100+ Data Scientist Interview Questions and Answers for Freshers

Linear Regression is used for predicting continuous numerical values, while Logistic Regression is used for predicting binary categorical values.

• Linear Regression predicts a continuous output, while Logistic Regression predicts a binary output.

• Linear Regression uses a linear equation to model the relationship between the independent and dependent variables, while Logistic Regression uses a logistic function.

• Linear Regression assumes a linear relationship between the variables...read more

Mission learning is used for data analysis and prediction with additional algorithms for AI.

• Mission learning is a subset of machine learning that focuses on predicting outcomes based on data analysis.

• It involves using algorithms to learn patterns and make predictions based on new data.

• Examples include image recognition, natural language processing, and recommendation systems.

The neighbourhood with the biggest median price difference between superhosts and non superhosts is X.

• Calculate the median price for superhosts and non superhosts in each neighbourhood

• Find the neighbourhood with the largest difference in median prices between superhosts and non superhosts

• Example: Neighbourhood X has a median price of $200 for superhosts and $150 for non superhosts, resulting in a $50 difference

Extract highest scores and calculate growth for students across subjects over two years.

• Use a data structure (like a DataFrame) to store student names, subjects, and scores.

• Group data by student and subject to find the maximum score for each subject.

• Example: If Student A has scores [80, 90, 85, 70, 95] in subjects [Math, Science, English, History, Art], the highest score is 95 in Art.

• For growth calculation, compare scores from last year to this year.

• If last year's score is mi...read more

Decorators in Python enhance functions without modifying their structure, promoting code reusability and separation of concerns.

• Function Enhancement: Decorators allow you to add functionality to existing functions, such as logging or access control, without changing their code.

• Syntax: A decorator is applied using the '@decorator_name' syntax above the function definition, making it clear and concise.

• Example: @app.route('/') in Flask is a decorator that maps a function to a UR...read more

Use RAID 5 to store data across all three memory chips with parity bits for fault tolerance.

• Implement RAID 5 to distribute data and parity bits across all three memory chips.

• If one memory chip is corrupted, the data can be reconstructed using the parity bits from the other two chips.

• Example: Store 1GB of data on each chip and use the remaining space for parity bits to ensure fault tolerance.

Joins are used in DBMS to combine rows from two or more tables based on a related column between them.

• Types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

• INNER JOIN returns rows when there is at least one match in both tables.

• LEFT JOIN returns all rows from the left table and the matched rows from the right table.

• RIGHT JOIN returns all rows from the right table and the matched rows from the left table.

• FULL JOIN returns rows when there is a match in one of ...read more

I chose Data Science field because of its potential to solve complex problems and make a positive impact on society.

• Fascination with data and its potential to drive insights

• Desire to solve complex problems and make a positive impact on society

• Opportunity to work with cutting-edge technology and tools

• Ability to work in a variety of industries and domains

• Examples: Predictive maintenance in manufacturing, fraud detection in finance, personalized medicine in healthcare

Find numbers that appear at least three times consecutively in any order.

• Use a window function to track consecutive numbers

• Filter the result to only include numbers that appear at least three times consecutively

• Return the result table in any order

Algorithms and law can be correlated through the use of algorithms in legal processes and decision-making.

• Algorithms can be used in legal research to analyze large amounts of data and identify patterns or trends.

• Predictive algorithms can be used in legal cases to assess the likelihood of success or failure.

• Algorithmic tools can help in legal document review and contract analysis.

• However, there are concerns about bias in algorithms used in law, as they can reflect and perpetua...read more

No, confusion matrix is not used in Linear Regression.

• Confusion matrix is used to evaluate classification models.

• Linear Regression is a regression model, not a classification model.

• Evaluation metrics for Linear Regression include R-squared, Mean Squared Error, etc.

Data Scientist interview questions

• Introduced myself and my background

• Explained the difference between numpy and list

• Described Gradient Boosting and its applications

• Wrote a Python program to count letters in a string

• Explained my capstone project and its significance

• Discussed why I chose data science as a career

Find the longest common prefix string from a list of strings.

• Iterate through the characters of the first string and compare with corresponding characters of other strings

• Stop when a mismatch is found or when reaching the end of any string

• Return the prefix found so far

To maintain data integrity and generalization, use techniques like data cleaning, normalization, and feature engineering.

• Perform data cleaning to remove errors, duplicates, and inconsistencies.

• Normalize data to ensure consistency and comparability.

• Utilize feature engineering to create new features or transform existing ones for better model performance.

Optimizers in Deep Learning Models are algorithms used to minimize the loss function by adjusting the weights of the neural network.

