๐งฟ Boost React Performance
Performance bottlenecks in React often come from unnecessary re-renders and poor state management. Hereโs a straightforward guide to optimizing your React apps.
โค1
Roadmap to become a Programmer:
๐ Learn Programming Fundamentals (Logic, Syntax, Flow)
โ๐ Choose a Language (Python / Java / C++)
โ๐ Learn Data Structures & Algorithms
โ๐ Learn Problem Solving (LeetCode / HackerRank)
โ๐ Learn OOPs & Design Patterns
โ๐ Learn Version Control (Git & GitHub)
โ๐ Learn Debugging & Testing
โ๐ Work on Real-World Projects
โ๐ Contribute to Open Source
โโ Apply for Job / Internship
React โค๏ธ for More ๐ก
๐ Learn Programming Fundamentals (Logic, Syntax, Flow)
โ๐ Choose a Language (Python / Java / C++)
โ๐ Learn Data Structures & Algorithms
โ๐ Learn Problem Solving (LeetCode / HackerRank)
โ๐ Learn OOPs & Design Patterns
โ๐ Learn Version Control (Git & GitHub)
โ๐ Learn Debugging & Testing
โ๐ Work on Real-World Projects
โ๐ Contribute to Open Source
โโ Apply for Job / Internship
React โค๏ธ for More ๐ก
โค9
If you want to Excel at using one of the most powerful programming languages in the world, learn these essential Python features:
โข List Comprehensions โ [x for x in range(10) if x % 2 == 0]
โข Lambda Functions โ lambda x: x * 2
โข Map, Filter, Reduce โ Functional programming magic
โข F-strings โ f"Hello, {name}!" (Best way to format strings)
โข Enumerate & Zip โ Iterate smarter
โข Generators & Yield โ Efficient memory usage
โข Exception Handling โ try-except-finally for error-proof code
โข Decorators โ @
โข Pandas & NumPy โ Data manipulation & numerical computing
โข Async Programming โ Speed up tasks with asyncio
Free Python Resources: ๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Like it if you need a complete tutorial on all these topics! ๐โค๏ธ
โข List Comprehensions โ [x for x in range(10) if x % 2 == 0]
โข Lambda Functions โ lambda x: x * 2
โข Map, Filter, Reduce โ Functional programming magic
โข F-strings โ f"Hello, {name}!" (Best way to format strings)
โข Enumerate & Zip โ Iterate smarter
โข Generators & Yield โ Efficient memory usage
โข Exception Handling โ try-except-finally for error-proof code
โข Decorators โ @
staticmethod, @classmethod, @propertyโข Pandas & NumPy โ Data manipulation & numerical computing
โข Async Programming โ Speed up tasks with asyncio
Free Python Resources: ๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Like it if you need a complete tutorial on all these topics! ๐โค๏ธ
โค5
YouTube channels for web development languages:
๐๐ฟ๐ผ๐ป๐๐ฒ๐ป๐ฑ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐ & ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ๐
HTML/CSS ๐จ โ Kevin Powell
JavaScript ๐ โ The Net Ninja
TypeScript ๐ โ Academind
React โ๏ธ โ Traversy Media
Angular ๐บ โ Academind
Vue. js ๐ฉ โ Vue Mastery
๐๐ฎ๐ฐ๐ธ๐ฒ๐ป๐ฑ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐ & ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ๐
Node. js ๐ โ Traversy Media
PHP ๐ โ PHP Academy
Ruby on Rails ๐ โ Drifting Ruby
Django (Python) ๐ โ Corey Schafer
Flask (Python) ๐ฅ โ Pretty Printed
ASP. NET (C#) ๐ฏ โ IAmTimCorey
๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ & ๐๐ฒ๐๐ข๐ฝ๐
SQL ๐๏ธ โ DataSimplifier
MongoDB ๐ โ MongoDB Official
Docker ๐ณ โ TechWorld with Nana
React โค๏ธ for more
๐๐ฟ๐ผ๐ป๐๐ฒ๐ป๐ฑ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐ & ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ๐
HTML/CSS ๐จ โ Kevin Powell
JavaScript ๐ โ The Net Ninja
TypeScript ๐ โ Academind
React โ๏ธ โ Traversy Media
Angular ๐บ โ Academind
Vue. js ๐ฉ โ Vue Mastery
๐๐ฎ๐ฐ๐ธ๐ฒ๐ป๐ฑ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐ & ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ๐
Node. js ๐ โ Traversy Media
PHP ๐ โ PHP Academy
Ruby on Rails ๐ โ Drifting Ruby
Django (Python) ๐ โ Corey Schafer
Flask (Python) ๐ฅ โ Pretty Printed
ASP. NET (C#) ๐ฏ โ IAmTimCorey
๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ & ๐๐ฒ๐๐ข๐ฝ๐
SQL ๐๏ธ โ DataSimplifier
MongoDB ๐ โ MongoDB Official
Docker ๐ณ โ TechWorld with Nana
React โค๏ธ for more
โค5
๐ป Popular Coding Languages & Their Uses ๐
There are many programming languages, each serving different purposes. Here are some key ones you should know:
๐น 1. Python โ Beginner-friendly, versatile, and widely used in data science, AI, web development, and automation.
