IBM Senior Analytics and Data Scientist Interview Questions and Answers

Case study - prize optimization problem

Q1. Structured vs unstructured data
Ans. 
Structured data is organized and easily searchable, while unstructured data lacks a predefined format.
• Structured data is organized into rows and columns, like a database.

• Unstructured data includes text documents, images, videos, and social media posts.

• Structured data is easier to analyze and query, while unstructured data requires more advanced techniques like natural language processing.

• Examples of structured data inc...read more
Answered by AI
Add your answer

Q1. Project related questions from your CV
Add your answer

Q1. Question on transformers
Add your answer
Q2. Comparison of transfer learning and fintuning.
Ans. 
Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.
• Transfer learning uses knowledge gained from one task to improve learning on a different task.

• Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.

• Transfer learning is faster and requires less data compared to training a...read more
Answered by AI
Add your answer

Q1. Overfitting and Underfitting
Add your answer
Q2. Find Nth-largest element
Ans. 
Find Nth-largest element in an array
• Sort the array in descending order

• Return the element at index N-1
Answered by AI
Add your answer
Q3. NLP Data preprocessing
Add your answer

Q1. Salary Discussion
Add your answer
Q2. Fitment discussion
Add your answer

Q1. Tell me about yourself?
Ans. 
I am a data scientist with a background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.
• Background in statistics and machine learning

• Experience in solving complex problems using data-driven approaches

• Passionate about leveraging data to drive insights and decision-making
Answered by AI
Add your answer
Q2. Describe in detail about one of my main project.
Ans. 
Developed a predictive model for customer churn in a telecom company.
• Collected and cleaned customer data including usage patterns and demographics.

• Used machine learning algorithms such as logistic regression and random forest to build the model.

• Evaluated model performance using metrics like accuracy, precision, and recall.

• Implemented the model into the company's CRM system for real-time predictions.
Answered by AI
Add your answer
Q3. Few questions related to my projects.
Add your answer

Q1. Questions on Basics python(Since i am fresher)
Add your answer

Q1. Which GenAI projects I have worked on
Add your answer
Q2. What is the context window in LLMs
Ans. 
Context window in LLMs refers to the number of surrounding words considered when predicting the next word in a sequence.
• Context window helps LLMs capture dependencies between words in a sentence.

• A larger context window allows the model to consider more context but may lead to increased computational complexity.

• For example, in a context window of 2, the model considers 2 words before and 2 words after the target word fo
Answered by AI
Add your answer
Q3. What is top_k parameter
Ans. 
top_k parameter is used to specify the number of top elements to be returned in a result set.
• top_k parameter is commonly used in machine learning algorithms to limit the number of predictions or recommendations.

• For example, in recommendation systems, setting top_k=5 will return the top 5 recommended items for a user.

• In natural language processing tasks, top_k can be used to limit the number of possible next words in a
Answered by AI
Add your answer
Q4. What are regex patterns in python
Ans. 
Regex patterns in Python are sequences of characters that define a search pattern.
• Regex patterns are used for pattern matching and searching in strings.

• They are created using the 're' module in Python.

• Examples of regex patterns include searching for email addresses, phone numbers, or specific words in a text.
Answered by AI
Add your answer
Q5. What are iterators and tuples
Ans. 
Iterators are objects that allow iteration over a sequence of elements. Tuples are immutable sequences of elements.
• Iterators are used to loop through elements in a collection, like lists or dictionaries

• Tuples are similar to lists but are immutable, meaning their elements cannot be changed

• Example of iterator: for item in list: print(item)

• Example of tuple: my_tuple = (1, 2, 3)
Answered by AI
Add your answer
Q6. Do I have REST API experience
Ans. 
Yes, I have experience working with REST APIs in various projects.
• Developed RESTful APIs using Python Flask framework

• Consumed REST APIs in data analysis projects using requests library

• Used Postman for testing and debugging REST APIs
Answered by AI
Add your answer

Q1. Explain XGBoost algoritm
Ans. 
XGBoost is a powerful machine learning algorithm known for its speed and performance in handling large datasets.
• XGBoost stands for eXtreme Gradient Boosting, which is an implementation of gradient boosting machines.