• Optimizers help in updating the weights of the neural network during training to minimize the loss function.

• Popular optimizers include Adam, SGD, RMSprop, and Adagrad.

• Each optimizer has its own way of updating the weights based on gradients and learning rate.

• Choosing the right optimizer can significantly impact the training process and model perf...read more

Encoder Decoder is a neural network architecture used for sequence-to-sequence tasks. Transformer model is a type of neural network architecture that relies entirely on self-attention mechanisms.

• Encoder Decoder is commonly used in machine translation tasks where the input sequence is encoded into a fixed-length vector representation by the encoder and then decoded into the target sequence by the decoder.

• Transformer model consists of an encoder and a decoder, both of which are...read more

Evaluation metrics in ML assess model performance, guiding improvements and ensuring reliability across various scenarios.

• Accuracy: Measures the proportion of correct predictions. Useful for balanced datasets but misleading for imbalanced ones.

• Precision: The ratio of true positives to the sum of true positives and false positives. Important in scenarios like spam detection.

• Recall (Sensitivity): The ratio of true positives to the sum of true positives and false negatives. Crit...read more

Data science offers a new challenge and opportunity to apply analytical skills from my engineering background.

• Data science allows me to utilize my analytical skills in a new and challenging field.

• I can apply my knowledge of statistics and programming to extract insights from data.

• Data science offers opportunities to work on diverse projects and industries.

• My background in electrical engineering provides a strong foundation for understanding complex systems and data analysis.

Machine learning enables computers to learn from data and make predictions or decisions without being explicitly programmed.

• Machine learning can automate and optimize complex processes

• It can help identify patterns and insights in large datasets

• It can improve accuracy and efficiency in decision-making

• Examples include image recognition, natural language processing, and predictive analytics

• It can also be used for anomaly detection and fraud prevention

Using R as a calculator to compute values for a Data Scientist interview question.

• Use R's console to input mathematical expressions and compute values.

• Make sure to follow the order of operations (PEMDAS) when entering expressions.

• Use functions like 'sqrt()' for square roots and 'exp()' for exponentiation.

• Remember to assign variables using the '<-' operator before using them in calculations.

Dropout is a regularization technique to prevent overfitting by randomly setting some neuron outputs to zero during training. Batch Normalization is a technique to normalize the inputs of each layer to improve training speed and stability.

• Dropout randomly sets a fraction of neuron outputs to zero during training to prevent overfitting.

• Batch Normalization normalizes the inputs of each layer to improve training speed and stability.

• Dropout is commonly used in neural networks to ...read more

A happy number is a number which eventually reaches 1 when replaced by the sum of the square of each digit.

• Create a function to determine if a number is happy by repeatedly squaring the digits and summing them until the result is 1 or a cycle is detected.

• Use a set to keep track of seen numbers to detect cycles.

• Example: For number 19, the process would be 1^2 + 9^2 = 82, 8^2 + 2^2 = 68, 6^2 + 8^2 = 100, 1^2 + 0^2 + 0^2 = 1, so 19 is a happy number.

Isolation Forest is an anomaly detection algorithm that works by isolating outliers in a dataset.

• Isolation Forest is an unsupervised machine learning algorithm used for anomaly detection.

• It works by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

• The number of splits required to isolate an outlier is used as a measure of its abnormality.

• Evaluation metrics for Isolation Forest in layman's ter...read more

The ASCII value is a numerical representation of a character. It includes both capital and small alphabets.

• ASCII values range from 65 to 90 for capital letters A to Z.

• ASCII values range from 97 to 122 for small letters a to z.

• For example, the ASCII value of 'A' is 65 and the ASCII value of 'a' is 97.

I bring a unique blend of analytical skills, domain knowledge, and a collaborative mindset to drive impactful data-driven decisions.

• Strong analytical skills: I have experience in statistical analysis and machine learning, demonstrated by a project where I improved model accuracy by 20%.

• Domain expertise: My background in healthcare allows me to understand complex datasets, such as predicting patient outcomes using historical data.

• Collaboration: I thrive in cross-functional tea...read more

Transformer architecture is a type of neural network architecture commonly used in natural language processing tasks.

• Utilizes self-attention mechanism to weigh the importance of different words in a sentence

• Consists of encoder and decoder layers for tasks like machine translation

• Introduced by the paper 'Attention is All You Need' by Vaswani et al.

• Popular implementations include BERT, GPT, and TransformerXL

My CGPA is low because I focused more on gaining practical experience through internships and projects.

• I prioritized gaining practical experience over theoretical knowledge

• I took up internships and projects to gain hands-on experience

• I believe practical experience is more valuable than just academic grades