Data Science Interview Questions for Google

1. What was your role in the project?

• In a project, my role would involve helping with data processing, analysis, and deriving insights from data. This could involve tasks like cleaning and transforming data, building data pipelines, and applying machine learning or statistical models to gain insights. Depending on the project’s focus, I could be involved in data wrangling, data visualization, or even helping with reporting and decision support.

2. Can you tell me about your schedule and how you see the tasks assigned to you?

• As an AI, I don’t have a traditional work schedule. My tasks are typically dependent on the requests or queries presented to me. I handle tasks sequentially based on priority, and I aim to efficiently address each task by understanding the context of the request. My approach to a task is focused on clarity, accuracy, and providing helpful solutions.

3. What is a list?

• A list is a data structure in Python that holds an ordered collection of items. It allows you to store multiple values in a single variable, and the items within a list are indexed, meaning each item can be accessed by its position (index). Lists are mutable, so their content can be changed after they are created.

4. What is a dictionary?

• A dictionary is another data structure in Python, but instead of an ordered list, it stores data as key-value pairs. Each key is unique, and it maps to a corresponding value. Dictionaries are unordered, meaning the items have no specific order. You can quickly access, modify, and delete data in a dictionary using the key.

5. What is the map() function?

• The map() function in Python applies a given function to each item in an iterable (such as a list or tuple) and returns an iterator that yields the results. It is useful when you want to perform an operation on each item of a sequence, such as transforming or processing data.

6. Do you work on big data? How many rows and columns were in that data?

• Yes, I can handle big data concepts. In big data scenarios, datasets can have millions or even billions of rows and a large number of columns. The key challenge with big data is that it cannot fit into memory on a single machine, which is why distributed computing systems like Apache Spark or Hadoop are often used to process and analyze the data across multiple machines in parallel.

7. What is Spark and why do we use Spark?

• Apache Spark is an open-source, distributed computing system designed for big data processing. It is used to process large datasets quickly and efficiently, especially when the data exceeds the capacity of a single machine. Spark is faster than traditional tools like Hadoop because it performs computations in memory, reducing the need for disk I/O. Spark supports a wide range of processing, including batch processing, real-time stream processing, machine learning, and SQL-based queries.

8. Do you use the Pandas library? What is the apply() function in the Pandas library?

• Yes, Pandas is a powerful Python library used for data manipulation and analysis. The apply() function in Pandas allows you to apply a function along the rows or columns of a DataFrame. It is useful when you need to perform a custom transformation or operation on the data. This can be used for data wrangling tasks, such as cleaning or modifying values in a DataFrame.

9. What is join(), union(), and intersect()?

• join() is used to combine two datasets based on a common column, similar to SQL joins. It allows you to merge related data from different sources.
• union() is used to combine two datasets by appending one dataset to another. It effectively stacks the rows of both datasets together, as long as the columns match.
• intersect() returns the common elements (rows) between two datasets. This operation finds the intersection, similar to the SQL INTERSECT operation.

10. Which language do you prefer for coding — only Python or anything else?

• While I am proficient in Python, I am also capable of working with other programming languages such as Java, Scala, R, SQL, and even newer languages like Julia. However, Python is generally preferred for data-related tasks because of its simplicity, readability, and rich ecosystem of libraries for data analysis, machine learning, and scientific computing.

11. What is ETL and ELT?

• ETL stands for Extract, Transform, Load. It is a process commonly used in data warehousing where data is first extracted from a source, transformed into a suitable format, and then loaded into a destination database or data warehouse.
• ELT stands for Extract, Load, Transform. In ELT, the data is first extracted and loaded into the destination system, and then the transformation is done inside the target system. ELT is often used in cloud-based environments where the data warehouse is powerful enough to handle the transformation tasks.

12. Do you have machine learning knowledge?

• Yes, I have a strong understanding of machine learning concepts, including supervised and unsupervised learning, regression, classification, clustering, and neural networks. I can assist with building machine learning models, data preprocessing, feature selection, model evaluation, and deployment. I also have experience with popular machine learning libraries like Scikit-Learn, TensorFlow, PyTorch, and Keras.

13. What is HDFS?

• HDFS stands for Hadoop Distributed File System. It is the primary storage system used by Hadoop to store large datasets across a cluster of machines. HDFS is designed to be highly fault-tolerant by replicating data across different nodes in the cluster. It allows for scalable storage and efficient data access even when working with petabytes of data.

14. What are the different databases you have worked on?

• I can work with a variety of SQL-based databases, such as MySQL, PostgreSQL, SQL Server, and Oracle, as well as NoSQL databases like MongoDB, Cassandra, and Redis. I am also familiar with distributed data storage systems like HDFS (Hadoop Distributed File System), Amazon S3, and cloud data platforms like Google BigQuery and Amazon Redshift.

