Coding Interview ⛥
Python Learning Series Part-12 Complete Python Topics for Data Analysis: Natural Language Processing (NLP) Natural Language Processing involves working with human language data, enabling computers to understand, interpret, and generate human-like text.…
Python Learning Series Part-13
Deep Learning Basics with TensorFlow:
Deep Learning is a subset of machine learning that involves neural networks with multiple layers (deep neural networks). TensorFlow is an open-source deep learning library developed by Google.
1. Introduction to Neural Networks:
- Perceptrons and Activation Functions:
- Basic building blocks of neural networks.
- Activation Functions:
- Functions like ReLU or sigmoid introduce non-linearity.
2. Building Neural Networks:
- Sequential Model:
- A linear stack of layers.
- Compiling the Model:
- Specify optimizer, loss function, and metrics.
3. Training Neural Networks:
- Fit Method:
- Train the model on training data.
- Model Evaluation:
- Assess the model's performance on test data.
4. Convolutional Neural Networks (CNNs):
- Convolutional Layers:
- Specialized layers for image data.
- Pooling Layers:
- Reduce dimensionality.
5. Recurrent Neural Networks (RNNs):
- LSTM Layers:
- Handle sequences of data.
- Embedding Layers:
- Convert words to vectors in natural language processing.
Deep learning with TensorFlow is powerful for handling complex tasks like image recognition and sequence processing.
Hope it helps :)
Deep Learning Basics with TensorFlow:
Deep Learning is a subset of machine learning that involves neural networks with multiple layers (deep neural networks). TensorFlow is an open-source deep learning library developed by Google.
1. Introduction to Neural Networks:
- Perceptrons and Activation Functions:
- Basic building blocks of neural networks.
import tensorflow as tf
# Create a simple perceptron
perceptron = tf.keras.layers.Dense(units=1, activation='sigmoid', input_shape=(input_size,))
- Activation Functions:
- Functions like ReLU or sigmoid introduce non-linearity.
activation_relu = tf.keras.layers.Activation('relu')
activation_sigmoid = tf.keras.layers.Activation('sigmoid')
2. Building Neural Networks:
- Sequential Model:
- A linear stack of layers.
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(input_size,)),
tf.keras.layers.Dense(1, activation='sigmoid')
])
- Compiling the Model:
- Specify optimizer, loss function, and metrics.
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
3. Training Neural Networks:
- Fit Method:
- Train the model on training data.
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))
- Model Evaluation:
- Assess the model's performance on test data.
test_loss, test_accuracy = model.evaluate(X_test, y_test)
4. Convolutional Neural Networks (CNNs):
- Convolutional Layers:
- Specialized layers for image data.
model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu', input_shape=(height, width, channels)))
- Pooling Layers:
- Reduce dimensionality.
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
5. Recurrent Neural Networks (RNNs):
- LSTM Layers:
- Handle sequences of data.
model.add(tf.keras.layers.LSTM(units=50, return_sequences=True, input_shape=(timesteps, features)))
- Embedding Layers:
- Convert words to vectors in natural language processing.
model.add(tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length))
Deep learning with TensorFlow is powerful for handling complex tasks like image recognition and sequence processing.
Hope it helps :)
👍2
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Coding Interview ⛥
Python Learning Series Part-13 Deep Learning Basics with TensorFlow: Deep Learning is a subset of machine learning that involves neural networks with multiple layers (deep neural networks). TensorFlow is an open-source deep learning library developed by…
Python Learning Series Part-14
14. Transfer Learning with Pre-trained Models:
Transfer learning involves using pre-trained models as a starting point for a new task. It's a powerful technique that leverages the knowledge gained from training on large datasets.
1. Introduction to Transfer Learning:
- Why Transfer Learning?
- Utilize knowledge learned from one task to improve performance on a different, but related, task.
- Pre-trained Models:
- Models trained on massive datasets, such as ImageNet, that capture general features of images, text, or other data.
2. Transfer Learning in Computer Vision:
- Fine-tuning Pre-trained Models:
- Adjust the weights of a pre-trained model on a smaller dataset for a specific task.
- Feature Extraction:
- Use pre-trained models as feature extractors.
3. Transfer Learning in Natural Language Processing:
- Using Pre-trained Embeddings:
- Utilize word embeddings trained on large text corpora.
- Fine-tuning Language Models:
- Fine-tune models like BERT for specific tasks.
Transfer learning accelerates model development by leveraging pre-existing knowledge.
