C# Automation Framework for Web Apps

C# Automation Framework for Web Apps using Selenium, Appium, Sauce Labs, .NET

Rating ⭐️: 4.5 out 5
Students 👨‍🎓 : 12176
Duration ⏰ : 48min of on-demand video
Created by 👨‍🏫 : Nikolay Advolodkin

🔗 COURSE LINK

🔟 Python resources to boost your resume 👇👇

𝟭. 𝗜𝗻𝘁𝗿𝗼 𝘁𝗼 𝗣𝘆𝘁𝗵𝗼𝗻
This a great course to get started with learning Python, if you have no coding experience.
👉 https://kaggle.com/learn/intro-to-programming

𝟮. 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗰𝗼𝘂𝗿𝘀𝗲
Learn the fundamentals like functions, loops, conditional statements, etc of the most important language for data science.
👉 https://kaggle.com/learn/python

𝟯. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻
Part 1 prepares you for PCEP – Certified Entry-Level Python Programmer Certification.
Part 2 prepares you for PCAP – Certified Associate in Python Programming Certification.
👉 https://netacad.com/courses/programming/pcap-programming-essentials-python

𝟰. Python Data Structure and Algorithms
👉https://python.org

𝟱. 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You'll learn Python fundamentals like variables, loops, conditionals, and functions.
Then you'll quickly ramp up to complex data structures, networking, relational databases, and data visualization.
👉 https://freecodecamp.org/learn/scientific-computing-with-python/

𝟲. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You'll learn how to read data from sources like CSVs and SQL, and how to use libraries like Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data.
👉 https://freecodecamp.org/learn/data-analysis-with-python/

𝟳. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You will learn how to implement the graphical representation of data in order to interactively and efficiently convey insights to clients, customers, and stakeholders in general.
👉 https://cognitiveclass.ai/courses/data-visualization-python#about-course

𝟴. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You will build several neural networks and explore more advanced techniques like natural language processing and reinforcement learning.
👉 https://freecodecamp.org/learn/machine-learning-with-python/

9. Practice Python
👉 https://learnpython.org/

10. Free Python course by datacamp
👉 https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-1-python-basics?ex=1

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Here’s the complete list of 100 JavaScript interview questions:

1. What is JavaScript?
2. What are the data types in JavaScript?
3. What is the difference between null and undefined?
4. Explain the concept of hoisting in JavaScript.
5. What is a closure in JavaScript?
6. What is the difference between “==” and “===” operators in JavaScript?
7. Explain the concept of prototypal inheritance in JavaScript.
8. What are the different ways to define a function in JavaScript?
9. How does event delegation work in JavaScript?
10. What is the purpose of the “this” keyword in JavaScript?
11. What are the different ways to create objects in JavaScript?
12. Explain the concept of callback functions in JavaScript.
13. What is event bubbling and event capturing in JavaScript?
14. What is the purpose of the “bind” method in JavaScript?
15. Explain the concept of AJAX in JavaScript.
16. What is the “typeof” operator used for?
17. How does JavaScript handle errors and exceptions?
18. Explain the concept of event-driven programming in JavaScript.
19. What is the purpose of the “async” and “await” keywords in JavaScript?
20. What is the difference between a deep copy and a shallow copy in JavaScript?
21. How does JavaScript handle memory management?
22. Explain the concept of the event loop in JavaScript.
23. What is the purpose of the “map” method in JavaScript?
24. What is a promise in JavaScript?
25. How do you handle errors in promises?
26. Explain the concept of currying in JavaScript.
27. What is the purpose of the “reduce” method in JavaScript?
28. What is the difference between “null” and “undefined” in JavaScript?
29. What are the different types of loops in JavaScript?
30. What is the difference between “let,” “const,” and “var” in JavaScript?
31. Explain the concept of event propagation in JavaScript.
32. What are the different ways to manipulate the DOM in JavaScript?
33. What is the purpose of the “localStorage” and “sessionStorage” objects?
34. How do you handle asynchronous operations in JavaScript?
35. What is the purpose of the “forEach” method in JavaScript?
36. What are the differences between “let” and “var” in JavaScript?
37. Explain the concept of memoization in JavaScript.
38. What is the purpose of the “splice” method in JavaScript arrays?
39. What is a generator function in JavaScript?
40. How does JavaScript handle variable scoping?
41. What is the purpose of the “split” method in JavaScript?
42. What is the difference between a deep clone and a shallow clone of an object?
43. Explain the concept of the event delegation pattern.
44. What are the differences between JavaScript’s “null” and “undefined”?
45. What is the purpose of the “arguments” object in JavaScript?
46. What are the different ways to define methods in JavaScript objects?
47. Explain the concept of memoization and its benefits.
48. What is the difference between “slice” and “splice” in JavaScript arrays?
49. What is the purpose of the “apply” and “call” methods in JavaScript?
50. Explain the concept of the event loop in JavaScript and how it handles asynchronous operations.
51. What are arrow functions, and how are they different from regular functions?
52. What is the purpose of the “filter” method in JavaScript?
53. Explain the concept of destructuring in JavaScript.
54. What is a callback hell, and how can it be avoided?
55. What are “promises,” and how do they work in JavaScript?
56. Explain the difference between synchronous and asynchronous JavaScript.
57. What is JSON, and how is it used in JavaScript?
58. How can you remove duplicates from an array in JavaScript?
59. What is the purpose of the “includes” method in JavaScript?
60. How do you handle default values in JavaScript functions?
61. What are template literals, and how are they used?
62. Explain the concept of modules in JavaScript.
63. What is CORS, and why is it important?
64. What is the “fetch” API, and how does it work?
65. Explain the concept of immutability in JavaScript.
66. What is the purpose of the “find” method in JavaScript arrays?
67.

