Forwarded from Python Projects & Resources
๐๐๐ฆ๐๐ข ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
- Data Analytics
- Data Science
- Python
- Javascript
- Cybersecurity
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/4fYr1xO
Enroll For FREE & Get Certified๐
- Data Analytics
- Data Science
- Python
- Javascript
- Cybersecurity
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/4fYr1xO
Enroll For FREE & Get Certified๐
โค3
๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ ๐ฏ๐ฌ. ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ โ ๐๐ก๐๐ญโ๐ฌ ๐ญ๐ก๐ ๐๐ข๐๐๐๐ซ๐๐ง๐๐?
Whether you're starting a career in data or looking to pivot, itโs crucial to understand the key differences between a Data Analyst and a Data Scientist:
๐ ๐ ๐จ๐๐ฎ๐ฌ
Data Analyst: Interprets existing data to uncover insights.
Data Scientist: Predicts future trends using advanced models.
๐ ๏ธ ๐๐จ๐จ๐ฅ๐ฌ ๐๐ฌ๐๐
Data Analyst: Excel, SQL, Tableau
Data Scientist: Python, R, Machine Learning tools
๐ผ ๐๐ฒ๐ฉ๐ ๐จ๐ ๐๐จ๐ซ๐ค
Data Analyst: Reporting and dashboarding
Data Scientist: Building models and algorithms
๐ง ๐๐ค๐ข๐ฅ๐ฅ๐ฌ๐๐ญ
Data Analyst: Data cleaning, visualization
Data Scientist: Data-driven product development and strategy
Both roles are essentialโbut they serve different purposes. One tells you what happened, the other helps you decide what to do next.
Whether you're starting a career in data or looking to pivot, itโs crucial to understand the key differences between a Data Analyst and a Data Scientist:
๐ ๐ ๐จ๐๐ฎ๐ฌ
Data Analyst: Interprets existing data to uncover insights.
Data Scientist: Predicts future trends using advanced models.
๐ ๏ธ ๐๐จ๐จ๐ฅ๐ฌ ๐๐ฌ๐๐
Data Analyst: Excel, SQL, Tableau
Data Scientist: Python, R, Machine Learning tools
๐ผ ๐๐ฒ๐ฉ๐ ๐จ๐ ๐๐จ๐ซ๐ค
Data Analyst: Reporting and dashboarding
Data Scientist: Building models and algorithms
๐ง ๐๐ค๐ข๐ฅ๐ฅ๐ฌ๐๐ญ
Data Analyst: Data cleaning, visualization
Data Scientist: Data-driven product development and strategy
Both roles are essentialโbut they serve different purposes. One tells you what happened, the other helps you decide what to do next.
โค1
Forwarded from Artificial Intelligence
๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ฅ๐๐ ๐ฅ๐ผ๐ฎ๐ฑ๐บ๐ฎ๐ฝ ,๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ,๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ & ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐๐๐ถ๐ฑ๐ฒ๐
Roadmap:- https://pdlink.in/41c1Kei
Certifications:- https://pdlink.in/3Fq7E4p
Projects:- https://pdlink.in/3ZkXetO
Interview Q/A :- https://pdlink.in/4jLOJ2a
Enroll For FREE & Become a Certified Data Analyst In 2025๐
Roadmap:- https://pdlink.in/41c1Kei
Certifications:- https://pdlink.in/3Fq7E4p
Projects:- https://pdlink.in/3ZkXetO
Interview Q/A :- https://pdlink.in/4jLOJ2a
Enroll For FREE & Become a Certified Data Analyst In 2025๐
โค3
Launch Your Career in Data Analytics, Data Science & AI ๐
Learn Live from Top Data Experts at Leading Tech Companies!
Master in-demand skills and land your dream job in Data Science, Analytics, or AI with expert-led live training.
Eligibility: BTech / BCA / BSc / BBA / BA / BCom
๐น 2000+ Students Placed
๐น 500+ Hiring Partners
๐น โน7.4 LPA Average Package
๐น โน41 LPA Highest Package
Limited Seats โ Secure Yours Now๐
๐ https://go.acciojob.com/RYFvdU
Learn Live from Top Data Experts at Leading Tech Companies!
Master in-demand skills and land your dream job in Data Science, Analytics, or AI with expert-led live training.