Hope it helps :)
14. Transfer Learning with Pre-trained Models:
Transfer learning involves using pre-trained models as a starting point for a new task. It's a powerful technique that leverages the knowledge gained from training on large datasets.
1. Introduction to Transfer Learning:
- Why Transfer Learning?
- Utilize knowledge learned from one task to improve performance on a different, but related, task.
- Pre-trained Models:
- Models trained on massive datasets, such as ImageNet, that capture general features of images, text, or other data.
2. Transfer Learning in Computer Vision:
- Fine-tuning Pre-trained Models:
- Adjust the weights of a pre-trained model on a smaller dataset for a specific task.
base_model = tf.keras.applications.MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
base_model.trainable = False # Freeze the pre-trained layers
model = tf.keras.Sequential([
base_model,
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(10, activation='softmax')
])
- Feature Extraction:
- Use pre-trained models as feature extractors.
base_model = tf.keras.applications.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
for layer in base_model.layers:
layer.trainable = False # Freeze pre-trained layers
model = tf.keras.Sequential([
base_model,
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(10, activation='softmax')
])
3. Transfer Learning in Natural Language Processing:
- Using Pre-trained Embeddings:
- Utilize word embeddings trained on large text corpora.
embeddings_index = load_pretrained_word_embeddings()
embedding_matrix = create_embedding_matrix(word_index, embeddings_index)
embedding_layer = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, weights=[embedding_matrix], input_length=max_length)
- Fine-tuning Language Models:
- Fine-tune models like BERT for specific tasks.
bert_model = TFBertModel.from_pretrained('bert-base-uncased')
Transfer learning accelerates model development by leveraging pre-existing knowledge.
Hope it helps :)
👍1
How to send follow up email to a recruiter 👇👇
Dear [Recruiter’s Name],
I hope this email finds you doing well. I wanted to take a moment to express my sincere gratitude for the time and consideration you have given me throughout the recruitment process for the [position] role at [company].
I understand that you must be extremely busy and receive countless applications, so I wanted to reach out and follow up on the status of my application. If it’s not too much trouble, could you kindly provide me with any updates or feedback you may have?
I want to assure you that I remain genuinely interested in the opportunity to join the team at [company] and I would be honored to discuss my qualifications further. If there are any additional materials or information you require from me, please don’t hesitate to let me know.
Thank you for your time and consideration. I appreciate the effort you put into recruiting and look forward to hearing from you soon.
Warmest regards,
Like if helps
👉Telegram Link: https://t.me/addlist/wcoDjKedDTBhNzFl
All the best 👍👍
Dear [Recruiter’s Name],
I hope this email finds you doing well. I wanted to take a moment to express my sincere gratitude for the time and consideration you have given me throughout the recruitment process for the [position] role at [company].
I understand that you must be extremely busy and receive countless applications, so I wanted to reach out and follow up on the status of my application. If it’s not too much trouble, could you kindly provide me with any updates or feedback you may have?
I want to assure you that I remain genuinely interested in the opportunity to join the team at [company] and I would be honored to discuss my qualifications further. If there are any additional materials or information you require from me, please don’t hesitate to let me know.
Thank you for your time and consideration. I appreciate the effort you put into recruiting and look forward to hearing from you soon.
Warmest regards,
Like if helps
👉Telegram Link: https://t.me/addlist/wcoDjKedDTBhNzFl
All the best 👍👍
👍1
Leetcode Questions you can check to Learn DSA from scratch 👇👇
1️⃣ Arrays: Data structures, such as arrays, store elements in contiguous memory locations. They are versatile and useful for a wide variety of purposes.
LeetCode Problems:
• Search in Rotated Sorted Array (Problem #33)
• Product of Array Except Self (Problem #238)
• Find the Missing Number (Problem #268)
2️⃣Two Pointers: In Two Pointers, two pointers are maintained in the collection and can be manipulated to solve a problem efficiently.
LeetCode problems:
• Trapping Rain Water (Problem #42)
• Longest Substring Without Repeating Characters (Problem #3)
• Squares of a Sorted Array (Problem #977)
3️⃣In-place Linked List Traversal: As an explanation, in-place traversal is a technique for modifying linked list nodes without using extra space.
LeetCode Problems:
• Remove Nth Node From End of List (Problem #19)
• Reorder List (Problem #143)
4️⃣Fast & Slow Pointers: This pattern uses two pointers to traverse a sequence at different speeds (fast and slow), often used to detect cycles or find a specific position in the sequence.
