🔰 All Type of Campus Placement Previous Material 🔰
Contain:- 100+ Companies
SIze 30 GB+
⭕Download link:-
https://drive.google.com/drive/folders/1SkCOcAS0Kqvuz-MJkkjbFr1GSue6Ms6m
Contain:- 100+ Companies
SIze 30 GB+
⭕Download link:-
https://drive.google.com/drive/folders/1SkCOcAS0Kqvuz-MJkkjbFr1GSue6Ms6m
Data Analysis with Excel
👇👇
https://t.iss.one/excel_analyst/2
Power BI DAX Functions
👇👇
https://t.iss.one/PowerBI_analyst/2
All about SQL
👇👇
https://t.iss.one/sqlanalyst/29
Python for data analysis
👇👇
https://t.iss.one/pythonanalyst/26
Statistics Book and other useful resources
👇👇
https://t.iss.one/DataAnalystInterview/34
Join channel as per your interest :)
👇👇
https://t.iss.one/excel_analyst/2
Power BI DAX Functions
👇👇
https://t.iss.one/PowerBI_analyst/2
All about SQL
👇👇
https://t.iss.one/sqlanalyst/29
Python for data analysis
👇👇
https://t.iss.one/pythonanalyst/26
Statistics Book and other useful resources
👇👇
https://t.iss.one/DataAnalystInterview/34
Join channel as per your interest :)
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Interview questions asked by top product-based companies.
A friend of mine recently shared their interview journey, and I'd like to pass on what I learned about the data structures and algorithms (DSA) rounds.
👨🏾💻 Data Structures: He encountered questions on topics like arrays, strings, matrices, stacks, queues, and different types of linked lists (singly, doubly, and circular).
▶️ Algorithms: He was also interviewed on a wide array of algorithms like linear search, binary search, and sorting algorithms (bubble, quick, merge).
And faced questions on more challenging subjects like Greedy algorithms, Dynamic programming, and Graph algorithms.
🖛 Specifics: The devil lies in the details! His interview also delved into advanced topics such as Advanced Data Structures, Pattern Searching, Recursion, Backtracking, and Divide and Conquer strategies.
However, your ability to apply these concepts to real-world situations will undoubtedly set you apart from others.
On top, If you’re stuck at any of the above questions and need the right guidance in cracking top product-based company interviews,
As a community of tech enthusiasts, let's share our own interview experiences in the comments below. Together, we can learn from each other's experiences.
A friend of mine recently shared their interview journey, and I'd like to pass on what I learned about the data structures and algorithms (DSA) rounds.
👨🏾💻 Data Structures: He encountered questions on topics like arrays, strings, matrices, stacks, queues, and different types of linked lists (singly, doubly, and circular).
▶️ Algorithms: He was also interviewed on a wide array of algorithms like linear search, binary search, and sorting algorithms (bubble, quick, merge).
And faced questions on more challenging subjects like Greedy algorithms, Dynamic programming, and Graph algorithms.
🖛 Specifics: The devil lies in the details! His interview also delved into advanced topics such as Advanced Data Structures, Pattern Searching, Recursion, Backtracking, and Divide and Conquer strategies.
However, your ability to apply these concepts to real-world situations will undoubtedly set you apart from others.
On top, If you’re stuck at any of the above questions and need the right guidance in cracking top product-based company interviews,
As a community of tech enthusiasts, let's share our own interview experiences in the comments below. Together, we can learn from each other's experiences.
A%2FB Testing 101 for PMs.pdf
662 KB
AB Testing 101 for PMs.pdf
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Product team cases where a #productteams improved content discovery
Case: Netflix and Personalized Content Recommendations
Problem: Netflix wanted to improve user engagement by enhancing content discovery and reducing churn.
Solution: Using a product outcome mindset, Netflix's product team developed a recommendation algorithm that analyzed user viewing behavior and preferences to offer personalized content suggestions.
Outcome: Netflix saw a significant increase in user engagement, with the personalized recommendations leading to higher watch times and reduced churn.
Learn more: You can read about Netflix's recommendation system in various articles and research papers, such as "Netflix Recommendations: Beyond the 5 stars" (by Netflix).
Case: Spotify and Music Discovery
Problem: Spotify users were overwhelmed by the vast music library and struggled to discover new music.
Solution: Spotify's product team used data-driven insights to create personalized playlists like "Discover Weekly" and "Release Radar," tailored to users' listening habits.
Outcome: The personalized playlists increased user engagement, time spent on the platform, and the likelihood of users discovering and enjoying new music.
Link: Learn more about Spotify's approach to music discovery in articles like "How Spotify Discover Weekly and Release Radar Playlist Work" (by The Verge).
Case: Netflix and Personalized Content Recommendations
Problem: Netflix wanted to improve user engagement by enhancing content discovery and reducing churn.
Solution: Using a product outcome mindset, Netflix's product team developed a recommendation algorithm that analyzed user viewing behavior and preferences to offer personalized content suggestions.
Outcome: Netflix saw a significant increase in user engagement, with the personalized recommendations leading to higher watch times and reduced churn.
Learn more: You can read about Netflix's recommendation system in various articles and research papers, such as "Netflix Recommendations: Beyond the 5 stars" (by Netflix).
Case: Spotify and Music Discovery
Problem: Spotify users were overwhelmed by the vast music library and struggled to discover new music.
Solution: Spotify's product team used data-driven insights to create personalized playlists like "Discover Weekly" and "Release Radar," tailored to users' listening habits.
Outcome: The personalized playlists increased user engagement, time spent on the platform, and the likelihood of users discovering and enjoying new music.
Link: Learn more about Spotify's approach to music discovery in articles like "How Spotify Discover Weekly and Release Radar Playlist Work" (by The Verge).
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