Tips for solving leetcode codings interview problems

If input array is sorted then
- Binary search
- Two pointers

If asked for all permutations/subsets then
- Backtracking

If given a tree then
- DFS
- BFS

If given a graph then
- DFS
- BFS

If given a linked list then
- Two pointers

If recursion is banned then
- Stack

If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer

If asked for maximum/minimum subarray/subset/options then
- Dynamic programming

If asked for top/least K items then
- Heap

If asked for common strings then
- Map
- Trie

Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space

𝗙𝗿𝗲𝗲 𝗔𝗜 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀😍

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Top free Data Science resources

1. CS109 Data Science
http://cs109.github.io/2015/pages/videos.html

2. Machine Learning with Python
https://www.freecodecamp.org/learn/machine-learning-with-python/

3. Learning From Data from California Institute of Technology
http://work.caltech.edu/telecourse

4. Mathematics for Machine Learning by University of California, Berkeley
https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI

5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY

6. Python Data Science Handbook
https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM

7.  CS 221 ― Artificial Intelligence
https://stanford.edu/~shervine/teaching/cs-221/

8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science
https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf

9. Python for Data Analysis by Boston University
https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx

10.  Data Mining bu University of Buffalo
https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE

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Hey guys,

Today, let’s talk about some of the Python questions you might face during a data analyst interview. Below, I’ve compiled the most commonly asked Python questions you should be prepared for in your interviews.

1. Why is Python used in data analysis?

Python is popular for data analysis due to its simplicity, readability, and vast ecosystem of libraries like Pandas, NumPy, Matplotlib, and Scikit-learn. It allows for quick prototyping, data manipulation, and visualization. Moreover, Python integrates seamlessly with other tools like SQL, Excel, and cloud platforms, making it highly versatile for both small-scale analysis and large-scale data engineering.

2. What are the essential libraries used for data analysis in Python?

Some key libraries you’ll use frequently are:

- Pandas: For data manipulation and analysis. It provides data structures like DataFrames, which are perfect for handling tabular data.
- NumPy: For numerical operations. It supports arrays and matrices and includes mathematical functions.
- Matplotlib/Seaborn: For data visualization. Matplotlib allows for creating static, interactive, and animated visualizations, while Seaborn makes creating complex plots easier.
- Scikit-learn: For machine learning. It provides tools for data mining and analysis.

3. What is a Python dictionary, and how is it used in data analysis?

A dictionary in Python is an unordered collection of key-value pairs. It’s extremely useful in data analysis for storing mappings (like labels to corresponding values) or for quick lookups.

Example:

sales = {"January": 12000, "February": 15000, "March": 17000}
print(sales["February"])  # Output: 15000

4. Explain the difference between a list and a tuple in Python.

- List: Mutable, meaning you can modify (add, remove, or change) elements. It’s written in square brackets [ ].

Example:

  my_list = [10, 20, 30]
  my_list.append(40)
  

- Tuple: Immutable, meaning once defined, you cannot modify it. It’s written in parentheses ( ).

Example:

  my_tuple = (10, 20, 30)
  

5. How would you handle missing data in a dataset using Python?

Handling missing data is critical in data analysis, and Python’s Pandas library makes it easy. Here are some common methods:

- Drop missing data:

  df.dropna()
  

- Fill missing data with a specific value:

  df.fillna(0)
  

- Forward-fill or backfill missing values:

  df.fillna(method='ffill')  # Forward-fill
  df.fillna(method='bfill')  # Backfill
  

6. How do you merge/join two datasets in Python?

- pd.merge(): For SQL-style joins (inner, outer, left, right).

  df_merged = pd.merge(df1, df2, on='common_column', how='inner')
  

- pd.concat(): For concatenating along rows or columns.

  df_concat = pd.concat([df1, df2], axis=1)

7. What is the purpose of lambda functions in Python?

A lambda function is an anonymous, single-line function that can be used for quick, simple operations. They are useful when you need a short, throwaway function.

Example:

add = lambda x, y: x + y
print(add(10, 20))  # Output: 30

Lambdas are often used in data analysis for quick transformations or filtering operations within functions like map() or filter().

If you’re preparing for interviews, focus on writing clean, optimized code and understand how Python fits into the larger data ecosystem.

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𝗪𝗮𝗻𝘁 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗧𝗵𝗮𝘁 𝗚𝗲𝘁𝘀 𝗬𝗼𝘂 𝗛𝗶𝗿𝗲𝗱?😍

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├── 🧠 Why TypeScript? JavaScript with Superpowers
├── ⚙️ Setting up TypeScript (tsc, tsconfig)
├── 🔡 Type Annotations (number, string, boolean, etc.)
├── 📦 Interfaces & Type Aliases
├── 🧱 Classes, Inheritance & Access Modifiers
├── 🔁 Generics
├── ❌ Type Narrowing & Type Guards
├── 🔄 Enums, Tuples & Union Types
├── 🧩 Modules & Namespaces
├── 🔧 Working with TypeScript & React/Vue
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🧠 Technologies for Data Science, Machine Learning & AI!