LeetCode Problems:
• Happy Number (Problem #202)
• Subarray Sum Equals K (Problem #560)
• Intersection of Two Linked Lists (Problem #160)
5️⃣Merge Intervals: This pattern involves merging overlapping intervals in a collection, often used in problems dealing with intervals or ranges.
LeetCode problems:
• Non-overlapping Intervals (Problem #435)
• Minimum Number of Arrows to Burst Balloons (Problem #452)
Join for more: https://t.me/crackingthecodinginterviews
ENJOY LEARNING 👍👍
1️⃣ Arrays: Data structures, such as arrays, store elements in contiguous memory locations. They are versatile and useful for a wide variety of purposes.
LeetCode Problems:
• Search in Rotated Sorted Array (Problem #33)
• Product of Array Except Self (Problem #238)
• Find the Missing Number (Problem #268)
2️⃣Two Pointers: In Two Pointers, two pointers are maintained in the collection and can be manipulated to solve a problem efficiently.
LeetCode problems:
• Trapping Rain Water (Problem #42)
• Longest Substring Without Repeating Characters (Problem #3)
• Squares of a Sorted Array (Problem #977)
3️⃣In-place Linked List Traversal: As an explanation, in-place traversal is a technique for modifying linked list nodes without using extra space.
LeetCode Problems:
• Remove Nth Node From End of List (Problem #19)
• Reorder List (Problem #143)
4️⃣Fast & Slow Pointers: This pattern uses two pointers to traverse a sequence at different speeds (fast and slow), often used to detect cycles or find a specific position in the sequence.
LeetCode Problems:
• Happy Number (Problem #202)
• Subarray Sum Equals K (Problem #560)
• Intersection of Two Linked Lists (Problem #160)
5️⃣Merge Intervals: This pattern involves merging overlapping intervals in a collection, often used in problems dealing with intervals or ranges.
LeetCode problems:
• Non-overlapping Intervals (Problem #435)
• Minimum Number of Arrows to Burst Balloons (Problem #452)
Join for more: https://t.me/crackingthecodinginterviews
ENJOY LEARNING 👍👍
👍3
RtBrick questions
1) explain types of ipc( inter-process techniques )
2) how would you implement a custom memory allocator , given that you should also be able to detect memory leaks , and detection to follow sequential order of allocations.
3) if I connect two pc's directly using a cable , will ip be needed to send packets
4) difference between private and public IP
5) why shared_memory is faster than other techniques
6) how to optimise a code from data perspective, how to optimise from loop( instructions ) perspective
7) why pointer sizes remains constant
8) why struct padding is needed
9) meta and data blocks
10) how to debug a core file , what steps , how to backtrace
11) location of shared_memory , static vs shared libs
12) connect 2 routers, now in order to ping these two, will arp be used or not , and how does ping work
13) when to use TCP and UDP
14) different regions in a process memory and where do mapping happen
15) why virtual memory and how does process know by demand paging which page to fetch?? How do you even divide a process into pages as a process / program is a file running
16) when to use an event I/O mechanism and pub - sub model
17) if 2 process are exceeding cpu cycles , and you know the root cause is some anomaly in the event i/o mechanism, how would you go about debugging it
18) explain symmetric vs asymmetric cryptography, what is a private key and public key, explain any common cryptography asymmetric algo
19) when you use a vpn, from which side is it feasible to inspect the public IP of the source ? What are SSL certificates, how to authenticate using SSL certificates
20) explain master to peer slave architecture, how to debug issues in a typical consensus self election algothrm
21) what is a little endian and big endian system and how to detect one . When sending data into a network , can you directly send little endian data to big endian or not ??? If yes , how, if no , why and how ?
------------DSA-------------
BST, DLL, insertion deletion searching in them, any balanced BST, longest subarray without repeat chars, common but manipulation techniques
1) explain types of ipc( inter-process techniques )
2) how would you implement a custom memory allocator , given that you should also be able to detect memory leaks , and detection to follow sequential order of allocations.