15. What is data normalization, and why is it important?

• Data normalization refers to the process of scaling numerical data to fall within a specific range, often between 0 and 1. It helps to ensure that no single feature dominates others due to differences in scale, which can be particularly important in machine learning models (e.g., neural networks) and distance-based algorithms (e.g., KNN, SVM). Normalizing data can improve model performance and convergence.

16. What is feature engineering?

• Feature engineering involves creating new features or transforming existing features to improve the performance of machine learning models. This can include handling missing data, encoding categorical variables, creating interaction terms, or performing dimensionality reduction. The goal is to make the data more suitable for model training.

17. What is the difference between supervised and unsupervised learning?

• Supervised learning involves training a model on labeled data, meaning each training example has an associated target label or outcome. Examples include regression and classification.
• Unsupervised learning involves working with data that doesn’t have labeled outcomes. The goal is to find hidden patterns or groupings in the data. Examples include clustering (e.g., K-Means) and dimensionality reduction (e.g., PCA).

18. What is overfitting in machine learning?

• Overfitting occurs when a machine learning model learns the noise or random fluctuations in the training data rather than the underlying trend. This results in a model that performs well on training data but poorly on unseen data. Techniques like cross-validation, regularization, and pruning are used to mitigate overfitting.

19. What is the difference between precision and recall?

• Precision measures how many of the positively predicted instances are actually positive, while recall measures how many of the actual positive instances were correctly identified. They are often used together in evaluating classification models, especially in imbalanced datasets.
• Precision = TP / (TP + FP)
• Recall = TP / (TP + FN)
  where TP is True Positives, FP is False Positives, and FN is False Negatives.

20. What is cross-validation?

• Cross-validation is a technique used to assess the performance of a machine learning model. The data is split into multiple subsets, and the model is trained on some subsets while tested on others. K-Fold Cross-Validation is the most common, where the dataset is divided into “K” parts, and the model is trained “K” times with each part serving as a validation set once.

21. What is the difference between bagging and boosting in machine learning?

• Bagging (Bootstrap Aggregating) involves training multiple models in parallel on different subsets of data and combining their predictions. Random Forest is a popular example of bagging.
• Boosting is a sequential technique where each model is trained to correct the errors of the previous model. Examples include AdaBoost, Gradient Boosting, and XGBoost. Boosting tends to be more prone to overfitting than bagging.

22. What is the purpose of the groupby() function in Pandas?

• The groupby() function in Pandas allows you to split data into groups based on some criteria (e.g., column values) and apply a function to each group. It is useful for aggregating data, such as computing the mean, sum, or count for each group.

23. What is the difference between a list and a tuple in Python?

• A list is mutable, meaning its content can be changed after creation (e.g., adding/removing elements). A tuple is immutable, meaning once it is created, its content cannot be changed. Tuples are often used when data integrity is crucial and for fixed collections.

24. What is gradient descent?

• Gradient descent is an optimization algorithm used to minimize the loss function by iteratively adjusting the model’s parameters. It is commonly used to train machine learning models like linear regression and neural networks. The algorithm calculates the gradient (slope) of the loss function with respect to model parameters and updates them in the direction of the steepest descent.

25. What is PCA (Principal Component Analysis)?

• PCA is a dimensionality reduction technique used to reduce the number of features in a dataset while retaining as much variance as possible. It transforms the data into a new coordinate system where the axes are the directions of maximum variance.

26. What are the advantages of using cloud services like AWS or Google Cloud for big data processing?

• Cloud services provide scalable storage and compute resources, making them ideal for handling large datasets. They offer managed services for big data processing (like AWS EMR, Google BigQuery) that can automatically scale to meet the demands of your workload, reduce infrastructure management overhead, and provide high availability and fault tolerance.

27. What is the difference between NoSQL and SQL databases?

• SQL databases (e.g., MySQL, PostgreSQL) are relational and use structured query language for querying and managing data. They are suitable for structured data and support ACID (Atomicity, Consistency, Isolation, Durability) properties.
• NoSQL databases (e.g., MongoDB, Cassandra) are non-relational and are often used for unstructured or semi-structured data. They are more scalable and flexible but might sacrifice consistency for availability and partition tolerance (CAP theorem).

28. What is a confusion matrix?

• A confusion matrix is a table used to evaluate the performance of a classification model. It compares the predicted labels against the true labels and shows the number of true positives, false positives, true negatives, and false negatives. This helps in calculating various performance metrics like accuracy, precision, recall, and F1-score.

29. What is the difference between a primary key and a foreign key in databases?

• A primary key is a unique identifier for a record in a database table. It ensures that each record is distinct.
• A foreign key is a column in a table that refers to the primary key of another table, establishing a relationship between the two tables.

30. What is the difference between truncate() and delete() in SQL?

• truncate() removes all rows from a table, but it does not log individual row deletions, making it faster than DELETE. It also resets auto-increment values.
• delete() allows you to remove specific rows based on a condition and can be rolled back if used within a transaction.