Coding Interview Resources
前往频道在 Telegram
This channel contains the free resources and solution of coding problems which are usually asked in the interviews. Managed by: @love_data
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网络:📈 Telegram 频道 Coding Interview Resources 的分析概览
频道 Coding Interview Resources (@crackingthecodinginterview) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 52 208 名订阅者，在 技术与应用 类别中位列第 2 466，并在 印度 地区排名第 6 748 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 52 208 名订阅者。
根据 07 十月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 9，过去 24 小时变化为 13，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 1.82%。内容发布后 24 小时内通常能获得 0.72% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 953 次浏览，首日通常累积 376 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 3。
- 主题关注点： 内容集中在 array, stack, algorithm, programming, sort 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“This channel contains the free resources and solution of coding problems which are usually asked in the interviews.
Managed by: @love_data”
凭借高频更新（最新数据采集于 08 十月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
52 208
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| 日期 | 订阅者增长 | 提及 | 频道 | |
| 08 十月 | 0 | |||
| 07 十月 | +13 | |||
| 06 十月 | +13 | |||
| 05 十月 | 0 | |||
| 04 十月 | +15 | |||
| 03 十月 | 0 | |||
| 02 十月 | +1 | |||
| 01 十月 | +11 |
频道帖子
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| 2 | 🚀 DSA Topics Every Programmer Should Know 💻🔥
📦 1. Arrays
✔ Traversal
✔ Searching
✔ Sorting
✔ Prefix Sum
✔ Two Pointers
🔤 2. Strings
✔ Character Frequency
✔ Palindromes
✔ Anagrams
✔ String Manipulation
🔗 3. Linked Lists
✔ Singly Linked List
✔ Doubly Linked List
✔ Reverse a Linked List
✔ Fast & Slow Pointers
📚 4. Stacks & Queues
✔ Stack Operations
✔ Queue Operations
✔ Monotonic Stack
✔ Circular Queue
🔑 5. Hashing
✔ HashMap
✔ HashSet
✔ Frequency Counting
✔ Duplicate Detection
🌳 6. Trees
✔ Binary Trees
✔ BST
✔ Tree Traversals
✔ Height & Depth
✔ Lowest Common Ancestor
🌐 7. Graphs
✔ BFS
✔ DFS
✔ Shortest Path
✔ Connected Components
✔ Topological Sorting
🔎 8. Searching & Sorting
✔ Binary Search
✔ Merge Sort
✔ Quick Sort
✔ Heap Sort
🧩 9. Recursion & Backtracking
✔ Recursion Basics
✔ Subsets
✔ Permutations
✔ Combination Problems
✔ N-Queens
⚡ 10. Dynamic Programming
✔ Memoization
✔ Tabulation
✔ 1D & 2D DP
✔ Knapsack Problems
✔ Longest Common Subsequence
⏱️ BONUS: Complexity Analysis
✔ Big-O Notation
✔ Time Complexity
✔ Space Complexity
💬 Tap ❤️ if this helped you! | 320 |
| 3 | 🧩Now, Let's Understand Functions in Programming 👨💻🔥
After variables, operators, conditions, and loops, the next important concept is functions.
Functions help you organize code, avoid repetition, and make programs easier to understand and maintain.
🧠 1. What is a Function?
A function is a reusable block of code designed to perform a specific task.
Instead of writing the same code repeatedly, you can put it inside a function and call it whenever you need it.
For example:
def greet():
print("Hello!")
greet()
Output:
Hello!
🎯 2. Why Do We Use Functions?
Functions help with:
✔ Code reusability
✔ Better organization
✔ Easier debugging
✔ Readability
✔ Maintaining large programs
A good function usually focuses on one clear task.
📥 3. Function Parameters
Functions can accept information from the code that calls them.
def greet(name):
print("Hello", name)
greet("Alex")
Output:
Hello Alex
Here, "name" is a parameter.
"Alex" is the argument passed to the function.
📤 4. Returning a Value
A function can return a result instead of directly displaying it.
def add(a, b):
return a + b
result = add(10, 5)
print(result)
Output:
15
"return" sends the result back to the place where the function was called.
🔄 5. Parameters vs Arguments
These two terms are often confused.
In:
def add(a, b):
return a + b
"a" and "b" are parameters.
In:
add(10, 5)
"10" and "5" are arguments.
Parameter → Variable defined by the function
Argument → Actual value passed to the function
⚙️ 6. Default Parameters
A function can have a default value for a parameter.
def greet(name="Guest"):
print("Hello", name)
greet()
greet("Alex")
Output:
Hello Guest
Hello Alex
If no argument is provided, the default value is used.
🌍 7. Functions in Different Languages
The syntax changes between languages, but the concept remains similar.
Python
def add(a, b):
return a + b
JavaScript
function add(a, b) {
return a + b;
}
Java
static int add(int a, int b) {
return a + b;
}
C++
int add(int a, int b) {
return a + b;
}
The purpose is the same: define reusable logic that can be called when needed.