3) if I connect two pc's directly using a cable , will ip be needed to send packets
4) difference between private and public IP
5) why shared_memory is faster than other techniques
6) how to optimise a code from data perspective, how to optimise from loop( instructions ) perspective
7) why pointer sizes remains constant
8) why struct padding is needed
9) meta and data blocks
10) how to debug a core file , what steps , how to backtrace
11) location of shared_memory , static vs shared libs
12) connect 2 routers, now in order to ping these two, will arp be used or not , and how does ping work
13) when to use TCP and UDP
14) different regions in a process memory and where do mapping happen
15) why virtual memory and how does process know by demand paging which page to fetch?? How do you even divide a process into pages as a process / program is a file running
16) when to use an event I/O mechanism and pub - sub model
17) if 2 process are exceeding cpu cycles , and you know the root cause is some anomaly in the event i/o mechanism, how would you go about debugging it
18) explain symmetric vs asymmetric cryptography, what is a private key and public key, explain any common cryptography asymmetric algo
19) when you use a vpn, from which side is it feasible to inspect the public IP of the source ? What are SSL certificates, how to authenticate using SSL certificates
20) explain master to peer slave architecture, how to debug issues in a typical consensus self election algothrm
21) what is a little endian and big endian system and how to detect one . When sending data into a network , can you directly send little endian data to big endian or not ??? If yes , how, if no , why and how ?
------------DSA-------------
BST, DLL, insertion deletion searching in them, any balanced BST, longest subarray without repeat chars, common but manipulation techniques
👍2
How to become a better software developer:
1. Admit you don't know it all
2. Practice, practice, practice
3. Take every opportunity to learn
4. Accept your peers as a valuable source of knowledge
5. Embrace failure as a way to grow
1. Admit you don't know it all
2. Practice, practice, practice
3. Take every opportunity to learn
4. Accept your peers as a valuable source of knowledge
5. Embrace failure as a way to grow
Typical java interview questions sorted by experience
Junior
* Name some of the characteristics of OO programming languages
* What are the access modifiers you know? What does each one do?
* What is the difference between overriding and overloading a method in Java?
* What’s the difference between an Interface and an abstract class?
* Can an Interface extend another Interface?
* What does the static word mean in Java?
* Can a static method be overridden in Java?
* What is Polymorphism? What about Inheritance?
* Can a constructor be inherited?
* Do objects get passed by reference or value in Java? Elaborate on that.
* What’s the difference between using == and .equals on a string?
* What is the hashCode() and equals() used for?
* What does the interface Serializable do? What about Parcelable in Android?
* Why are Array and ArrayList different? When would you use each?
* What’s the difference between an Integer and int?
* What is a ThreadPool? Is it better than using several “simple” threads?
* What the difference between local, instance and class variables?
Mid
* What is reflection?
* What is dependency injection? Can you name a few libraries? (Have you used any?)
* What are strong, soft and weak references in Java?
* What does the keyword synchronized mean?
* Can you have “memory leaks” on Java?
* Do you need to set references to null on Java/Android?
* What does it means to say that a String is immutable?
* What are transient and volatile modifiers?
* What is the finalize() method?
* How does the try{} finally{} works?
* What is the difference between instantiation and initialisation of an object?
* When is a static block run?
* Why are Generics are used in Java?
* Can you mention the design patterns you know? Which of those do you normally use?
* Can you mention some types of testing you know?
Senior
* How does Integer.parseInt() works?
* Do you know what is the “double check locking” problem?
* Do you know the difference between StringBuffer and StringBuilder?
* How is a StringBuilder implemented to avoid the immutable string allocation problem?
* What does Class.forName method do?
* What is Autoboxing and Unboxing?
* What’s the difference between an Enumeration and an Iterator?
* What is the difference between fail-fast and fail safe in Java?
* What is PermGen in Java?
* What is a Java priority queue?
* *s performance influenced by using the same number in different types: Int, Double and Float?
* What is the Java Heap?
* What is daemon thread?
* Can a dead thread be restarted?
Source: medium.
ENJOY LEARNING 👍👍
Junior
* Name some of the characteristics of OO programming languages
* What are the access modifiers you know? What does each one do?
* What is the difference between overriding and overloading a method in Java?
* What’s the difference between an Interface and an abstract class?
* Can an Interface extend another Interface?
* What does the static word mean in Java?
* Can a static method be overridden in Java?
* What is Polymorphism? What about Inheritance?
* Can a constructor be inherited?
* Do objects get passed by reference or value in Java? Elaborate on that.
* What’s the difference between using == and .equals on a string?
* What is the hashCode() and equals() used for?
* What does the interface Serializable do? What about Parcelable in Android?
* Why are Array and ArrayList different? When would you use each?
* What’s the difference between an Integer and int?
* What is a ThreadPool? Is it better than using several “simple” threads?
* What the difference between local, instance and class variables?
Mid
* What is reflection?