Eligibility: BTech / BCA / BSc / BBA / BA / BCom
๐น 2000+ Students Placed
๐น 500+ Hiring Partners
๐น โน7.4 LPA Average Package
๐น โน41 LPA Highest Package
Limited Seats โ Secure Yours Now๐
๐ https://go.acciojob.com/RYFvdU
Acciojob
Launch Your Tech Career in Data Science & AI from Scratch
Land your Dream Data Science Job in 7 Months with 500+ Hiring Partners & 100% Job Assistance. Get Mentored by IITians & Data Experts from Top Tech Companies.
โค2
Forwarded from Python Projects & Resources
๐๐ป๐ฑ๐๐๐๐ฟ๐ ๐๐ฝ๐ฝ๐ฟ๐ผ๐๐ฒ๐ฑ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐
Whether youโre interested in AI, Data Analytics, Cybersecurity, or Cloud Computing, thereโs something here for everyone.
โ 100% Free Courses
โ Govt. Incentives on Completion
โ Self-paced Learning
โ Certificates to Showcase on LinkedIn & Resume
โ Mock Assessments to Test Your Skills
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/447coEk
Enroll for FREE & Get Certified ๐
Whether youโre interested in AI, Data Analytics, Cybersecurity, or Cloud Computing, thereโs something here for everyone.
โ 100% Free Courses
โ Govt. Incentives on Completion
โ Self-paced Learning
โ Certificates to Showcase on LinkedIn & Resume
โ Mock Assessments to Test Your Skills
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/447coEk
Enroll for FREE & Get Certified ๐
โค1
Master Javascript :
The JavaScript Tree ๐
|
|โโ Variables
| โโโ var
| โโโ let
| โโโ const
|
|โโ Data Types
| โโโ String
| โโโ Number
| โโโ Boolean
| โโโ Object
| โโโ Array
| โโโ Null
| โโโ Undefined
|
|โโ Operators
| โโโ Arithmetic
| โโโ Assignment
| โโโ Comparison
| โโโ Logical
| โโโ Unary
| โโโ Ternary (Conditional)
||โโ Control Flow
| โโโ if statement
| โโโ else statement
| โโโ else if statement
| โโโ switch statement
| โโโ for loop
| โโโ while loop
| โโโ do-while loop
|
|โโ Functions
| โโโ Function declaration
| โโโ Function expression
| โโโ Arrow function
| โโโ IIFE (Immediately Invoked Function Expression)
|
|โโ Scope
| โโโ Global scope
| โโโ Local scope
| โโโ Block scope
| โโโ Lexical scope
||โโ Arrays
| โโโ Array methods
| | โโโ push()
| | โโโ pop()
| | โโโ shift()
| | โโโ unshift()
| | โโโ splice()
| | โโโ slice()
| | โโโ concat()
| โโโ Array iteration
| โโโ forEach()
| โโโ map()
| โโโ filter()
| โโโ reduce()|
|โโ Objects
| โโโ Object properties
| | โโโ Dot notation
| | โโโ Bracket notation
| โโโ Object methods
| | โโโ Object.keys()
| | โโโ Object.values()
| | โโโ Object.entries()
| โโโ Object destructuring
||โโ Promises
| โโโ Promise states
| | โโโ Pending
| | โโโ Fulfilled
| | โโโ Rejected
| โโโ Promise methods
| | โโโ then()
| | โโโ catch()
| | โโโ finally()
| โโโ Promise.all()
|
|โโ Asynchronous JavaScript
| โโโ Callbacks
| โโโ Promises
| โโโ Async/Await
|
|โโ Error Handling
| โโโ try...catch statement
| โโโ throw statement
|
|โโ JSON (JavaScript Object Notation)
||โโ Modules
| โโโ import
| โโโ export
|
|โโ DOM Manipulation
| โโโ Selecting elements
| โโโ Modifying elements
| โโโ Creating elements
|
|โโ Events
| โโโ Event listeners
| โโโ Event propagation
| โโโ Event delegation
|
|โโ AJAX (Asynchronous JavaScript and XML)
|
|โโ Fetch API
||โโ ES6+ Features
| โโโ Template literals
| โโโ Destructuring assignment
| โโโ Spread/rest operator
| โโโ Arrow functions
| โโโ Classes
| โโโ let and const
| โโโ Default parameters
| โโโ Modules
| โโโ Promises
|
|โโ Web APIs
| โโโ Local Storage
| โโโ Session Storage
| โโโ Web Storage API
|
|โโ Libraries and Frameworks
| โโโ React
| โโโ Angular
| โโโ Vue.js
||โโ Debugging
| โโโ Console.log()
| โโโ Breakpoints
| โโโ DevTools
|
|โโ Others
| โโโ Closures
| โโโ Callbacks
| โโโ Prototypes
| โโโ this keyword
| โโโ Hoisting
| โโโ Strict mode
|
| END __