🧠 8. Local Variables
A variable created inside a function is generally local to that function.
def calculate():
result = 100
print(result)
calculate()
Here, "result" belongs to the function's local scope.
Understanding scope becomes especially important as programs become larger.
🔁 9. Functions Can Call Other Functions
One function can use another function.
def add(a, b):
return a + b
def show_result():
result = add(10, 20)
print(result)
show_result()
This allows larger programs to be broken into smaller, manageable pieces.
🧩 10. Functions and Loops
Functions and loops are often used together.
def print_numbers():
for i in range(1, 6):
print(i)
print_numbers()
The function organizes the task, while the loop handles repetition.
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-----
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| 4 |  🚀𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 | 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗪𝗜𝘁𝗵 𝗚𝗲𝗻𝗔𝗜
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| 5 |  𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘! 🔥
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| 6 | ✅ Daily Coding Habits That Make You a Better Developer 🧠💻✨
1️⃣ Code Every Day (Even 30 Mins)
Consistency builds muscle memory and long-term skills.
2️⃣ Read Other People’s Code
Explore GitHub repos or open-source projects to learn new patterns.
3️⃣ Write Clean, Readable Code
Use meaningful names, proper indentation, and comments.
4️⃣ Review and Refactor
Don’t just finish—improve. Refactor messy code for better logic and performance.
5️⃣ Practice DSA Regularly
Solve at least 1-2 problems a day on LeetCode or HackerRank.
6️⃣ Use Git from Day One
Commit often. It builds discipline and version control skills.
7️⃣ Learn One New Concept Weekly
Could be OOP, error handling, regex, or a new library.
8️⃣ Build Small Projects
Apply what you learn in mini real-world apps—no better way to reinforce skills.
9️⃣ Keep a Code Journal
Write what you learned daily. Great for review and portfolio building.
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| 7 |  𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜 𝗶𝗻 𝟮𝟬𝟮𝟲🚀
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| 8 | Here’s a DSA problem-solving cheat sheet that will help you solve 90–95% of questions that come your way.
♦ If the input is an array or string:
• Is the array sorted?
– Yes: Use Binary Search or Two Pointers.
– No: Move to the next checks.
• What is the question asking?
– Number of ways to do something / Max-Min of something:
▪ If decisions are dependent on each other, use Dynamic Programming.
▪ If decisions are independent, use Greedy.
– Is something possible?
▪ Try Backtracking.
• Does it involve string manipulation?
– Prefix matching: Use Trie.
– Building strings or finding distances: Use Stack or Monotonic Stack.
• Is it about finding a specific element?
– Use a Hash Map or Set.
• Does it involve elements being added/removed in a sliding window fashion?
– Use a Sliding Window or Counting Hash Map.
• Is the problem about continuously finding the max/min element or removing them?
– Use a Heap or Monotonic Queue.
♦ If the input is a graph:
• Does the question involve finding the shortest path or the fewest steps?
– Yes: Use Breadth-First Search (BFS).
– No: Use Depth-First Search (DFS).
♦ If the input is a tree (probably binary):
• Does the question involve specific depths/levels?
– Yes: Use Breadth-First Search (BFS).
– No: Use Depth-First Search (DFS).
♦ If the input is a linked list:
• Does it involve detecting cycles?
– Use Fast and Slow Pointers.
• Does it involve reversing or modifications?
– Use a prev pointer for reversing.
– Use a dummy pointer for maintaining the original head.
This flow will help you quickly identify the optimal approach for most DSA problems.
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| 9 |  🎓 𝗛𝗔𝗥𝗩𝗔𝗥𝗗 𝗨𝗡𝗜𝗩𝗘𝗥𝗦𝗜𝗧𝗬 𝗙𝗥𝗘𝗘 𝗢𝗡𝗟𝗜𝗡𝗘 𝗖𝗢𝗨𝗥𝗦𝗘𝗦 😍
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| 10 | Top 21 skills to learn this year 👇
1. Artificial Intelligence and Machine Learning: Understanding AI algorithms and applications.
2. Data Science: Proficiency in tools like Python/ R, Jupyter Notebook, and GitHub, with the ability to apply data science algorithms to solve real-world problems.
3. Cybersecurity: Protecting data and systems from cyber threats.
4. Cloud Computing: Proficiency in platforms like AWS, Azure, and Google Cloud.
5. Blockchain Technology: Understanding blockchain architecture and applications beyond cryptocurrencies.
6. Digital Marketing: Expertise in SEO, social media, and online advertising.
7. Programming: Skills in languages such as Python, JavaScript, and Go.
8. UX/UI Design: Creating intuitive and effective user interfaces and experiences.
9. Consulting: Expertise in providing strategic advice, improving business processes, and implementing solutions to drive business growth.
10. Data Analysis and Visualization: Proficiency in tools like Excel, SQL, Tableau, and Power BI to analyze and present data effectively.
