Coding Projects

📊 𝟱 𝗕𝗲𝘀𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗠𝗦 𝗘𝘅𝗰𝗲𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 Excel is one of the most valuable workplace skills — start learning for FREE today! ✅ Beginner Friendly ✅ Learn at Your Own Pace ✅ Improve Excel & Data Analysis Skills ✅ Useful for Jobs & Interviews ✅ Completely FREE Resources 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/3UkOmoa 🎓 Perfect for Students | Freshers | Data Analyst Aspirants | Working Professionals

Here are some tips to help you craft a standout resume: ### 1. Personalize Your Content - Tailor to the Job: Customize your resume for each job application, highlighting relevant skills and experiences. - Showcase Achievements: Use quantifiable achievements to demonstrate your impact, such as "Increased sales by 20% in six months." - Include a Summary: Start with a strong professional summary that encapsulates your career goals and key strengths. ### 2. Use a Modern Design - Choose a Clean Layout: Opt for a design that is clean and easy to read. Avoid clutter and excessive use of colors. - Incorporate Visuals: Use icons, charts, or infographics to represent skills and achievements visually. - Select a Professional Font: Use modern, professional fonts like Calibri, Arial, or Helvetica. ### 3. Highlight Skills - Soft and Hard Skills: Include both soft skills (like communication and teamwork) and hard skills (like software proficiency). - Keywords: Use industry-specific keywords to pass through applicant tracking systems (ATS). ### 4. Add Unique Sections - Projects: Highlight any notable projects, especially if they are relevant to the job. - Certifications and Courses: Include relevant certifications and any courses that enhance your qualifications. - Volunteer Work: Mention volunteer work or extracurricular activities that show leadership and community involvement. ### 5. Use a Resume Builder or Template - Online Tools: Consider using online tools like Canva, Resume.io, or Zety to create visually appealing resumes. - Templates: Use or modify professionally designed templates to save time and ensure a polished look. ### 6. Proofread and Edit - No Errors: Ensure there are no spelling or grammatical errors. - Consistent Formatting: Maintain consistent formatting for headings, bullet points, and spacing. ### 7. Add a Personal Touch - Cover Letter: Accompany your resume with a personalized cover letter that explains why you're a great fit for the position. - LinkedIn Profile: Include a link to your LinkedIn profile or personal website, ensuring they are up-to-date and aligned with your resume. 👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226 👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5 Like for more ❤️ All the best 👍👍

𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗢𝗻 𝗟𝗮𝘁𝗲𝘀𝘁 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 😍 - AI - Data Analytics - Data Science - CloudComputing - Cyber Security ​ 💫Build a Future Ready Career in the AI Era ​ 💫Learn the Skills, Hiring Trends, and Preparation Strategies That Matter ​ 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 👇:- ​ https://pdlink.in/45w4ztg ​ (Only few slots left ) ​ Date & Time :- 18th August 2026 & 7PM

💻 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 | 𝟱 𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 🚀 Want to learn SQL from scratch to advanced level without spending anything? These 5 YouTube channels offer tutorials, practical examples and problem-solving content. 🔥 Learn → Practice → Build Projects → Prepare for SQL Interviews 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/4wCjU6x 📊 Perfect for Students | Freshers | Data Analyst Aspirants | SQL Beginners

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If you aspire to work in top product companies, here’s my advice: 👉 For SDE-1 or SWE positions, focus on: ✔️ Continuously upskilling and improving your abilities. ✔️ Developing strong problem-solving skills. ✔️Mastering DSA – trust me, you’ll 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. 👉 For higher-level roles (SDE-2 and SDE-3), focus on: ✔️ DSA + System Design (both LLD and HLD). ✔️ Building your leadership skills, as you’ll need to lead teams and projects. 🔸I know it’s challenging to do this while working full-time, but you’ll need to carve out time to consistently upskill yourself. Remember, your learning plan should be sensible and well-organized. Best Programming Resources: https://topmate.io/coding/886839 ENJOY LEARNING 👍👍

📊 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 | 𝟱 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 🚀 Learning Data Analytics? Don't stop with tutorials — build real projects that you can showcase on your resume and portfolio! 💻 🔥 Practice with 5 Hands-On Projects covering: 🗄️ SQL 📊 Excel 📈 Tableau 📉 Power BI 🔗𝗟𝗶𝗻𝗸 👇:-  https://pdlink.in/45LLDH7 🎓 Perfect for Students | Freshers | Data Analyst Aspirants | Beginners

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𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 😍 Build real AI products - not just prompts 🎯 Program Highlights:- 🚀 15+ AI Projects 👨‍🏫 Live Online Classes + 1-on-1 Mentorship 💼 End-to-End Placement Support 🤝 500+ Partner Companies 🎓 2000+ Students Placed 💰 Average Salary: ₹7.4 LPA 🏆 Highest Salary: ₹41 LPA 🔗 𝗕𝗼𝗼𝗸 𝗮 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗖𝗹𝗮𝘀𝘀:- https://pdlink.in/4fWJVID 🔥 Learn AI → Build Real Projects → Create Your Portfolio → Become Job Ready

🇮🇳 𝗙𝗥𝗘𝗘 𝗚𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁-𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🎓 Upgrade your skills with *SWAYAM*, an initiative by the Government of India! ✅ Learn from leading institutes and expert educators ✅ Courses in AI, Programming, Data Science, Business & more ✅ Suitable for students, freshers and professionals ✅ Learn online at your own pace ✅ Strengthen your résumé with valuable certifications 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/4gc1MKx 📢 Share this opportunity with your friends and classmates!