How does “import” and “export” work in JavaScript?
68. What is a Proxy object in JavaScript?
69. Explain the difference between “var,” “let,” and “const” scope.
70. How do you flatten an array in JavaScript?
71. What are IIFE (Immediately Invoked Function Expressions)?
72. What is the purpose of the “setTimeout” and “setInterval” functions?
73. Explain the concept of polyfills in JavaScript.
74. What is a WeakMap in JavaScript, and how is it different from a Map?
75. How do you handle data binding in JavaScript?
76. What is the purpose of the “every” and “some” methods in JavaScript arrays?
77. Explain the difference between “==” and “===” in JavaScript.
78. How do you remove properties from an object in JavaScript?
79. What is the purpose of the “Object.freeze” method?
80. How do you check if an object is empty in JavaScript?
81. Explain the concept of throttling in JavaScript.
82. What is a WeakSet in JavaScript?
83. How does JavaScript’s garbage collection work?
84. What is debouncing in JavaScript, and how is it different from throttling?
85. What is the purpose of the “Object.assign” method?
86. Explain the difference between map, filter, and reduce.
87. How do you handle inheritance in JavaScript?
88. What is the difference between synchronous and asynchronous functions?
89. How does the “async” keyword work with functions?
90. What are JavaScript decorators?
91. Explain how to optimize JavaScript code for performance.
92. What is a service worker in JavaScript?
93. How do you use “try...catch” for error handling in JavaScript?
94. Explain how promises can be chained.
95. What are the benefits of using closures in JavaScript?
96. How can you reverse a string in JavaScript?
97. What is a Set in JavaScript?
98. Explain the concept of promises with async/await.
99. How do you use “Object.keys,” “Object.values,” and “Object.entries” in JavaScript?
100. What is the purpose of “Promise.all” in JavaScript?

This list provides a comprehensive overview of JavaScript concepts, making it useful for interview preparation.