11. Business Analysis & Project Management: Using tools and methodologies like Agile and Scrum.
12. Remote Work Tools: Proficiency in tools for remote collaboration and productivity.
13. Financial Literacy: Understanding personal finance, investment, and cryptocurrencies.
14. Emotional Intelligence: Skills in empathy, communication, and relationship management.
15. Business Acumen: A deep understanding of how businesses operate, including strategic thinking, market analysis, and financial literacy.
16. Investment Banking: Knowledge of financial markets, valuation methods, mergers and acquisitions, and financial modeling.
17. Mobile App Development: Skills in developing apps for iOS and Android using Swift, Kotlin, or React Native.
18. Financial Management: Proficiency in financial planning, analysis, and tools like QuickBooks and SAP.
19. Web Development: Proficiency in front-end and back-end development using HTML, CSS, JavaScript, and frameworks like React, Angular, and Node.js.
20. Data Engineering: Skills in designing, building, and maintaining data pipelines and architectures using tools like Hadoop, Spark, and Kafka.
21. Soft Skills: Improving leadership, teamwork, and adaptability skills.
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| 11 |  𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀!
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| 12 | Deployment and Real-World Practice
91. What is model deployment?
92. What is batch vs real-time prediction?
93. What is model drift?
94. How do you monitor model performance?
95. What is feature store?
96. What is experiment tracking?
97. How do you explain model predictions?
98. What is data versioning?
99. How do you handle failed models?
100. How do you communicate results to non-technical stakeholders?
Double Tap ♥️ For Detailed Answers | 874 |
| 13 | Top 100 Data Science Interview Questions ✅
Data Science Basics
1. What is data science and how is it different from data analytics?
2. What are the key steps in a data science lifecycle?
3. What types of problems does data science solve?
4. What skills does a data scientist need in real projects?
5. What is the difference between structured and unstructured data?
6. What is exploratory data analysis and why do you do it first?
7. What are common data sources in real companies?
8. What is feature engineering?
9. What is the difference between supervised and unsupervised learning?
10. What is bias in data and how does it affect models?
Statistics and Probability
11. What is the difference between mean, median, and mode?
12. What is standard deviation and variance?
13. What is probability distribution?
14. What is normal distribution and where is it used?
15. What is skewness and kurtosis?
16. What is correlation vs causation?
17. What is hypothesis testing?
18. What are Type I and Type II errors?
19. What is p-value?
20. What is confidence interval?
Data Cleaning and Preprocessing
21. How do you handle missing values?
22. How do you treat outliers?
23. What is data normalization and standardization?
24. When do you use Min-Max scaling vs Z-score?
25. How do you handle imbalanced datasets?
26. What is one-hot encoding?
27. What is label encoding?
28. How do you detect data leakage?
29. What is duplicate data and how do you handle it?
30. How do you validate data quality?
Python for Data Science
31. Why is Python popular in data science?
32. Difference between list, tuple, set, and dictionary?
33. What is NumPy and why is it fast?
34. What is Pandas and where do you use it?
35. Difference between loc and iloc?
36. What are vectorized operations?
37. What is lambda function?
38. What is list comprehension?
39. How do you handle large datasets in Python?
40. What are common Python libraries used in data science?
Data Visualization
41. Why is data visualization important?
42. Difference between bar chart and histogram?
43. When do you use box plots?
44. What does a scatter plot show?
45. What are common mistakes in data visualization?
46. Difference between Seaborn and Matplotlib?
47. What is a heatmap used for?
48. How do you visualize distributions?
49. What is dashboarding?
50. How do you choose the right chart?
Machine Learning Basics
51. What is machine learning?
52. Difference between regression and classification?
53. What is overfitting and underfitting?
54. What is train-test split?
55. What is cross-validation?
56. What is bias-variance tradeoff?
57. What is feature selection?
58. What is model evaluation?
59. What is baseline model?
60. How do you choose a model?
Supervised Learning
61. How does linear regression work?
62. Assumptions of linear regression?
63. What is logistic regression?
64. What is decision tree?
65. What is random forest?
66. What is KNN and when do you use it?
67. What is SVM?
68. How does Naive Bayes work?
69. What are ensemble methods?
70. How do you tune hyperparameters?
Unsupervised Learning
71. What is clustering?
72. Difference between K-means and hierarchical clustering?
73. How do you choose value of K?
74. What is PCA?
75. Why is dimensionality reduction needed?
76. What is anomaly detection?
77. What is association rule mining?
78. What is DBSCAN?
79. What is cosine similarity?
80. Where is unsupervised learning used?
Model Evaluation Metrics
81. What is accuracy and when is it misleading?
82. What is precision and recall?
83. What is F1 score?
84. What is ROC curve?
85. What is AUC?
86. Difference between confusion matrix metrics?
87. What is log loss?
88. What is RMSE?
89. What metric do you use for imbalanced data?
90. How do business metrics link to ML metrics? | 772 |
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| 15 | 💻 100 Days Coding Roadmap 🚀👨💻
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