The JavaScript Tree ๐
|
|โโ Variables
| โโโ var
| โโโ let
| โโโ const
|
|โโ Data Types
| โโโ String
| โโโ Number
| โโโ Boolean
| โโโ Object
| โโโ Array
| โโโ Null
| โโโ Undefined
|
|โโ Operators
| โโโ Arithmetic
| โโโ Assignment
| โโโ Comparison
| โโโ Logical
| โโโ Unary
| โโโ Ternary (Conditional)
||โโ Control Flow
| โโโ if statement
| โโโ else statement
| โโโ else if statement
| โโโ switch statement
| โโโ for loop
| โโโ while loop
| โโโ do-while loop
|
|โโ Functions
| โโโ Function declaration
| โโโ Function expression
| โโโ Arrow function
| โโโ IIFE (Immediately Invoked Function Expression)
|
|โโ Scope
| โโโ Global scope
| โโโ Local scope
| โโโ Block scope
| โโโ Lexical scope
||โโ Arrays
| โโโ Array methods
| | โโโ push()
| | โโโ pop()
| | โโโ shift()
| | โโโ unshift()
| | โโโ splice()
| | โโโ slice()
| | โโโ concat()
| โโโ Array iteration
| โโโ forEach()
| โโโ map()
| โโโ filter()
| โโโ reduce()|
|โโ Objects
| โโโ Object properties
| | โโโ Dot notation
| | โโโ Bracket notation
| โโโ Object methods
| | โโโ Object.keys()
| | โโโ Object.values()
| | โโโ Object.entries()
| โโโ Object destructuring
||โโ Promises
| โโโ Promise states
| | โโโ Pending
| | โโโ Fulfilled
| | โโโ Rejected
| โโโ Promise methods
| | โโโ then()
| | โโโ catch()
| | โโโ finally()
| โโโ Promise.all()
|
|โโ Asynchronous JavaScript
| โโโ Callbacks
| โโโ Promises
| โโโ Async/Await
|
|โโ Error Handling
| โโโ try...catch statement
| โโโ throw statement
|
|โโ JSON (JavaScript Object Notation)
||โโ Modules
| โโโ import
| โโโ export
|
|โโ DOM Manipulation
| โโโ Selecting elements
| โโโ Modifying elements
| โโโ Creating elements
|
|โโ Events
| โโโ Event listeners
| โโโ Event propagation
| โโโ Event delegation
|
|โโ AJAX (Asynchronous JavaScript and XML)
|
|โโ Fetch API
||โโ ES6+ Features
| โโโ Template literals
| โโโ Destructuring assignment
| โโโ Spread/rest operator
| โโโ Arrow functions
| โโโ Classes
| โโโ let and const
| โโโ Default parameters
| โโโ Modules
| โโโ Promises
|
|โโ Web APIs
| โโโ Local Storage
| โโโ Session Storage
| โโโ Web Storage API
|
|โโ Libraries and Frameworks
| โโโ React
| โโโ Angular
| โโโ Vue.js
||โโ Debugging
| โโโ Console.log()
| โโโ Breakpoints
| โโโ DevTools
|
|โโ Others
| โโโ Closures
| โโโ Callbacks
| โโโ Prototypes
| โโโ this keyword
| โโโ Hoisting
| โโโ Strict mode
|
| END __
โค4
Forwarded from Python Projects & Resources
๐ง๐ผ๐ฝ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ & ๐๐ฒ๐ฎ๐ฑ๐ถ๐ป๐ด ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐ข๐ณ๐ณ๐ฒ๐ฟ๐ถ๐ป๐ด ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
Harward :- https://pdlink.in/4kmYOn1
MIT :- https://pdlink.in/45cvR95
HP :- https://pdlink.in/45ci02k
Google :- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/441GCKF
Standford :- https://pdlink.in/3ThPwNw
IIM :- https://pdlink.in/4nfXDrV
Enroll for FREE & Get Certified ๐
Harward :- https://pdlink.in/4kmYOn1
MIT :- https://pdlink.in/45cvR95
HP :- https://pdlink.in/45ci02k
Google :- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/441GCKF
Standford :- https://pdlink.in/3ThPwNw
IIM :- https://pdlink.in/4nfXDrV
Enroll for FREE & Get Certified ๐
Important questions to ace your machine learning interview with an approach to answer:
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
๐๐
https://topmate.io/coding/914624
Like for more ๐
1. Machine Learning Project Lifecycle:
- Define the problem
- Gather and preprocess data
- Choose a model and train it
- Evaluate model performance
- Tune and optimize the model
- Deploy and maintain the model
2. Supervised vs Unsupervised Learning:
- Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
- Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).