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🚀 𝗚𝗼𝗼𝗴𝗹𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟲 🎓 Want to upgrade your resume with Google skills and certifications Explore FREE learning opportunities and build in-demand skills for today's job market. 👉Artificial Intelligence & Generative AI 📊 Data Analytics ☁️ Cloud Computing 📢 Digital Marketing 🔐 Cybersecurity 💻 Tech & Career Skills 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/4z9pdgf 🔥 Don't just collect certificates — build skills that can help you stand out in 2026!

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An LRU Cache removes the item that has not been used for the longest time when the cache reaches its capacity. A common implementation uses: • Hash Map for O(1) lookup. • Doubly Linked List for O(1) insertion and removal. Time Complexity: • Get: O(1) • Put: O(1) 2️⃣0️⃣0️⃣ How Would You Design a URL Shortener? Answer: A URL shortener converts a long URL into a short, unique URL. Example: Long URL: https://example.com/products/category/item/12345 Short URL: https://short.ly/aB92x A basic system can use: 1. Generate a unique ID for each URL. 2. Convert the ID into a short Base62 string. 3. Store the mapping between the short code and original URL. 4. When the short URL is requested, look up the original URL. 5. Redirect the user to the original URL. Important Design Considerations: • Unique short IDs • Database design • Caching • Scalability • High availability • Expiration of URLs • Analytics and click tracking 🔥 Double Tap ❤️ For More ----- 2.3 ₽ · /balance_help

🚀 Coding Interview Questions with Answers (Part 20) 1️⃣9️⃣1️⃣ What is the Two Sum Problem? Answer: The Two Sum problem asks you to find two elements in an array whose sum equals a given target. Example: Input: [2][7][11][15] Target: 9 Output: [2][7] A Hash Map can be used to store previously seen values and find the required complement efficiently. Time Complexity: O(n) Space Complexity: O(n) 1️⃣9️⃣2️⃣ What is the Longest Substring Without Repeating Characters Problem? Answer: The goal is to find the longest substring that contains no repeated characters. Example: Input: "abcbb" Output: 3 The longest substring is "abc". A Sliding Window with a Hash Set or Hash Map can solve this efficiently. Time Complexity: O(n) Space Complexity: O(k) 1️⃣9️⃣3️⃣ What is the Longest Common Subsequence (LCS) Problem? Answer: LCS finds the longest sequence that appears in the same order in two strings, but the characters do not need to be adjacent. Example: Input: "abcde" and "ace" Output: "ace" Dynamic Programming is commonly used to solve this problem. Time Complexity: O(m × n) Space Complexity: O(m × n) 1️⃣9️⃣4️⃣ What is the Longest Increasing Subsequence (LIS) Problem? Answer: LIS finds the longest subsequence of an array where the elements are in strictly increasing order. Example: Input: [10][9][2][5][3][7][101][18] Output: 4 One possible LIS is: [2][3][7][101] It can be solved using Dynamic Programming or an optimized Binary Search approach. Time Complexity: O(n log n) using the optimized approach. 1️⃣9️⃣5️⃣ What is the Maximum Subarray Sum Problem? Answer: The goal is to find the contiguous subarray with the largest possible sum. Example: Input: [-2][1][-3][4][-1][2][1][-5][4] Output: 6 The maximum-sum subarray is: [4][-1][2][1] Kadane's Algorithm can solve this efficiently. Time Complexity: O(n) Space Complexity: O(1) 1️⃣9️⃣6️⃣ What is the Merge Intervals Problem? Answer: The Merge Intervals problem requires combining overlapping intervals into a single interval. Example: Input: [[1,3][2,6][8,10][9,12]] Output: [[1,6][8,12]] The typical approach is to sort the intervals by their starting value and then merge overlapping intervals. Time Complexity: O(n log n) Space Complexity: O(n) 1️⃣9️⃣7️⃣ What is the Trapping Rain Water Problem? Answer: The problem asks you to calculate how much rainwater can be trapped between bars of different heights. Example: Input: [0][1][0][2][1][0][1][3][2][1][2][1] Output: 6 A Two Pointers approach can solve this problem efficiently by tracking the maximum height from both sides. Time Complexity: O(n) Space Complexity: O(1) 1️⃣9️⃣8️⃣ What is the Median of Two Sorted Arrays Problem? Answer: The goal is to find the median of two sorted arrays without necessarily merging them completely. Example: Input: [1][3] and [2] Output: 2 An optimized solution uses Binary Search to partition the two arrays correctly. Time Complexity: O(log(min(m,n))) Space Complexity: O(1) 1️⃣9️⃣9️⃣ What is the LRU Cache Problem? Answer: LRU stands for Least Recently Used.