📊 Data Science
▪️ Python – The go-to language for Data Science
▪️ R – Statistical Computing and Graphics
▪️ Pandas – Data Manipulation & Analysis
▪️ NumPy – Numerical Computing
▪️ Matplotlib / Seaborn – Data Visualization
▪️ Jupyter Notebooks – Interactive Development Environment

🤖 Machine Learning
▪️ Scikit-learn – Classical ML Algorithms
▪️ TensorFlow – Deep Learning Framework
▪️ Keras – High-Level Neural Networks API
▪️ PyTorch – Deep Learning with Dynamic Computation
▪️ XGBoost – High-Performance Gradient Boosting
▪️ LightGBM – Fast, Distributed Gradient Boosting

🧠 Artificial Intelligence
▪️ OpenAI GPT – Natural Language Processing
▪️ Transformers (Hugging Face) – Pretrained Models for NLP
▪️ spaCy – Industrial-Strength NLP
▪️ NLTK – Natural Language Toolkit
▪️ Computer Vision (OpenCV) – Image Processing & Object Detection
▪️ YOLO (You Only Look Once) – Real-Time Object Detection

💾 Data Storage & Databases
▪️ SQL – Structured Query Language for Databases
▪️ MongoDB – NoSQL, Flexible Data Storage
▪️ BigQuery – Google’s Data Warehouse for Large Scale Data
▪️ Apache Hadoop – Distributed Storage and Processing
▪️ Apache Spark – Big Data Processing & ML

🌐 Data Engineering & Deployment
▪️ Apache Airflow – Workflow Automation & Scheduling
▪️ Docker – Containerization for ML Models
▪️ Kubernetes – Container Orchestration
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▪️ Flask / FastAPI – APIs for ML Models

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𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗝𝘂𝘀𝘁 𝗥𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝟱 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻’𝘁 𝗠𝗶𝘀𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱!😍

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Here is an A-Z list of essential programming terms:

1. Array: A data structure that stores a collection of elements of the same type in contiguous memory locations.

2. Boolean: A data type that represents true or false values.

3. Conditional Statement: A statement that executes different code based on a condition.

4. Debugging: The process of identifying and fixing errors or bugs in a program.

5. Exception: An event that occurs during the execution of a program that disrupts the normal flow of instructions.

6. Function: A block of code that performs a specific task and can be called multiple times in a program.

7. GUI (Graphical User Interface): A visual way for users to interact with a computer program using graphical elements like windows, buttons, and menus.

8. HTML (Hypertext Markup Language): The standard markup language used to create web pages.

9. Integer: A data type that represents whole numbers without any fractional part.

10. JSON (JavaScript Object Notation): A lightweight data interchange format commonly used for transmitting data between a server and a web application.

11. Loop: A programming construct that allows repeating a block of code multiple times.

12. Method: A function that is associated with an object in object-oriented programming.

13. Null: A special value that represents the absence of a value.

14. Object-Oriented Programming (OOP): A programming paradigm based on the concept of "objects" that encapsulate data and behavior.

15. Pointer: A variable that stores the memory address of another variable.

16. Queue: A data structure that follows the First-In-First-Out (FIFO) principle.

17. Recursion: A programming technique where a function calls itself to solve a problem.

18. String: A data type that represents a sequence of characters.

19. Tuple: An ordered collection of elements, similar to an array but immutable.

20. Variable: A named storage location in memory that holds a value.

21. While Loop: A loop that repeatedly executes a block of code as long as a specified condition is true.

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𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗙𝗮𝘀𝘁: 𝗟𝗲𝗮𝗿𝗻 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝘁𝗵 𝗣𝗿𝗼𝗷𝗲𝗰𝘁-𝗕𝗮𝘀𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟯𝟬 𝗗𝗮𝘆𝘀!😍

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🚀 Roadmap to Become a C++ Developer 🔰

📂 Programming Basics
 ∟📂 Master C++ Syntax, Variables & Data Types
  ∟📂 Learn Control Flow, Loops & Functions
   ∟📂 Practice with Simple Programs

📂 Object-Oriented Programming (OOP)
 ∟📂 Understand Classes, Objects & Inheritance
  ∟📂 Dive into Encapsulation, Polymorphism & Abstraction
   ∟📂 Explore Templates & the Standard Template Library (STL)

📂 Memory Management & Pointers
 ∟📂 Grasp Pointers, References & Dynamic Memory Allocation
  ∟📂 Master Manual Memory Management
   ∟📂 Learn Smart Pointers & RAII Principles

📂 Data Structures & Algorithms
 ∟📂 Study Arrays, Vectors, Lists, Maps & Sets
  ∟📂 Understand Sorting, Searching & Recursion
   ∟📂 Solve Coding Challenges to Reinforce Concepts

📂 Tools & Build Systems
 ∟📂 Get Comfortable with IDEs (e.g., Visual Studio, CLion)
  ∟📂 Learn CMake & Other Build Tools
   ∟📂 Master Git & Version Control Systems

📂 Advanced C++ Concepts
 ∟📂 Explore Lambda Functions & Modern C++ Features
  ∟📂 Understand Multithreading & Concurrency
   ∟📂 Dive into Performance Optimization & Best Practices

📂 Debugging & Testing
 ∟📂 Learn Debugging Techniques & Tools
  ∟📂 Master Unit Testing with Frameworks (e.g., Google Test)
   ∟📂 Analyze and Optimize Code Performance

📂 Projects & Real-World Applications
 ∟📂 Build Complex, End-to-End C++ Applications
  ∟📂 Contribute to Open-Source Projects
   ∟📂 Showcase Your Work on GitHub & Portfolio

📂 Interview Preparation & Job Hunting
 ∟📂 Solve C++ Coding Challenges
  ∟📂 Master Data Structures, Algorithms & System Design
   ∟📂 Network & Apply for C++ Roles

✅️ Get Hired

React "❤️" for More 👨‍💻

𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁’𝘀 𝗙𝗥𝗘𝗘 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 – 𝗟𝗲𝗮𝗿𝗻 𝗛𝗼𝘄 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗪𝗼𝗿𝗸𝘀😍

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