3. Evaluation Metrics for Regression:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared (coefficient of determination)
4. Overfitting and Prevention:
- Overfitting: Model learns the noise instead of the underlying pattern.
- Prevention: Use simpler models, cross-validation, regularization.
5. Bias-Variance Tradeoff:
- Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.
6. Cross-Validation:
- Technique to assess model performance by splitting data into multiple subsets for training and validation.
7. Feature Selection Techniques:
- Filter methods (e.g., correlation analysis)
- Wrapper methods (e.g., recursive feature elimination)
- Embedded methods (e.g., Lasso regularization)
8. Assumptions of Linear Regression:
- Linearity
- Independence of errors
- Homoscedasticity (constant variance)
- No multicollinearity
9. Regularization in Linear Models:
- Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.
10. Classification vs Regression:
- Classification: Predicts a categorical outcome (e.g., class labels).
- Regression: Predicts a continuous numerical outcome (e.g., house price).
11. Dimensionality Reduction Algorithms:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
12. Decision Tree:
- Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.
13. Ensemble Methods:
- Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).
14. Handling Missing or Corrupted Data:
- Imputation (e.g., mean substitution)
- Removing rows or columns with missing data
- Using algorithms robust to missing values
15. Kernels in Support Vector Machines (SVM):
- Linear kernel
- Polynomial kernel
- Radial Basis Function (RBF) kernel
Data Science Interview Resources
๐๐
https://topmate.io/coding/914624
Like for more ๐
โค2
๐๐ข๐๐ซ๐จ๐ฌ๐จ๐๐ญ ๐
๐๐๐ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ!๐๐ป
Supercharge your career with 5 FREE Microsoft certification courses designed to boost your data analytics skills!
๐๐ง๐ซ๐จ๐ฅ๐ฅ ๐ ๐จ๐ซ ๐ ๐๐๐๐ :-
https://bit.ly/3Vlixcq
- Earn certifications to showcase your skills
Donโt waitโstart your journey to success today! โจ
Supercharge your career with 5 FREE Microsoft certification courses designed to boost your data analytics skills!