• It is widely used in machine learning competitions and is known for its speed and performance.

• XGBoost uses a technique called boosting, where multiple weak learners are combined to create a strong learner.

• It...read more
Answered by AI
Add your answer
Q2. XgBoost algorithm has 10-20 features. How are the splits decided, on which feature are they going to be divided?
Ans. 
XgBoost algorithm uses a greedy approach to determine splits based on feature importance.
• XgBoost algorithm calculates the information gain for each feature to determine the best split.

• The feature with the highest information gain is chosen for the split.

• This process is repeated recursively for each node in the tree.

• Features can be split based on numerical values or categories.

• Example: If a feature like 'age' has the hi...read more
Answered by AI
View 1 more answer
Q3. Do you have any experience on cloud platform?
Ans. 
Yes, I have experience working on cloud platforms such as AWS and Google Cloud.
• Experience with AWS services like S3, EC2, and Redshift

• Familiarity with Google Cloud services like BigQuery and Compute Engine

• Utilized cloud platforms for data storage, processing, and analysis
Answered by AI
Add your answer
Q4. What is entropy, information gain?
Ans. 
Entropy is a measure of randomness or uncertainty in a dataset, while information gain is the reduction in entropy after splitting a dataset based on a feature.
• Entropy is used in decision tree algorithms to determine the best feature to split on.

• Information gain measures the effectiveness of a feature in classifying the data.

• Higher information gain indicates that a feature is more useful for splitting the data.

• Entropy ...read more
Answered by AI
Add your answer
Q5. What is hypothesis testing?
Ans. 
Hypothesis testing is a statistical method used to make inferences about a population based on sample data.
• Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

• The null hypothesis is assumed to be true until there is enough evidence to reject it.

• Statistical tests are used to determine the likelihood of observing the data if the null hypothesis is true.

• The p-value is used to determine ...read more
Answered by AI
Add your answer
Q6. Explain precision and recall, when are they used in which scenario?
Ans. 
Precision and recall are metrics used in evaluating the performance of classification models.
• Precision measures the accuracy of positive predictions, while recall measures the ability of the model to find all positive instances.

• Precision = TP / (TP + FP)

• Recall = TP / (TP + FN)

• Precision is important when false positives are costly, while recall is important when false negatives are costly.

• For example, in a spam email de...read more
Answered by AI
Add your answer
Q7. What is data imbalance?
Ans. 
Data imbalance refers to unequal distribution of classes in a dataset, where one class has significantly more samples than others.
• Data imbalance can lead to biased models that favor the majority class.

• It can result in poor performance for minority classes, as the model may struggle to accurately predict them.

• Techniques like oversampling, undersampling, and using different evaluation metrics can help address data imbala...read more
Answered by AI
Add your answer
Q8. What is SMOTE? Do you have any experience working on Time Series? Code analysis of global variable?
Ans. 
SMOTE stands for Synthetic Minority Over-sampling Technique, used to balance imbalanced datasets by generating synthetic samples.
• SMOTE is commonly used in machine learning to address class imbalance by creating synthetic samples of the minority class.

• It works by generating new instances of the minority class by interpolating between existing instances.

• SMOTE is particularly useful in scenarios where the minority class i...read more
Answered by AI
Add your answer
Q9. Find 5th highest salary in every department. What are window functions Difference between union and union all Difference between delete and truncate.
Add your answer

Q1. DFA Focus :Sorting ,Searching ,Stacks,Queues, HashMaps
Add your answer
Q2. Os & cn: Process scheduling, TCP/IP, HTTP basics
Add your answer

Q1. Asked questions related to stats , Senario based. Interviewer was very nice
Add your answer
Q2. Asked questions on Python Programming and asked for Programs Like How can update List , Asked to Build a Tough Dict , Class etc.
Add your answer

Q1. Why u want to join?
Add your answer
Q2. What is your future goal?
Add your answer