๐น 2. JavaScript โ Essential for frontend and backend web development, powering interactive websites and applications.
๐น 3. Java โ Used for enterprise applications, Android development, and large-scale systems due to its stability.
๐น 4. C++ โ High-performance language ideal for game development, operating systems, and embedded systems.
๐น 5. C# โ Commonly used in game development (Unity), Windows applications, and enterprise software.
๐น 6. Swift โ The go-to language for iOS and macOS development, known for its efficiency.
๐น 7. Go (Golang) โ Designed for high-performance applications, cloud computing, and network programming.
๐น 8. Rust โ Focuses on memory safety and performance, making it great for system-level programming.
๐น 9. SQL โ Essential for database management, allowing efficient data retrieval and manipulation.
๐น 10. Kotlin โ Popular for Android app development, offering modern features compared to Java.
๐ฅ React โค๏ธ for more ๐๐
There are many programming languages, each serving different purposes. Here are some key ones you should know:
๐น 1. Python โ Beginner-friendly, versatile, and widely used in data science, AI, web development, and automation.
๐น 2. JavaScript โ Essential for frontend and backend web development, powering interactive websites and applications.
๐น 3. Java โ Used for enterprise applications, Android development, and large-scale systems due to its stability.
๐น 4. C++ โ High-performance language ideal for game development, operating systems, and embedded systems.
๐น 5. C# โ Commonly used in game development (Unity), Windows applications, and enterprise software.
๐น 6. Swift โ The go-to language for iOS and macOS development, known for its efficiency.
๐น 7. Go (Golang) โ Designed for high-performance applications, cloud computing, and network programming.
๐น 8. Rust โ Focuses on memory safety and performance, making it great for system-level programming.
๐น 9. SQL โ Essential for database management, allowing efficient data retrieval and manipulation.
๐น 10. Kotlin โ Popular for Android app development, offering modern features compared to Java.
๐ฅ React โค๏ธ for more ๐๐
โค7
๐๐๐ ๐๐๐ฌ๐ ๐๐ญ๐ฎ๐๐ข๐๐ฌ ๐๐จ๐ซ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ:
Join for more: https://xn--r1a.website/sqlanalyst
1. Dannyโs Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/
2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/
3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT
4. Data Bank: Thatโs money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv
5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf
6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG
7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7
8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8
Join for more: https://xn--r1a.website/sqlanalyst
1. Dannyโs Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/
2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/
3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT
4. Data Bank: Thatโs money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv
5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf
6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG
7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7
8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8
โค2
โ
Top 10 Coding Interview Questions (2025) ๐ผ๐จโ๐ป
1๏ธโฃ Subarray with given sum
Find continuous subarray that sums to a target value.
2๏ธโฃ Count triplets with given sum
Find triplets in array whose sum equals a target.
3๏ธโฃ Kadaneโs Algorithm
Find maximum sum subarray in O(n).
4๏ธโฃ Missing number in array
Find the one number missing from 1 to N.
5๏ธโฃ Sort an array of 0s, 1s and 2s
Dutch National Flag problem โ sort in a single scan.
6๏ธโฃ Depth First Traversal (Graph)
Traverse graph nodes using stack or recursion.
7๏ธโฃ Topological Sort
Order nodes in a Directed Acyclic Graph (DAG).
8๏ธโฃ Activity Selection (Greedy)
Select max non-overlapping activities.
9๏ธโฃ Longest Increasing Subsequence (DP)
Find length of longest increasing subsequence in array.
๐ N-Queen Problem (Backtracking)
Place N queens on an NรN board so none attack each other.
๐ฌ Tap โค๏ธ for more
1๏ธโฃ Subarray with given sum
Find continuous subarray that sums to a target value.
2๏ธโฃ Count triplets with given sum
Find triplets in array whose sum equals a target.
3๏ธโฃ Kadaneโs Algorithm
Find maximum sum subarray in O(n).
4๏ธโฃ Missing number in array
Find the one number missing from 1 to N.
5๏ธโฃ Sort an array of 0s, 1s and 2s
Dutch National Flag problem โ sort in a single scan.
6๏ธโฃ Depth First Traversal (Graph)
Traverse graph nodes using stack or recursion.
7๏ธโฃ Topological Sort
Order nodes in a Directed Acyclic Graph (DAG).
8๏ธโฃ Activity Selection (Greedy)
Select max non-overlapping activities.
9๏ธโฃ Longest Increasing Subsequence (DP)
Find length of longest increasing subsequence in array.
๐ N-Queen Problem (Backtracking)
Place N queens on an NรN board so none attack each other.