๐๐ง๐ซ๐จ๐ฅ๐ฅ ๐ ๐จ๐ซ ๐ ๐๐๐๐ :-
https://bit.ly/3Vlixcq
- Earn certifications to showcase your skills
Donโt waitโstart your journey to success today! โจ
โค4
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
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
โค3
๐๐ฟ๐ฒ๐ฒ ๐๐ & ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐๐ผ๐๐ฟ๐๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ๐๐
Want to explore AI & Machine Learning but donโt know where to start โ or donโt want to spend โนโนโน on it?๐จโ๐ป
Learn the foundations of AI, machine learning basics, data handling, and real-world use cases in just a few hours.๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/401SWry
This 100% FREE course is designed just for beginners โ whether youโre a student, fresher, or career switcherโ ๏ธ
Want to explore AI & Machine Learning but donโt know where to start โ or donโt want to spend โนโนโน on it?๐จโ๐ป
Learn the foundations of AI, machine learning basics, data handling, and real-world use cases in just a few hours.๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/401SWry
This 100% FREE course is designed just for beginners โ whether youโre a student, fresher, or career switcherโ ๏ธ
โค1
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 โ 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
Credits: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
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 โ 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
Credits: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
โค2
๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ ๐ง๐ฒ๐ฐ๐ต ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ? ๐๐ฒ๐ฟ๐ฒโ๐ ๐ฌ๐ผ๐๐ฟ ๐ฆ๐๐ฒ๐ฝ-๐ฏ๐-๐ฆ๐๐ฒ๐ฝ ๐ฅ๐ผ๐ฎ๐ฑ๐บ๐ฎ๐ฝ ๐๐ผ ๐๐ฟ๐ฎ๐ฐ๐ธ ๐ฃ๐ฟ๐ผ๐ฑ๐๐ฐ๐-๐๐ฎ๐๐ฒ๐ฑ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐!๐
Landing your dream tech job takes more than just writing code โ it requires structured preparation across key areas๐จโ๐ป
This roadmap will guide you from zero to offer letter! ๐ผ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3GdfTS2
This plan works if you stay consistent๐ชโ ๏ธ
Landing your dream tech job takes more than just writing code โ it requires structured preparation across key areas๐จโ๐ป
This roadmap will guide you from zero to offer letter! ๐ผ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3GdfTS2
This plan works if you stay consistent๐ชโ ๏ธ
โค1
Hey guys,
Today, letโs talk about some of the Python questions you might face during a data analyst interview. Below, Iโve 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 youโll 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. Itโs extremely useful in data analysis for storing mappings (like labels to corresponding values) or for quick lookups.
Example:
4. Explain the difference between a list and a tuple in Python.
- List: Mutable, meaning you can modify (add, remove, or change) elements. Itโs written in square brackets
Example:
- Tuple: Immutable, meaning once defined, you cannot modify it. Itโs written in parentheses
Example:
5. How would you handle missing data in a dataset using Python?
Handling missing data is critical in data analysis, and Pythonโs Pandas library makes it easy. Here are some common methods:
- Drop missing data:
- Fill missing data with a specific value:
- Forward-fill or backfill missing values:
6. How do you merge/join two datasets in Python?
- pd.merge(): For SQL-style joins (inner, outer, left, right).
- pd.concat(): For concatenating along rows or columns.
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:
Lambdas are often used in data analysis for quick transformations or filtering operations within functions like
If youโre preparing for interviews, focus on writing clean, optimized code and understand how Python fits into the larger data ecosystem.
Here you can find essential Python Interview Resources๐
https://t.me/DataSimplifier
Like for more resources like this ๐ โฅ๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
Today, letโs talk about some of the Python questions you might face during a data analyst interview. Below, Iโve 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 youโll 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. Itโs 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. Itโs written in square brackets
[ ]
.Example:
my_list = [10, 20, 30]
my_list.append(40)
- Tuple: Immutable, meaning once defined, you cannot modify it. Itโs 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 Pythonโs 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 youโre preparing for interviews, focus on writing clean, optimized code and understand how Python fits into the larger data ecosystem.
Here you can find essential Python Interview Resources๐
https://t.me/DataSimplifier
Like for more resources like this ๐ โฅ๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
โค3
๐ช๐ฎ๐ป๐ ๐๐ผ ๐๐๐ถ๐น๐ฑ ๐ฎ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฃ๐ผ๐ฟ๐๐ณ๐ผ๐น๐ถ๐ผ ๐ง๐ต๐ฎ๐ ๐๐ฒ๐๐ ๐ฌ๐ผ๐ ๐๐ถ๐ฟ๐ฒ๐ฑ?๐
If youโre just starting out in data analytics and wondering how to stand out โ real-world projects are the key๐
No recruiter is impressed by โjust theory.โ What they want to see? Actionable proof of your skills๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4ezeIc9
Show recruiters that you donโt just โknowโ tools โ you use them to solve problemsโ ๏ธ
If youโre just starting out in data analytics and wondering how to stand out โ real-world projects are the key๐
No recruiter is impressed by โjust theory.โ What they want to see? Actionable proof of your skills๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4ezeIc9
Show recruiters that you donโt just โknowโ tools โ you use them to solve problemsโ ๏ธ
โค1
๐ฐ TypeScript Roadmap for Beginners 2025
โโโ ๐ง Why TypeScript? JavaScript with Superpowers
โโโ โ๏ธ Setting up TypeScript (tsc, tsconfig)
โโโ ๐ก Type Annotations (number, string, boolean, etc.)