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Time Complexity: O(n + m) Space Complexity: O(n + m) 1️⃣8️⃣9️⃣ How Do You Check if Two Strings are Anagrams? Answer: Two strings are anagrams if they contain the same characters with the same frequencies, but possibly in a different order. Example: "listen" → "silent" Both contain the same characters, so they are anagrams. Python:

str1 = "listen"
str2 = "silent"

if sorted(str1) == sorted(str2):
    print("Anagrams")
else:
    print("Not Anagrams")

Time Complexity: O(n log n) A frequency-count approach can achieve O(n) average time. 1️⃣9️⃣0️⃣ How Do You Find the First Non-Repeating Character? Answer: Count the frequency of every character, then scan the string again and return the first character whose frequency is "1". Example: Input: "swiss" Output: "w" Python:

from collections import Counter

text = "swiss"
count = Counter(text)

for char in text:
    if count[char] == 1:
        print(char)
        break

Time Complexity: O(n) Space Complexity: O(k), where "k" is the number of distinct characters. 🔥 Double Tap ❤️ For Part-20 ----- 2.47 ₽ · /balance_help

🚀 Coding Interview Questions with Answers (Part 19) 1️⃣8️⃣1️⃣ How Do You Reverse a String? Answer: Reversing a string means arranging its characters in the opposite order. Example: Input: "hello" Output: "olleh" Python:

text = "hello"
reversed_text = text[::-1]
print(reversed_text)

Time Complexity: O(n) Space Complexity: O(n) 1️⃣8️⃣2️⃣ How Do You Find the Largest Element in an Array? Answer: Traverse the array while keeping track of the largest value found so far. Example: Input: [10, 25, 7, 42, 18] Output: 42 Python:

numbers = [10, 25, 7, 42, 18]

largest = numbers[0]

for num in numbers:
    if num > largest:
        largest = num

print(largest)

Time Complexity: O(n) Space Complexity: O(1) 1️⃣8️⃣3️⃣ How Do You Find the Second Largest Element in an Array? Answer: Maintain two variables: one for the largest element and another for the second largest. Update them while traversing the array. Example: Input: [10, 25, 7, 42, 18] Output: 25 Python:

numbers = [10, 25, 7, 42, 18]

largest = second = float('-inf')

for num in numbers:
    if num > largest:
        second = largest
        largest = num
    elif largest > num > second:
        second = num

print(second)

Time Complexity: O(n) Space Complexity: O(1) 1️⃣8️⃣4️⃣ How Do You Check Whether a String is a Palindrome? Answer: A palindrome is a string that reads the same forward and backward. Examples: "madam" → Palindrome "level" → Palindrome "hello" → Not a palindrome Python:

text = "madam"

if text == text[::-1]:
    print("Palindrome")
else:
    print("Not a palindrome")

Time Complexity: O(n) 1️⃣8️⃣5️⃣ How Do You Find Duplicate Elements in an Array? Answer: Use a set to keep track of elements that have already appeared. If an element is already present in the set, it is a duplicate. Example: Input: [1, 2, 3, 2, 4, 1] Output: [1, 2] Python:

numbers = [1, 2, 3, 2, 4, 1]

seen = set()
duplicates = set()

for num in numbers:
    if num in seen:
        duplicates.add(num)
    else:
        seen.add(num)

print(duplicates)

Average Time Complexity: O(n) Space Complexity: O(n) 1️⃣8️⃣6️⃣ How Do You Remove Duplicates from an Array? Answer: A common approach is to use a set, which stores only unique values. Example: Input: [1, 2, 2, 3, 3, 4] Output: [1, 2, 3, 4] Python:

numbers = [1, 2, 2, 3, 3, 4]

unique_numbers = list(set(numbers))

print(unique_numbers)

If the original order must be preserved:

unique_numbers = list(dict.fromkeys(numbers))

Average Time Complexity: O(n) 1️⃣8️⃣7️⃣ How Do You Find the Missing Number in an Array? Answer: If an array contains numbers from "1" to "n" with one number missing, calculate the expected sum and subtract the actual sum. Example: Input: [1, 2, 4, 5] Output: 3 Python:

numbers = [1, 2, 4, 5]
n = 5

expected = n * (n + 1) // 2
missing = expected - sum(numbers)

print(missing)

Time Complexity: O(n) Space Complexity: O(1) 1️⃣8️⃣8️⃣ How Do You Merge Two Sorted Arrays? Answer: Use two pointers to compare elements from both arrays and add the smaller element to the result. Example: Input: [1, 3, 5] [2, 4, 6] Output: [1, 2, 3, 4, 5, 6] Python:

a = [1, 3, 5]
b = [2, 4, 6]

i = j = 0
result = []

while i < len(a) and j < len(b):
    if a[i] < b[j]:
        result.append(a[i])
        i += 1
    else:
        result.append(b[j])
        j += 1

while i < len(a):
    result.append(a[i])
    i += 1

while j < len(b):
    result.append(b[j])
    j += 1

print(result)