๐ฌ Tap โค๏ธ for more
โค7
Master Power BI with this Cheat Sheet๐ฅ
If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision!
๐ ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐๐ฎ๐๐ถ๐ฐ๐:
DAX Functions:
- SUMX: Sum of values based on a condition.
- FILTER: Filter data based on a given condition.
- RELATED: Retrieve a related column from another table.
- CALCULATE: Perform dynamic calculations.
- EARLIER: Access a column from a higher context.
- CROSSJOIN: Create a Cartesian product of two tables.
- UNION: Combine the results from multiple tables.
- RANKX: Rank data within a column.
- DISTINCT: Filter unique rows.
Data Modeling:
- Relationships: Create, manage, and modify relationships.
- Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year).
- Calculated Columns: Create calculated columns to extend data.
- Measures: Write powerful measures to analyze data effectively.
Data Visualization:
- Charts: Bar charts, line charts, pie charts, and more.
- Table & Matrix: Display tabular data and matrix visuals.
- Slicers: Create interactive filters.
- Tooltips: Enhance visual interactivity with tooltips.
- Map: Display geographical data effectively.
โจ ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐ง๐ถ๐ฝ๐:
โ Use DAX for efficient data analysis.
โ Optimize data models for performance.
โ Utilize drill-through and drill-down for deeper insights.
โ Leverage bookmarks for enhanced navigation.
โ Annotate your reports with comments for clarity.
Like this post if you need more content like this ๐โค๏ธ
If you're preparing for a Power BI interview, this cheat sheet covers the key concepts and DAX commands you'll need. Bookmark it for last-minute revision!
๐ ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐๐ฎ๐๐ถ๐ฐ๐:
DAX Functions:
- SUMX: Sum of values based on a condition.
- FILTER: Filter data based on a given condition.
- RELATED: Retrieve a related column from another table.
- CALCULATE: Perform dynamic calculations.
- EARLIER: Access a column from a higher context.
- CROSSJOIN: Create a Cartesian product of two tables.
- UNION: Combine the results from multiple tables.
- RANKX: Rank data within a column.
- DISTINCT: Filter unique rows.
Data Modeling:
- Relationships: Create, manage, and modify relationships.
- Hierarchies: Build time-based hierarchies (e.g., Date, Month, Year).
- Calculated Columns: Create calculated columns to extend data.
- Measures: Write powerful measures to analyze data effectively.
Data Visualization:
- Charts: Bar charts, line charts, pie charts, and more.
- Table & Matrix: Display tabular data and matrix visuals.
- Slicers: Create interactive filters.
- Tooltips: Enhance visual interactivity with tooltips.
- Map: Display geographical data effectively.
โจ ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐ง๐ถ๐ฝ๐:
โ Use DAX for efficient data analysis.
โ Optimize data models for performance.
โ Utilize drill-through and drill-down for deeper insights.
โ Leverage bookmarks for enhanced navigation.
โ Annotate your reports with comments for clarity.
Like this post if you need more content like this ๐โค๏ธ
โค3๐1
Some important questions to crack data science interview
Q. Describe how Gradient Boosting works.
A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Q. Describe the decision tree model.
A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.
Q. What is a neural network?
A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.
Q. Explain the Bias-Variance Tradeoff
A. The biasโvariance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.
Q. Whatโs the difference between L1 and L2 regularization?
A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.
ENJOY LEARNING ๐๐
Q. Describe how Gradient Boosting works.
A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Q. Describe the decision tree model.
A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.
Q. What is a neural network?
A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.
Q. Explain the Bias-Variance Tradeoff
A. The biasโvariance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.
Q. Whatโs the difference between L1 and L2 regularization?
A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.
ENJOY LEARNING ๐๐
โค4
๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ผ ๐๐ป๐ฟ๐ผ๐น๐น ๐๐ป ๐ฎ๐ฌ๐ฎ๐ฑ ๐
Learn Fundamental Skills with Free Online Courses & Earn Certificates
- AI
- GenAI
- Data Science,
- BigData
- Python
- Cloud Computing
- Machine Learning
- Cyber Security
๐๐ข๐ง๐ค ๐:-
https://linkpd.in/freecourses
Enroll for FREE & Get Certified ๐
Learn Fundamental Skills with Free Online Courses & Earn Certificates
- AI
- GenAI
- Data Science,
- BigData
- Python
- Cloud Computing
- Machine Learning
- Cyber Security
๐๐ข๐ง๐ค ๐:-
https://linkpd.in/freecourses
Enroll for FREE & Get Certified ๐
โค1
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.
Best Programming Resources: https://topmate.io/coding/898340
Join for more: https://xn--r1a.website/programming_guide
ENJOY LEARNING ๐๐
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.
Best Programming Resources: https://topmate.io/coding/898340
Join for more: https://xn--r1a.website/programming_guide
ENJOY LEARNING ๐๐
โค3๐2