โโโ ๐ฆ Interfaces & Type Aliases
โโโ ๐งฑ Classes, Inheritance & Access Modifiers
โโโ ๐ Generics
โโโ โ Type Narrowing & Type Guards
โโโ ๐ Enums, Tuples & Union Types
โโโ ๐งฉ Modules & Namespaces
โโโ ๐ง Working with TypeScript & React/Vue
โโโ ๐งช TypeScript Projects:
โ โโโ Form Validation App
โ โโโ API Data Viewer with TS + Fetch
โ โโโ Typed To-do App
Free Resources: https://whatsapp.com/channel/0029Vax4TBY9Bb62pAS3mX32
โโโ ๐ง Why TypeScript? JavaScript with Superpowers
โโโ โ๏ธ Setting up TypeScript (tsc, tsconfig)
โโโ ๐ก Type Annotations (number, string, boolean, etc.)
โโโ ๐ฆ Interfaces & Type Aliases
โโโ ๐งฑ Classes, Inheritance & Access Modifiers
โโโ ๐ Generics
โโโ โ Type Narrowing & Type Guards
โโโ ๐ Enums, Tuples & Union Types
โโโ ๐งฉ Modules & Namespaces
โโโ ๐ง Working with TypeScript & React/Vue
โโโ ๐งช TypeScript Projects:
โ โโโ Form Validation App
โ โโโ API Data Viewer with TS + Fetch
โ โโโ Typed To-do App
Free Resources: https://whatsapp.com/channel/0029Vax4TBY9Bb62pAS3mX32
โค1
๐ช๐ฎ๐ป๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ป-๐๐ฒ๐บ๐ฎ๐ป๐ฑ ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐ธ๐ถ๐น๐น๐ โ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ โ ๐๐ถ๐ฟ๐ฒ๐ฐ๐๐น๐ ๐ณ๐ฟ๐ผ๐บ ๐๐ผ๐ผ๐ด๐น๐ฒ?๐
Whether youโre a student, job seeker, or just hungry to upskill โ these 5 beginner-friendly courses are your golden ticket๐๏ธ
No fluff. No fees. Just career-boosting knowledge and certificates that make your resume popโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/42vL6br
Enjoy Learning โ ๏ธ
Whether youโre a student, job seeker, or just hungry to upskill โ these 5 beginner-friendly courses are your golden ticket๐๏ธ
No fluff. No fees. Just career-boosting knowledge and certificates that make your resume popโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/42vL6br
Enjoy Learning โ ๏ธ
โค2
๐ง Technologies for Data Science, Machine Learning & AI!
๐ Data Science
โช๏ธ Python โ The go-to language for Data Science
โช๏ธ R โ Statistical Computing and Graphics
โช๏ธ Pandas โ Data Manipulation & Analysis
โช๏ธ NumPy โ Numerical Computing
โช๏ธ Matplotlib / Seaborn โ Data Visualization
โช๏ธ Jupyter Notebooks โ Interactive Development Environment
๐ค Machine Learning
โช๏ธ Scikit-learn โ Classical ML Algorithms
โช๏ธ TensorFlow โ Deep Learning Framework
โช๏ธ Keras โ High-Level Neural Networks API
โช๏ธ PyTorch โ Deep Learning with Dynamic Computation
โช๏ธ XGBoost โ High-Performance Gradient Boosting
โช๏ธ LightGBM โ Fast, Distributed Gradient Boosting
๐ง Artificial Intelligence
โช๏ธ OpenAI GPT โ Natural Language Processing
โช๏ธ Transformers (Hugging Face) โ Pretrained Models for NLP
โช๏ธ spaCy โ Industrial-Strength NLP
โช๏ธ NLTK โ Natural Language Toolkit
โช๏ธ Computer Vision (OpenCV) โ Image Processing & Object Detection
โช๏ธ YOLO (You Only Look Once) โ Real-Time Object Detection
๐พ Data Storage & Databases
โช๏ธ SQL โ Structured Query Language for Databases
โช๏ธ MongoDB โ NoSQL, Flexible Data Storage
โช๏ธ BigQuery โ Googleโs Data Warehouse for Large Scale Data
โช๏ธ Apache Hadoop โ Distributed Storage and Processing
โช๏ธ Apache Spark โ Big Data Processing & ML
๐ Data Engineering & Deployment
โช๏ธ Apache Airflow โ Workflow Automation & Scheduling
โช๏ธ Docker โ Containerization for ML Models
