9 things to do when you’re stuck in coding:
🔍 Read the error message carefully — it often tells you the issue
✍️ Rubber duck debugging — explain your code out loud
🧩 Break the problem into smaller parts
🧠 Revisit the logic — not just the syntax
❓ Google the error or issue with specific keywords
🛠️ Use console logs or print statements to trace the flow
⏸️ Take a short break — come back with a fresh mind
👥 Ask for help — forums, friends, or mentors
📖 Check the official documentation or trusted sources
#coding #tips
🔍 Read the error message carefully — it often tells you the issue
✍️ Rubber duck debugging — explain your code out loud
🧩 Break the problem into smaller parts
🧠 Revisit the logic — not just the syntax
❓ Google the error or issue with specific keywords
🛠️ Use console logs or print statements to trace the flow
⏸️ Take a short break — come back with a fresh mind
👥 Ask for help — forums, friends, or mentors
📖 Check the official documentation or trusted sources
#coding #tips
👍7❤2
Essential Python Libraries for Data Science
- Numpy: Fundamental for numerical operations, handling arrays, and mathematical functions.
- SciPy: Complements Numpy with additional functionalities for scientific computing, including optimization and signal processing.
- Pandas: Essential for data manipulation and analysis, offering powerful data structures like DataFrames.
- Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations.
- Keras: A high-level neural networks API, facilitating rapid prototyping and experimentation in deep learning.
- TensorFlow: An open-source machine learning framework widely used for building and training deep learning models.
- Scikit-learn: Provides simple and efficient tools for data mining, machine learning, and statistical modeling.
- Seaborn: Built on Matplotlib, Seaborn enhances data visualization with a high-level interface for drawing attractive and informative statistical graphics.
- Statsmodels: Focuses on estimating and testing statistical models, providing tools for exploring data, estimating models, and statistical testing.
- NLTK (Natural Language Toolkit): A library for working with human language data, supporting tasks like classification, tokenization, stemming, tagging, parsing, and more.
These libraries collectively empower data scientists to handle various tasks, from data preprocessing to advanced machine learning implementations.
ENJOY LEARNING 👍👍
- Numpy: Fundamental for numerical operations, handling arrays, and mathematical functions.
- SciPy: Complements Numpy with additional functionalities for scientific computing, including optimization and signal processing.
- Pandas: Essential for data manipulation and analysis, offering powerful data structures like DataFrames.
- Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations.
- Keras: A high-level neural networks API, facilitating rapid prototyping and experimentation in deep learning.
- TensorFlow: An open-source machine learning framework widely used for building and training deep learning models.
- Scikit-learn: Provides simple and efficient tools for data mining, machine learning, and statistical modeling.
- Seaborn: Built on Matplotlib, Seaborn enhances data visualization with a high-level interface for drawing attractive and informative statistical graphics.
- Statsmodels: Focuses on estimating and testing statistical models, providing tools for exploring data, estimating models, and statistical testing.
- NLTK (Natural Language Toolkit): A library for working with human language data, supporting tasks like classification, tokenization, stemming, tagging, parsing, and more.
These libraries collectively empower data scientists to handle various tasks, from data preprocessing to advanced machine learning implementations.
ENJOY LEARNING 👍👍
👍6
How to get job as python fresher?
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
👍9❤2🤝1
Here's the A–Z list of essential Python programming concepts
A - Arguments
B - Built-in Functions
C - Comprehensions
D - Dictionaries
E - Exceptions
F - Functions
G - Generators
H - Higher-Order Functions
I - Iterators
J - Join Method
K - Keyword Arguments
L - Lambda Functions
M - Modules
N - NoneType
O - Object-Oriented Programming
P - PEP8
Q - Queue
R - Range Function
S - Sets
T - Tuples
U - Unpacking
V - Variables
W - While Loop
X - XOR Operation
Y - Yield Keyword
Z - Zip Function
These concepts are foundational to mastering Python and writing clean, efficient, and Pythonic code.
