List App โ Create tasks, mark as done, store in browser.
2๏ธโฃ Weather App โ Fetch live weather data using a public API.
3๏ธโฃ Unit Converter โ Convert currencies, length, or weight.
4๏ธโฃ Personal Portfolio Website โ Showcase skills, projects & resume.
5๏ธโฃ Calculator App โ Build a clean UI for basic math operations.
โ๏ธ Intermediate Projects
6๏ธโฃ Chatbot with AI โ Use NLP libraries to answer user queries.
7๏ธโฃ Stock Market Tracker โ Real-time graphs & stock performance.
8๏ธโฃ Expense Tracker โ Manage budgets & visualize spending.
9๏ธโฃ Image Classifier (ML) โ Classify objects using pre-trained models.
๐ E-Commerce Website โ Product catalog, cart, payment gateway.
๐ Advanced Projects
1๏ธโฃ1๏ธโฃ Blockchain Voting System โ Decentralized & tamper-proof elections.
1๏ธโฃ2๏ธโฃ Social Media Analytics Dashboard โ Analyze engagement, reach & sentiment.
1๏ธโฃ3๏ธโฃ AI Code Assistant โ Suggest code improvements or detect bugs.
1๏ธโฃ4๏ธโฃ IoT Smart Home App โ Control devices using sensors and Raspberry Pi.
1๏ธโฃ5๏ธโฃ AR/VR Simulation โ Build immersive learning or game experiences.
๐ก Tip: Build in public. Share your process on GitHub, LinkedIn & Twitter.
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| 16 | ๐ Complete Roadmap to Become a Web Developer
๐ 1. Learn the Basics of the Web
โ How the internet works
โ What is HTTP/HTTPS, DNS, Hosting, Domain
โ Difference between frontend & backend
๐ 2. Frontend Development (Client-Side)
โ๐ HTML โ Structure of web pages
โ๐ CSS โ Styling, Flexbox, Grid, Media Queries
โ๐ JavaScript โ DOM Manipulation, Events, ES6+
โ๐ Responsive Design โ Mobile-first approach
โ๐ Version Control โ Git & GitHub
๐ 3. Advanced Frontend
โ๐ JavaScript Frameworks/Libraries โ React (recommended), Vue or Angular
โ๐ Package Managers โ npm or yarn
โ๐ Build Tools โ Webpack, Vite
โ๐ APIs โ Fetch, REST API integration
โ๐ Frontend Deployment โ Netlify, Vercel
๐ 4. Backend Development (Server-Side)
โ๐ Choose a Language โ Node.js (JavaScript), Python, PHP, Java, etc.
โ๐ Databases โ MongoDB (NoSQL), MySQL/PostgreSQL (SQL)
โ๐ Authentication & Authorization โ JWT, OAuth
โ๐ RESTful APIs / GraphQL
โ๐ MVC Architecture
๐ 5. Full-Stack Skills
โ๐ MERN Stack โ MongoDB, Express, React, Node.js
โ๐ CRUD Operations โ Create, Read, Update, Delete
โ๐ State Management โ Redux or Context API
โ๐ File Uploads, Payment Integration, Email Services
๐ 6. Testing & Optimization
โ๐ Debugging โ Chrome DevTools
โ๐ Performance Optimization
โ๐ Unit & Integration Testing โ Jest, Cypress
๐ 7. Hosting & Deployment
โ๐ Frontend โ Netlify, Vercel
โ๐ Backend โ Render, Railway, or VPS (e.g. DigitalOcean)
โ๐ CI/CD Basics
๐ 8. Build Projects & Portfolio
โ Blog App
โ E-commerce Site
โ Portfolio Website
โ Admin Dashboard
๐ 9. Keep Learning & Contributing
โ Contribute to open-source
โ Stay updated with trends
โ Practice on platforms like LeetCode or Frontend Mentor
โ
Apply for internships/jobs with a strong GitHub + portfolio!
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| 19 | AI text often sounds almost right, but still feels off. AIToHuman fixes that by making your writing sound natural, clear, and human without changing your ideas. Clean up your content in seconds. Go try it โ https://aitohuman.com | 3 353 |
| 20 | ๐ซ If you're a Web Developer in your 20s, beware of this silent career killer:
โบ Fake learning.
It feels like you're growing, but you're not.
Hereโs how it sneaks in:
โฆ You watch a 10-minute YouTube video on React.
โฆ Then scroll through a blog on โCSS Grid vs Flexbox.โ
โฆ Try out a VS Code extension.
โฆ Skim a post on โTop 10 Tailwind Tricks.โ
โฆ Maybe save a few GitHub repos for later.
By evening?
You feel productive. Smart. Ahead.
But a week later?
โฆ You can't build a simple responsive layout from scratch.
โฆ You still fumble with useEffect or basic routing.
โฆ You avoid the command line and Git.
Thatโs fake learning.
Youโre collecting knowledge like trading cards โ but not using it.
๐ ๏ธ Hereโs how to escape that trap:
โ Pick one skill (e.g., HTML+CSS, React, APIs) โ go deep, not wide.
โ Build projects from scratch: portfolios, blogs, dashboards.
โ Donโt copy-paste. Type the code. Break it. Fix it.
โ Push to GitHub. Explain it in a README or to a peer.
โ Ask: โCan I build this without a tutorial?โ โ If not, you havenโt learned it.
๐ก Real developers arenโt made in tutorials.
Theyโre forged in broken UIs, bugged APIs, and 3 AM console logs.
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