โช๏ธ Kubernetes โ Container Orchestration
โช๏ธ AWS Sagemaker / Google AI Platform โ Cloud ML Model Deployment
โช๏ธ Flask / FastAPI โ APIs for ML Models
๐ง Tools & Libraries for Automation & Experimentation
โช๏ธ MLflow โ Tracking ML Experiments
โช๏ธ TensorBoard โ Visualization for TensorFlow Models
โช๏ธ DVC (Data Version Control) โ Versioning for Data & Models
React โค๏ธ for more
๐ Data Science
โช๏ธ Python โ The go-to language for Data Science
โช๏ธ R โ Statistical Computing and Graphics
โช๏ธ Pandas โ Data Manipulation & Analysis
โช๏ธ NumPy โ Numerical Computing
โช๏ธ Matplotlib / Seaborn โ Data Visualization
โช๏ธ Jupyter Notebooks โ Interactive Development Environment
๐ค Machine Learning
โช๏ธ Scikit-learn โ Classical ML Algorithms
โช๏ธ TensorFlow โ Deep Learning Framework
โช๏ธ Keras โ High-Level Neural Networks API
โช๏ธ PyTorch โ Deep Learning with Dynamic Computation
โช๏ธ XGBoost โ High-Performance Gradient Boosting
โช๏ธ LightGBM โ Fast, Distributed Gradient Boosting
๐ง Artificial Intelligence
โช๏ธ OpenAI GPT โ Natural Language Processing
โช๏ธ Transformers (Hugging Face) โ Pretrained Models for NLP
โช๏ธ spaCy โ Industrial-Strength NLP
โช๏ธ NLTK โ Natural Language Toolkit
โช๏ธ Computer Vision (OpenCV) โ Image Processing & Object Detection
โช๏ธ YOLO (You Only Look Once) โ Real-Time Object Detection
๐พ Data Storage & Databases
โช๏ธ SQL โ Structured Query Language for Databases
โช๏ธ MongoDB โ NoSQL, Flexible Data Storage
โช๏ธ BigQuery โ Googleโs Data Warehouse for Large Scale Data
โช๏ธ Apache Hadoop โ Distributed Storage and Processing
โช๏ธ Apache Spark โ Big Data Processing & ML
๐ Data Engineering & Deployment
โช๏ธ Apache Airflow โ Workflow Automation & Scheduling
โช๏ธ Docker โ Containerization for ML Models
โช๏ธ Kubernetes โ Container Orchestration
โช๏ธ AWS Sagemaker / Google AI Platform โ Cloud ML Model Deployment
โช๏ธ Flask / FastAPI โ APIs for ML Models
๐ง Tools & Libraries for Automation & Experimentation
โช๏ธ MLflow โ Tracking ML Experiments
โช๏ธ TensorBoard โ Visualization for TensorFlow Models
โช๏ธ DVC (Data Version Control) โ Versioning for Data & Models
React โค๏ธ for more
โค2
Forwarded from Python Projects & Resources
๐๐ฎ๐ฟ๐๐ฎ๐ฟ๐ฑ ๐๐๐๐ ๐ฅ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ๐ฑ ๐ฑ ๐๐ฅ๐๐ ๐ง๐ฒ๐ฐ๐ต ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฌ๐ผ๐ ๐๐ฎ๐ปโ๐ ๐ ๐ถ๐๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ!๐
๐จ Harvard just dropped 5 FREE online tech courses โ no fees, no catches!๐
Whether youโre just starting out or upskilling for a tech career, this is your chance to learn from one of the worldโs top universities โ for FREE. ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4eA368I
๐กLearn at your own pace, earn certificates, and boost your resumeโ ๏ธ
๐จ Harvard just dropped 5 FREE online tech courses โ no fees, no catches!๐
Whether youโre just starting out or upskilling for a tech career, this is your chance to learn from one of the worldโs top universities โ for FREE. ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4eA368I
๐กLearn at your own pace, earn certificates, and boost your resumeโ ๏ธ
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://t.me/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://t.me/programming_guide
ENJOY LEARNING ๐๐
โค2