इश्क में ग़ैरत-ए-जज़्बात ने रोने ना दिया - सुदर्शन फ़ाकिर

 इश्क में ग़ैरत-ए-जज़्बात ने रोने ना दिया

वरना क्या बात थी किस बात ने रोने ना दिया

आप कहते थे कि रोने से ना बदलेंगे नसीब

उमर भर आप की इस बात ने रोने ना दिया

रोने वालों से कहो उन का भी रोना रो लें

जिन को मज़बूरी-ए-हालत ने रोने ना दिया

तुझ से मिल कर हमें रोना था बहुत रोना था

तांगी-ए-वक्त-ए-मुलाकात ने रोने ना दिया

एक दो रोज़ का सदमा हो तो रो लें 'फकीर'

हम को हर रोज़ के सदमत ने रोने ना दिया

Skills required to be a Python Developer

 Responsibilities of the Candidate:

1. Develop and maintain web applications using Python and Flask.
2. Design and implement GraphQL APIs to support front-end and mobile applications.
3. Containerize applications using Docker for consistent development, testing, and deployment.
4. Collaborate with cross-functional teams to define, design, and ship new features. Write clean, maintainable, and efficient code.
5. Troubleshoot and debug applications to optimize performance and usability. Participate in code reviews to maintain quality and share knowledge.
6. Stay updated with the latest industry trends and technologies to bring innovative solutions to the table.

Requirements:

1. Proficiency in Python with a solid understanding of its frameworks.
2. Experience with Flask and building RESTful and GraphQL APIs.
3. Hands-on experience with Docker for containerization.
4. Knowledge of database technologies like SQL, NoSQL, and ORM.
5. Understanding of front-end technologies such as JavaScript, HTML5, and CSS3.
6. Experience with version control systems like Git.
7. Strong problem-solving skills and ability to work in a fast-paced environment.
8. Good communication skills and a team player.
9. Experience with cloud services like AWS, GCP, or Azure. Familiarity with CI/CD pipelines.
10. Knowledge of microservices architecture.

What is GraphQL

GATE DA (Data Science & AI) syllabus

1. Probability and Statistics: 

1. Counting (permutation and combinations), 
2. probability axioms,
3. Sample space, events, 
4. independent events, mutually exclusive events, 
5. marginal, conditional and joint probability, 
6. Bayes Theorem, 
7. conditional expectation and variance, 
8. mean, median, mode and standard deviation, 
9. correlation, and covariance, 
10. random variables, 
11. discrete random variables and probability mass functions, 
12. uniform, Bernoulli, binomial distribution, 
13. Continuous random variables and probability distribution function, 
14. uniform, exponential, Poisson, normal, standard normal, 
15. t-distribution, chi-squared distributions, 
16. cumulative distribution function,
17. Conditional PDF, 
18. Central limit theorem, 
19. confidence interval, 
20. z-test, t-test, chi-squared test.

2. Linear Algebra: 

1. Vector space, 
2. subspaces, 
3. linear dependence and independence of vectors,
4. matrices, 
5. projection matrix, 
6. orthogonal matrix, 
7. idempotent matrix, 
8. partition matrix and their properties, 
9. quadratic forms, 
10. systems of linear equations and solutions; 
11. Gaussian elimination, 
12. eigenvalues and eigenvectors, 
13. determinant, 
14. rank, 
15. nullity, 
16. projections, 
17. LU decomposition,
18. singular value decomposition.

3. Calculus and Optimization: 

1. Functions of a single variable, 
2. limit, 
3. continuity and differentiability,
4. Taylor series, 
5. maxima and minima, 
6. optimization involving a single variable.

4. Programming, Data Structures and Algorithms:

1. Programming in Python, 
2. Basic data structures: stacks, queues, linked lists, trees, hash tables; 
3. Search algorithms: linear search and binary search, 
4. Basic sorting algorithms: selection sort, bubble sort and insertion sort; 
5. Divide and conquer: merge sort, quicksort; 
6. Introduction to graph theory: Basic graph algorithms , traversals and shortest path.

5. Database Management and Warehousing:

ER-model, 

relational model:

 relational algebra, tuple calculus, 

SQL, 

integrity constraints, 

normal form, 

file organization, 

indexing, 

data types,

data transformation such as normalization, discretization, sampling, compression; 

data

warehouse modelling: 

schema for multidimensional data models, concept hierarchies,

measures: categorization and computations.

6. Machine Learning: 

1. Supervised Learning: 

regression and classification problems, 

simple linear regression, 

multiple linear regression, 

ridge regression, 

logistic regression, 

k-nearest neighbor, 

naive Bayes classifier, 

linear discriminant analysis, 

support vector machine,

decision trees,

 bias-variance trade-off: cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, 

multi-layer perceptron, 

feed-forward neural network; 

2. Unsupervised Learning: 

1. clustering algorithms, 
2. k-means/k-medoid, 
3. hierarchical clustering, 
1. top-down, 
2. bottom-up: single-linkage, multiple-linkage
1. dimensionality reduction, principal component analysis.

7. AI: 

1. Search: 
1. informed, 
2. uninformed, 
3. adversarial
2. Logic:
1. propositional, 
2. predicate; 
3. Reasoning under uncertainty topics:
1. conditional independence representation, 
2. exact inference through variable elimination, and 
3. approximate inference through sampling.

How can I run a C++ program directly from Windows?

How-can-I-run-a-C-program-directly-from-Windows

All built-in modules in Python

 All built-in modules in Python python 3 -modindex.html

How-to-distribute-a-python-application-professionally

Link  

Syllabus of core Python

 

• Python installation

• The Command Line

• Jupyter Notebook

• Python Comments

• The print statement

• Variables

• Constants

• Keywords

• Numbers

• Strings

• Lists

• Dictionaries

• Tuples

• Sets

• Control Flow

• IF, ELIF and ELSE statements

• Comparison operators

• For loops

• While Loops

• Nest Loops

• Break, Continue and Pass keywords

• enumerate

• List Comprehensions

• Dictionary Comprehensions

• How to use Functions

• How to create your own Functions

• Parameters and arguments

• *args and **kwargs

• lambda expressions

• Map and Filter functions

• Python Scope

• Accepting and validating user input

• Object Oriented Programming (OOP)

• Inheritance

• Polymorphism

• Special Methods

• Modules & Packages

• How to create your own packages

• Errors and Exceptions handling

• Decorators

• Generators

• Web Scraping using the requests and BeautifulSoup libraries

• GUI's using Tkinter

• Dashboards using plotly and dash

• Work with CSV files, PDF files and Databases

• The Collections module

• Regular Expressions (regex)

• Timing your Python Code