* What is dependency injection? Can you name a few libraries? (Have you used any?)
* What are strong, soft and weak references in Java?
* What does the keyword synchronized mean?
* Can you have “memory leaks” on Java?
* Do you need to set references to null on Java/Android?
* What does it means to say that a String is immutable?
* What are transient and volatile modifiers?
* What is the finalize() method?
* How does the try{} finally{} works?
* What is the difference between instantiation and initialisation of an object?
* When is a static block run?
* Why are Generics are used in Java?
* Can you mention the design patterns you know? Which of those do you normally use?
* Can you mention some types of testing you know?
Senior
* How does Integer.parseInt() works?
* Do you know what is the “double check locking” problem?
* Do you know the difference between StringBuffer and StringBuilder?
* How is a StringBuilder implemented to avoid the immutable string allocation problem?
* What does Class.forName method do?
* What is Autoboxing and Unboxing?
* What’s the difference between an Enumeration and an Iterator?
* What is the difference between fail-fast and fail safe in Java?
* What is PermGen in Java?
* What is a Java priority queue?
* *s performance influenced by using the same number in different types: Int, Double and Float?
* What is the Java Heap?
* What is daemon thread?
* Can a dead thread be restarted?
Source: medium.
ENJOY LEARNING 👍👍
👍3❤2
Mastering Apache Airflow: Top Interview Questions!
1. What is Apache Airflow?
2. Is Apache Airflow an ETL tool?
3. How do we define workflows in Apache Airflow?
4. What are the components of the Apache Airflow architecture?
5. What are Local Executors and their types in Airflow?
6. What is a Celery Executor?
7. How is Kubernetes Executor different from Celery Executor?
8. What are Variables (Variable Class) in Apache Airflow?
9. What is the purpose of Airflow XComs?
10. What are the states a Task can be in? Define an ideal task flow.
11. What is the role of Airflow Operators?
12. How does airflow communicate with a third party (S3, Postgres, MySQL)?
13. What are the basic steps to create a DAG?
14. What is Branching in Directed Acyclic Graphs (DAGs)?
15. What are ways to Control Airflow Workflow?
16. Explain the External task Sensor.
17. What are the ways to monitor Apache Airflow?
18. What is TaskFlow API? and how is it helpful?
19. How are Connections used in Apache Airflow?
20. Explain Dynamic DAGs.
21. What are some of the most useful Airflow CLI commands?
22. How to control the parallelism or concurrency of tasks in Apache Airflow configuration?
23. What do you understand by Jinja Templating?
24. What are Macros in Airflow?
25. What are the limitations of TaskFlow API?
Share with your friends, colleagues and college groups!
1. What is Apache Airflow?
2. Is Apache Airflow an ETL tool?
3. How do we define workflows in Apache Airflow?
4. What are the components of the Apache Airflow architecture?
5. What are Local Executors and their types in Airflow?
6. What is a Celery Executor?
7. How is Kubernetes Executor different from Celery Executor?
8. What are Variables (Variable Class) in Apache Airflow?
9. What is the purpose of Airflow XComs?
10. What are the states a Task can be in? Define an ideal task flow.
11. What is the role of Airflow Operators?
12. How does airflow communicate with a third party (S3, Postgres, MySQL)?
13. What are the basic steps to create a DAG?
14. What is Branching in Directed Acyclic Graphs (DAGs)?
15. What are ways to Control Airflow Workflow?
16. Explain the External task Sensor.
17. What are the ways to monitor Apache Airflow?
18. What is TaskFlow API? and how is it helpful?
19. How are Connections used in Apache Airflow?
20. Explain Dynamic DAGs.
21. What are some of the most useful Airflow CLI commands?
22. How to control the parallelism or concurrency of tasks in Apache Airflow configuration?
23. What do you understand by Jinja Templating?
24. What are Macros in Airflow?
25. What are the limitations of TaskFlow API?
Share with your friends, colleagues and college groups!
👍1
Common Programming Interview Questions
How do you reverse a string?
How do you determine if a string is a palindrome?
How do you calculate the number of numerical digits in a string?
How do you find the count for the occurrence of a particular character in a string?
How do you find the non-matching characters in a string?
How do you find out if the two given strings are anagrams?
How do you calculate the number of vowels and consonants in a string?
How do you total all of the matching integer elements in an array?
How do you reverse an array?
How do you find the maximum element in an array?
How do you sort an array of integers in ascending order?
How do you print a Fibonacci sequence using recursion?
