Python for Data Science

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Software development is complex, and the fancy names don't help.

Hashing vs. Encryption vs. Encoding


𝗛𝗮𝘀𝗵𝗶𝗻𝗴

This is a one-way process used for data integrity verification.

When you hash data, you get a unique string representing the original data.

It's a one-way street; once you hash something, you can't get the original data back from the hash.

While multiple values can theoretically yield the same hash, well-crafted cryptographic hash functions make such collisions incredibly rare and nearly impossible to compute.

This property makes it perfect for verifying if someone altered the data.

If even one-bit changes in the original data, the hash changes dramatically.


𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻

This is the real deal when it comes to data security.

It uses algorithms and keys to transform readable data (plaintext) into an unreadable format (ciphertext).

Only those with the correct key can unlock (decrypt) the data and read it.

This process is reversible, unlike hashing.

Encryption is critical for protecting sensitive data from unauthorized access.


𝗘𝗻𝗰𝗼𝗱𝗶𝗻𝗴

This is all about data representation.

It converts data from one format to another, making it easier to interpret and display.

Common formats:

• Base64
• UTF-8
• ASCII

Encoding does NOT provide security! It's for data transmission and storage convenience.


One common use of hashing is for secure password storage.

When you create an account or set a password, the system hashes and stores the password in the database.

During login, the system hashes the provided password and compares it to the stored hash without revealing the password.

𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗔𝗜 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗕𝘆 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁’𝘀 𝗦𝗲𝗻𝗶𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁😍

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12 most useful CSS functions

𝟰 𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗖𝗼𝗱𝗶𝗻𝗴 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗿𝗼 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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9 tips to get better at debugging code:

Read error messages carefully — they often tell you everything

Use print/log statements to trace code execution

Check one small part at a time

Reproduce the bug consistently

Use a debugger to step through code line by line

Compare working vs broken code

Check for typos, null values, and off-by-one errors

Rubber duck debugging — explain your code out loud

Take breaks — fresh eyes spot bugs faster

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Here are some interview preparation tips 👇👇

Technical Interview
1. Review Core Concepts:
  - Data Structures: Be comfortable with LinkedLists, Trees, Graphs, and their representations.
  - Algorithms: Brush up on searching and sorting algorithms, time complexities, and common algorithms (like Dijkstra’s or A*).
  - Programming Languages: Ensure you understand the language you are most comfortable with (e.g., C++, Java, Python) and know its standard library functions.

2. Practice Coding Problems:
  - Utilize platforms like LeetCode, HackerRank, or CodeSignal to practice medium-level coding questions. Focus on common patterns and problem-solving strategies.

3. Mock Interviews: Conduct mock technical interviews with peers or mentors to build confidence and receive feedback.

Personal Interview
1. Prepare Your Story:
  - Outline your educational journey, achievements, and any relevant projects. Emphasize experiences that demonstrate leadership, teamwork, and problem-solving skills.
  - Be ready to discuss your challenges and how you overcame them.

2. Articulate Your Goals:
  - Be clear about why you want to join the program and how it aligns with your career aspirations. Reflect on what you hope to gain from the experience.

- Focus on Fundamentals:
Be thorough with basic subjects like Operating Systems, Networking, OOP, and Databases. Clear concepts are key for technical interviews.

2. Common Interview Questions:

DSA:
- Implement various data structures like Linked Lists, Trees, Graphs, Stacks, and Queues.
- Understand searching and sorting algorithms: Binary Search, Merge Sort, Quick Sort, etc.
- Solve problems involving HashMaps, Sets, and other collections.

Sample DSA Questions
- Reverse a linked list.
- Find the first non-repeating character in a string.
- Detect a cycle in a graph.
- Implement a queue using two stacks.
- Find the lowest common ancestor in a binary tree.
 
3. Key Topics to Focus On

DSA:
- Arrays, Strings, Linked Lists, Trees, Graphs
- Recursion, Backtracking, Dynamic Programming
- Sorting and Searching Algorithms
- Time and Space Complexity

Core Subjects
- Operating Systems: Concepts like processes, threads, deadlocks, concurrency, and memory management.
- Database Management Systems (DBMS): Understanding SQL, Normalization, and database design.
- Object-Oriented Programming (OOP): Know about inheritance, polymorphism, encapsulation, and design patterns.
 
5. Tips
- Optimize Your Code: Write clean, optimized code. Discuss time and space complexities during interviews.
- Review Your Projects: Be ready to explain your past projects, the challenges you faced, and the technologies you used.....

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All the best 👍👍

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Best Resources for Tech Interviews

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👆 40 Project Ideas for Web Developers