Credits: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
A - Arguments
B - Built-in Functions
C - Comprehensions
D - Dictionaries
E - Exceptions
F - Functions
G - Generators
H - Higher-Order Functions
I - Iterators
J - Join Method
K - Keyword Arguments
L - Lambda Functions
M - Modules
N - NoneType
O - Object-Oriented Programming
P - PEP8
Q - Queue
R - Range Function
S - Sets
T - Tuples
U - Unpacking
V - Variables
W - While Loop
X - XOR Operation
Y - Yield Keyword
Z - Zip Function
These concepts are foundational to mastering Python and writing clean, efficient, and Pythonic code.
Credits: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
👍8
Machine Learning Algorithms every data scientist should know:
📌 Supervised Learning:
🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression
🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)
📌 Unsupervised Learning:
🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN
🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)
📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)
📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking
Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
📌 Supervised Learning:
🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression
🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)
📌 Unsupervised Learning:
🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN
🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)
📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)
📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking
Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
❤5👍2
The best doesn't come from working more.
It comes from working smarter.
The most common mistakes people make,
With practical tips to avoid each:
1) Working late every night.
• Prioritize quality time with loved ones.
Understand that long hours won't be remembered as fondly as time spent with family and friends.
2) Believing more hours mean more productivity.
• Focus on efficiency.
Complete tasks in less time to free up hours for personal activities and rest.
3) Ignoring the need for breaks.
• Take regular breaks to rejuvenate your mind.
Creativity and productivity suffer without proper rest.
4) Sacrificing personal well-being.
• Maintain a healthy work-life balance.
Ensure you don't compromise your health or relationships for work.
5) Feeling pressured to constantly produce.
• Quality over quantity.
6) Neglecting hobbies and interests.
• Engage in activities you love outside of work.
This helps to keep your mind fresh and inspired.
7) Failing to set boundaries.
• Set clear work hours and stick to them.
This helps to prevent overworking and ensures you have time for yourself.
8) Not delegating tasks.
• Delegate when possible.
Sharing the workload can enhance productivity and give you more free time.
9) Overlooking the importance of sleep.
• Prioritize sleep for better performance.
A well-rested mind is more creative and effective.
10) Underestimating the impact of overworking.
• Recognize the long-term effects.
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://t.iss.one/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍 👍
It comes from working smarter.
The most common mistakes people make,
With practical tips to avoid each:
1) Working late every night.
• Prioritize quality time with loved ones.
Understand that long hours won't be remembered as fondly as time spent with family and friends.
2) Believing more hours mean more productivity.
• Focus on efficiency.
Complete tasks in less time to free up hours for personal activities and rest.
3) Ignoring the need for breaks.
• Take regular breaks to rejuvenate your mind.
Creativity and productivity suffer without proper rest.
4) Sacrificing personal well-being.
• Maintain a healthy work-life balance.
Ensure you don't compromise your health or relationships for work.
5) Feeling pressured to constantly produce.
• Quality over quantity.
6) Neglecting hobbies and interests.
• Engage in activities you love outside of work.
This helps to keep your mind fresh and inspired.
7) Failing to set boundaries.
• Set clear work hours and stick to them.
This helps to prevent overworking and ensures you have time for yourself.
8) Not delegating tasks.
• Delegate when possible.
Sharing the workload can enhance productivity and give you more free time.
9) Overlooking the importance of sleep.
• Prioritize sleep for better performance.
A well-rested mind is more creative and effective.
10) Underestimating the impact of overworking.
• Recognize the long-term effects.
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://t.iss.one/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍 👍
👍4❤1
Python Detailed Roadmap 🚀
📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)
📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules
📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON
📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation
📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions
📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators
📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)
📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)
📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI
📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)
📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub
📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews
Like for more ❤️💪
📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)
📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules
📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON
📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation
📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions
📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators
📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)
📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)
📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI
📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)
📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub
📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews
Like for more ❤️💪
👍9❤5
If I Were to Start My Data Science Career from Scratch, Here's What I Would Do 👇
1️⃣ Master Advanced SQL
Foundations: Learn database structures, tables, and relationships.
Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.
Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.
JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.
Advanced Concepts: CTEs, window functions, and query optimization.
Metric Development: Build and report metrics effectively.
2️⃣ Study Statistics & A/B Testing
Descriptive Statistics: Know your mean, median, mode, and standard deviation.
Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.
Probability: Understand basic probability and Bayes' theorem.
Intro to ML: Start with linear regression, decision trees, and K-means clustering.
Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.
A/B Testing: Design experiments—hypothesis formation, sample size calculation, and sample biases.
3️⃣ Learn Python for Data
Data Manipulation: Use pandas for data cleaning and manipulation.
Data Visualization: Explore matplotlib and seaborn for creating visualizations.
Hypothesis Testing: Dive into scipy for statistical testing.
Basic Modeling: Practice building models with scikit-learn.
4️⃣ Develop Product Sense
Product Management Basics: Manage projects and understand the product life cycle.
Data-Driven Strategy: Leverage data to inform decisions and measure success.
Metrics in Business: Define and evaluate metrics that matter to the business.
5️⃣ Hone Soft Skills
Communication: Clearly explain data findings to technical and non-technical audiences.
Collaboration: Work effectively in teams.
Time Management: Prioritize and manage projects efficiently.
Self-Reflection: Regularly assess and improve your skills.
6️⃣ Bonus: Basic Data Engineering
Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.
ETL: Set up extraction jobs, manage dependencies, clean and validate data.
Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content 😄👍
1️⃣ Master Advanced SQL
Foundations: Learn database structures, tables, and relationships.
Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.
Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.
JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.
Advanced Concepts: CTEs, window functions, and query optimization.
Metric Development: Build and report metrics effectively.
2️⃣ Study Statistics & A/B Testing
Descriptive Statistics: Know your mean, median, mode, and standard deviation.
Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.
Probability: Understand basic probability and Bayes' theorem.
Intro to ML: Start with linear regression, decision trees, and K-means clustering.
Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.
A/B Testing: Design experiments—hypothesis formation, sample size calculation, and sample biases.
3️⃣ Learn Python for Data
Data Manipulation: Use pandas for data cleaning and manipulation.
Data Visualization: Explore matplotlib and seaborn for creating visualizations.
Hypothesis Testing: Dive into scipy for statistical testing.
Basic Modeling: Practice building models with scikit-learn.
4️⃣ Develop Product Sense
Product Management Basics: Manage projects and understand the product life cycle.
Data-Driven Strategy: Leverage data to inform decisions and measure success.
Metrics in Business: Define and evaluate metrics that matter to the business.
5️⃣ Hone Soft Skills
Communication: Clearly explain data findings to technical and non-technical audiences.
Collaboration: Work effectively in teams.
Time Management: Prioritize and manage projects efficiently.
Self-Reflection: Regularly assess and improve your skills.
6️⃣ Bonus: Basic Data Engineering
Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.
ETL: Set up extraction jobs, manage dependencies, clean and validate data.
Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content 😄👍
👍7❤3
Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.iss.one/free4unow_backup
ENJOY LEARNING 👍👍
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.iss.one/free4unow_backup
ENJOY LEARNING 👍👍
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Top 5 data science projects for freshers
1. Predictive Analytics on a Dataset:
- Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.
2. Customer Segmentation:
- Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.
3. Sentiment Analysis on Social Media Data:
- Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.
4. Recommendation System:
- Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.
5. Fraud Detection:
- Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.
Free Datsets -> https://t.iss.one/DataPortfolio/2?single
These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.
Join @pythonspecialist for more data science projects
1. Predictive Analytics on a Dataset:
- Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.
2. Customer Segmentation:
- Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.
3. Sentiment Analysis on Social Media Data:
- Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.
4. Recommendation System:
- Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.
5. Fraud Detection:
- Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.
Free Datsets -> https://t.iss.one/DataPortfolio/2?single
These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.
Join @pythonspecialist for more data science projects
👍4❤2
Python Learning Plan in 2025
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
👍9❤4