How do you calculate the sum of two integers?
How do you find the average of numbers in a list?
How do you check if an integer is even or odd?
How do you find the middle element of a linked list?
How do you remove a loop in a linked list?
How do you merge two sorted linked lists?
How do you implement binary search to find an element in a sorted array?
How do you print a binary tree in vertical order?
Conceptual Coding Interview Questions
What is a data structure?
What is an array?
What is a linked list?
What is the difference between an array and a linked list?
What is LIFO?
What is FIFO?
What is a stack?
What are binary trees?
What are binary search trees?
What is object-oriented programming?
What is the purpose of a loop in programming?
What is a conditional statement?
What is debugging?
What is recursion?
What are the differences between linear and non-linear data structures?
General Coding Interview Questions
What programming languages do you have experience working with?
Describe a time you faced a challenge in a project you were working on and how you overcame it.
Walk me through a project you’re currently or have recently worked on.
Give an example of a project you worked on where you had to learn a new programming language or technology. How did you go about learning it?
How do you ensure your code is readable by other developers?
What are your interests outside of programming?
How do you keep your skills sharp and up to date?
How do you collaborate on projects with non-technical team members?
Tell me about a time when you had to explain a complex technical concept to a non-technical team member.
How do you get started on a new coding project?
All the best 👍👍
How do you reverse a string?
How do you determine if a string is a palindrome?
How do you calculate the number of numerical digits in a string?
How do you find the count for the occurrence of a particular character in a string?
How do you find the non-matching characters in a string?
How do you find out if the two given strings are anagrams?
How do you calculate the number of vowels and consonants in a string?
How do you total all of the matching integer elements in an array?
How do you reverse an array?
How do you find the maximum element in an array?
How do you sort an array of integers in ascending order?
How do you print a Fibonacci sequence using recursion?
How do you calculate the sum of two integers?
How do you find the average of numbers in a list?
How do you check if an integer is even or odd?
How do you find the middle element of a linked list?
How do you remove a loop in a linked list?
How do you merge two sorted linked lists?
How do you implement binary search to find an element in a sorted array?
How do you print a binary tree in vertical order?
Conceptual Coding Interview Questions
What is a data structure?
What is an array?
What is a linked list?
What is the difference between an array and a linked list?
What is LIFO?
What is FIFO?
What is a stack?
What are binary trees?
What are binary search trees?
What is object-oriented programming?
What is the purpose of a loop in programming?
What is a conditional statement?
What is debugging?
What is recursion?
What are the differences between linear and non-linear data structures?
General Coding Interview Questions
What programming languages do you have experience working with?
Describe a time you faced a challenge in a project you were working on and how you overcame it.
Walk me through a project you’re currently or have recently worked on.
Give an example of a project you worked on where you had to learn a new programming language or technology. How did you go about learning it?
How do you ensure your code is readable by other developers?
What are your interests outside of programming?
How do you keep your skills sharp and up to date?
How do you collaborate on projects with non-technical team members?
Tell me about a time when you had to explain a complex technical concept to a non-technical team member.
How do you get started on a new coding project?
All the best 👍👍
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🚨Here is a comprehensive list of #interview questions that are commonly asked in job interviews for Data Scientist, Data Analyst, and Data Engineer positions:
➡️ Data Scientist Interview Questions
Technical Questions
1) What are your preferred programming languages for data science, and why?
2) Can you write a Python script to perform data cleaning on a given dataset?
3) Explain the Central Limit Theorem.
4) How do you handle missing data in a dataset?
5) Describe the difference between supervised and unsupervised learning.
6) How do you select the right algorithm for your model?
Questions Related To Problem-Solving and Projects
7) Walk me through a data science project you have worked on.
8) How did you handle data preprocessing in your project?
9) How do you evaluate the performance of a machine learning model?
10) What techniques do you use to prevent overfitting?
➡️Data Analyst Interview Questions
Technical Questions
1) Write a SQL query to find the second highest salary from the employee table.
2) How would you optimize a slow-running query?
3) How do you use pivot tables in Excel?
4) Explain the VLOOKUP function.
5) How do you handle outliers in your data?
6) Describe the steps you take to clean a dataset.
Analytical Questions
7) How do you interpret data to make business decisions?
8) Give an example of a time when your analysis directly influenced a business decision.
9) What are your preferred tools for data analysis and why?
10) How do you ensure the accuracy of your analysis?
➡️Data Engineer Interview Questions
Technical Questions
1) What is your experience with SQL and NoSQL databases?