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Clear all DSA rounds,

By mastering these 20 DSA patterns

1. Fast and Slow Pointer
- Cycle detection method
- O(1) space efficiency
- Linked list problems

2. Merge Intervals
- Sort and merge
- O(n log n) complexity
- Overlapping interval handling

3. Sliding Window
- Fixed/variable window
- O(n) time optimization
- Subarray/substring problems

4. Islands (Matrix Traversal)
- DFS/BFS traversal
- Connected component detection
- 2D grid problems

5. Two Pointers
- Dual pointer strategy
- Linear time complexity
- Array/list problems

6. Cyclic Sort
- Sorting in cycles
- O(n) time complexity
- Constant space usage

7. In-place Reversal of Linked List
- Reverse without extra space
- O(n) time efficiency
- Pointer manipulation technique

8. Breadth First Search
- Level-by-level traversal
- Uses queue structure
- Shortest path problems

9. Depth First Search
- Recursive/backtracking approach
- Uses stack (or recursion)
- Tree/graph traversal

10. Two Heaps
- Max and min heaps
- Median tracking efficiently
- O(log n) insertions

11. Subsets
- Generate all subsets
- Recursive or iterative
- Backtracking or bitmasking

12. Modified Binary Search
- Search in variations
- O(log n) time
- Rotated/specialized arrays

13. Bitwise XOR
- Toggle bits operation
- O(1) space complexity
- Efficient for pairing

14. Top 'K' elements
- Use heap/quickselect
- O(n log k) time
- Efficient selection problem

15. K-way Merge
- Merge sorted lists
- Min-heap based approach
- O(n log k) complexity

16. 0/1 Knapsack (Dynamic Programming)
- Choose or skip items
- O(n * W) complexity
- Maximize value selection

17. Unbounded Knapsack (Dynamic Programming)
- Unlimited item choices
- O(n * W) complexity
- Multiple item selection

18. Topological Sort (Graphs)
- Directed acyclic graph
- Order dependency resolution
- Uses DFS or BFS

19. Monotonic Stack
- Maintain increasing/decreasing stack
- Optimized for range queries
- O(n) time complexity

20. Backtracking
- Recursive decision-making
- Explore all possibilities
- Pruning with constraints

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Essential Topics to Master Data Science Interviews: 🚀

SQL:
1. Foundations
- Master SELECT statements with WHERE,Essential ToEssential ToEssential - Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Understand simple databases and table structures

2. Intermediate SQL
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Subqueries & nested queries
- Common Table Expressions (WITH clause)
- CASE statements for logical operations

3. Advanced SQL
- Advanced JOIN techniques (self-join, non-equi join)
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG)
- Query optimization with indexing
- Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Python Basics
- Syntax, variables, and data types
- Control structures (`if-else`, for andential Toloops)
- Data structures (lists, dictionaries, sets, tuples)
- Functions, lambda functions, error handling (try-except)
- Modules and packages

2. Pandas & Numpy
- Create and manipulate DataFrames & Series
- Indexing, selecting, and filtering data
- Handle missing data (`fillna`, `dropna`)
- Aggregate data with groupby
- Merge, join, and concatenate datasets

3. Data Visualization with Python
- Plot with Matplotlib (line, bar, histograms)
- Visualize with Seaborn (scatter, box, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Intro to interactive visualizations (e.g., Plotly)

Excel:
1. Excel Essentials
- Basic formulas (`SUMIFS`, COUNTIFS,Essent etc.)
- Charts and basic data visualization
- Sort, filter, and conditional formatting

2. Intermediate Excel
- Advanced formulas (`V/XLOOKUP`, INDEX-MATCH, nested IF)
- PivotTables & PivotCharts
- Data validation and What-if analysis

3. Advanced Excel
- Array formulas and advanced functions
- Power Pivot & Data Model
- Advanced Filter, Slicers, Timelines
- Dynamic charts & interactive dashboards

Power BI:
1. Data Modeling in Power BI
- Import data from multiple sources
- Manage relationships between datasets
- Data modeling basics (star, snowflake schemas)

2. Data Transformation in Power BI
- Data cleaning with Power Query
- Advanced data shaping techniques
- Calculated columns & measures with DAX

3. Data Visualization & Reporting in Power BI
- Interactive reports & dashboards
- Visualizations (bar, line, pie charts, maps)
- Publish, share, & schedule data refreshes

Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation & Variance
- Probability Distributions, Hypothesis Testing
- P-values & Confidence Intervals
- Correlation & Simple Linear Regression
- Normal, Binomial, Poisson Distributions

Show some ❤️ if you're ready to elevate your data science journey! 📊

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