2) How do you design a scalable database architecture?
3) Explain the ETL process you follow in your projects.
4) How do you handle data transformation and loading efficiently?
5) What is your experience with Hadoop/Spark?
6) How do you manage and process large datasets?
Questions Related To Problem-Solving and Optimization
7) Describe a data pipeline you have built.
8) What challenges did you face, and how did you overcome them?
9) How do you ensure your data processes run efficiently?
10) Describe a time when you had to optimize a slow data pipeline.
➡️ Data Scientist Interview Questions
Technical Questions
1) What are your preferred programming languages for data science, and why?
2) Can you write a Python script to perform data cleaning on a given dataset?
3) Explain the Central Limit Theorem.
4) How do you handle missing data in a dataset?
5) Describe the difference between supervised and unsupervised learning.
6) How do you select the right algorithm for your model?
Questions Related To Problem-Solving and Projects
7) Walk me through a data science project you have worked on.
8) How did you handle data preprocessing in your project?
9) How do you evaluate the performance of a machine learning model?
10) What techniques do you use to prevent overfitting?
➡️Data Analyst Interview Questions
Technical Questions
1) Write a SQL query to find the second highest salary from the employee table.
2) How would you optimize a slow-running query?
3) How do you use pivot tables in Excel?
4) Explain the VLOOKUP function.
5) How do you handle outliers in your data?
6) Describe the steps you take to clean a dataset.
Analytical Questions
7) How do you interpret data to make business decisions?
8) Give an example of a time when your analysis directly influenced a business decision.
9) What are your preferred tools for data analysis and why?
10) How do you ensure the accuracy of your analysis?
➡️Data Engineer Interview Questions
Technical Questions
1) What is your experience with SQL and NoSQL databases?
2) How do you design a scalable database architecture?
3) Explain the ETL process you follow in your projects.
4) How do you handle data transformation and loading efficiently?
5) What is your experience with Hadoop/Spark?
6) How do you manage and process large datasets?
Questions Related To Problem-Solving and Optimization
7) Describe a data pipeline you have built.
8) What challenges did you face, and how did you overcome them?
9) How do you ensure your data processes run efficiently?
10) Describe a time when you had to optimize a slow data pipeline.
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Coding Interview ⛥ pinned «🚨Here is a comprehensive list of #interview questions that are commonly asked in job interviews for Data Scientist, Data Analyst, and Data Engineer positions: ➡️ Data Scientist Interview Questions Technical Questions 1) What are your preferred programming…»
Study these 45 problems well and you have prepared for 99% of your System Design Interview:
𝐄𝐚𝐬𝐲
1. Design URL Shortener like TinyURL
2. Design Text Storage Service like Pastebin
3. Design Content Delivery Network (CDN)
4. Design Parking Garage
5. Design Vending Machine
6. Design Distributed Key-Value Store
7. Design Distributed Cache
8. Design Distributed Job Scheduler
9. Design Authentication System
10. Design Unified Payments Interface (UPI)
𝐌𝐞𝐝𝐢𝐮𝐦
11. Design Instagram
12. Design Tinder
13. Design WhatsApp
14. Design Facebook
15. Design Twitter
16. Design Reddit
17. Design Netflix
18. Design Youtube
19. Design Google Search
20. Design E-commerce Store like Amazon
21. Design Spotify
22. Design TikTok
23. Design Shopify
24. Design Airbnb
25. Design Autocomplete for Search Engines
26. Design Rate Limiter
27. Design Distributed Message Queue like Kafka
28. Design Flight Booking System
29. Design Online Code Editor
30. Design Stock Exchange System
31. Design an Analytics Platform (Metrics & Logging)
32. Design Notification Service
33. Design Payment System
𝐇𝐚𝐫𝐝
34. Design Location Based Service like Yelp
35. Design Uber
36. Design Food Delivery App like Doordash
37. Design Google Docs
38. Design Google Maps
39. Design Zoom
40. Design File Sharing System like Dropbox
41. Design Ticket Booking System like BookMyShow
42. Design Distributed Web Crawler
43. Design Code Deployment System
44. Design Distributed Cloud Storage like S3
45. Design Distributed Locking Service
𝐄𝐚𝐬𝐲
1. Design URL Shortener like TinyURL
2. Design Text Storage Service like Pastebin
3. Design Content Delivery Network (CDN)
4. Design Parking Garage
5. Design Vending Machine
6. Design Distributed Key-Value Store
7. Design Distributed Cache
8. Design Distributed Job Scheduler
9. Design Authentication System
10. Design Unified Payments Interface (UPI)
𝐌𝐞𝐝𝐢𝐮𝐦
11. Design Instagram
12. Design Tinder
13. Design WhatsApp
14. Design Facebook
15. Design Twitter
16. Design Reddit
17. Design Netflix
18. Design Youtube
19. Design Google Search
20. Design E-commerce Store like Amazon
21. Design Spotify
22. Design TikTok
23. Design Shopify
24. Design Airbnb
25. Design Autocomplete for Search Engines
26. Design Rate Limiter
27. Design Distributed Message Queue like Kafka
28. Design Flight Booking System
29. Design Online Code Editor
30. Design Stock Exchange System
31. Design an Analytics Platform (Metrics & Logging)
32. Design Notification Service
33. Design Payment System
𝐇𝐚𝐫𝐝
34. Design Location Based Service like Yelp
35. Design Uber
36. Design Food Delivery App like Doordash
37. Design Google Docs
38. Design Google Maps
39. Design Zoom
40. Design File Sharing System like Dropbox
41. Design Ticket Booking System like BookMyShow
42. Design Distributed Web Crawler
43. Design Code Deployment System
44. Design Distributed Cloud Storage like S3
45. Design Distributed Locking Service
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Coding Interview ⛥ pinned «Study these 45 problems well and you have prepared for 99% of your System Design Interview: 𝐄𝐚𝐬𝐲 1. Design URL Shortener like TinyURL 2. Design Text Storage Service like Pastebin 3. Design Content Delivery Network (CDN) 4. Design Parking Garage 5. Design…»
Data Analyst Interview QnA
1. Find avg of salaries department wise from table.
Answer-
2. What does Filter context in DAX mean?
Answer - Filter context in DAX refers to the subset of data that is actively being used in the calculation of a measure or in the evaluation of an expression. This context is determined by filters on the dashboard items like slicers, visuals, and filters pane which restrict the data being processed.
3. Explain how to implement Row-Level Security (RLS) in Power BI.
Answer - Row-Level Security (RLS) in Power BI can be implemented by:
- Creating roles within the Power BI service.
- Defining DAX expressions that specify the data each role can access.
- Assigning users to these roles either in Power BI or dynamically through AD group membership.
4. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
Answer -
5. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.
Answer -
1. Find avg of salaries department wise from table.
Answer-
SELECT department_id, AVG(salary) AS avg_salary
FROM employees
GROUP BY department_id;
2. What does Filter context in DAX mean?
Answer - Filter context in DAX refers to the subset of data that is actively being used in the calculation of a measure or in the evaluation of an expression. This context is determined by filters on the dashboard items like slicers, visuals, and filters pane which restrict the data being processed.
3. Explain how to implement Row-Level Security (RLS) in Power BI.
Answer - Row-Level Security (RLS) in Power BI can be implemented by:
- Creating roles within the Power BI service.
- Defining DAX expressions that specify the data each role can access.
- Assigning users to these roles either in Power BI or dynamically through AD group membership.
4. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
Answer -
d = {'apple': 2, 'banana': 5}
d['orange'] = 3 # Add element
d['apple'] = 4 # Modify element
sorted_d = dict(sorted(d.items())) # Sort dictionary
print(sorted_d)5. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.
Answer -
from collections import Counter
numbers = [1, 2, 2, 3, 4, 5, 1, 6, 7, 3, 8, 1]
count = Counter(numbers)
duplicates = {k: v for k, v in count.items() if v > 1}
print(duplicates)
Coding Interview ⛥ pinned «Data Analyst Interview QnA 1. Find avg of salaries department wise from table. Answer- SELECT department_id, AVG(salary) AS avg_salary FROM employees GROUP BY department_id; 2. What does Filter context in DAX mean? Answer - Filter context in DAX refers…»
Many data scientists don't know how to push ML models to production. Here's the recipe 👇
𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀
🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data
🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time
🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model
🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github
🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API
🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API
🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker
🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs
🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring
𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲
𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling.
𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁
Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.
𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻
Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment
𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁
Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.
𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴
Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data.
Like if you need similar content 😄👍
𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀
🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data
🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time
🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model
🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github
🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API
🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API
🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker
🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs
🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring
𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲
𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling.
𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁
Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.
𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻
Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment
𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁
Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.
𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴
Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data.
Like if you need similar content 😄👍
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