Data Science Interview Questions for Cognizant

by Snehali Patil Blog

Data Science Interview Questions for Cognizant

Data Science Interview Questions for Cognizant​

General Data Science Questions

  1. What is the goal of Data Science in business applications?
    Data Science aims to extract actionable insights from data, improve decision-making, and optimize processes to enhance business performance and customer experience.
  2. What is the difference between structured and unstructured data?
    Structured data is organized in tables with predefined schemas (e.g., databases), while unstructured data lacks a fixed format (e.g., images, videos, and text).
  3. What is the role of EDA (Exploratory Data Analysis) in Data Science?
    EDA involves summarizing, visualizing, and understanding data distributions to uncover patterns, detect anomalies, and guide feature selection.
  4. What are outliers, and how do you handle them?
    Outliers are extreme values that deviate significantly from the dataset’s distribution. They can be handled using statistical methods, transformation techniques, or removal based on business relevance.
  5. What are the major challenges in real-world Data Science projects?
    Key challenges include data quality issues, scalability, interpretability, deployment complexities, and aligning models with business objectives.

Python for Data Science

  1. Why is Python widely used in Data Science?
    Python offers an extensive ecosystem of libraries (Pandas, NumPy, Scikit-learn), ease of use, and strong community support, making it ideal for data analysis and machine learning.
  2. What are Python decorators, and how are they used?
    Decorators are functions that modify the behavior of other functions without altering their code, often used for logging, authentication, or timing execution.
  3. How does Pandas optimize large datasets?
    Pandas optimizes performance using vectorized operations, categorical data types, and chunk-wise processing for memory efficiency.
  4. What is the difference between NumPy’s array and a Python list?
    NumPy arrays are faster, consume less memory, and support vectorized operations, whereas Python lists are flexible but inefficient for numerical computations.
  5. What is the difference between apply(), map(), and vectorization in Pandas?
    apply() applies functions to rows/columns, map() applies functions to series elements, and vectorization leverages NumPy for efficient batch operations.

Machine Learning Basics

  1. What are the key steps in a machine learning pipeline?
    A machine learning pipeline includes data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and deployment.
  2. What is bias-variance tradeoff?
    High bias leads to underfitting (too simplistic models), while high variance causes overfitting (too complex models). A balanced model generalizes well.
  3. What is the difference between parametric and non-parametric models?
    Parametric models have fixed parameters (e.g., linear regression), while non-parametric models (e.g., decision trees) adapt complexity based on data.
  4. What is the role of cross-validation in machine learning?
    Cross-validation ensures model performance consistency by splitting data into multiple training and validation sets, reducing overfitting risks.
  5. What are some ways to handle categorical variables in machine learning?
    Encoding techniques include one-hot encoding, label encoding, frequency encoding, and target encoding, depending on the dataset and algorithm.

Deep Learning & Neural Networks

  1. What is an artificial neuron, and how does it work?
    An artificial neuron mimics a biological neuron, processing weighted inputs, applying an activation function, and generating an output.
  2. How do you prevent overfitting in neural networks?
    Techniques like dropout, batch normalization, L1/L2 regularization, and early stopping help prevent overfitting in deep learning models.
  3. What is the difference between batch gradient descent and stochastic gradient descent?
    Batch gradient descent updates weights after processing the full dataset, while stochastic gradient descent updates after each sample, improving convergence speed.
  4. Why is ReLU preferred over Sigmoid in deep learning?
    ReLU avoids vanishing gradients by keeping positive values and setting negatives to zero, enabling faster and more stable training.
  5. What is a convolutional neural network (CNN)?
    CNNs are specialized for image processing, using convolutional layers to detect spatial patterns like edges and textures.

Big Data & Spark

  1. What is Apache Spark, and how does it compare to Hadoop?
    Spark is an in-memory distributed computing framework that processes data faster than Hadoop’s batch-oriented MapReduce.
  2. What are the different components of Apache Spark?
    Spark consists of Spark Core, Spark SQL, Spark Streaming, MLlib (machine learning), and GraphX (graph processing).
  3. How does Apache Spark achieve fault tolerance?
    Spark maintains fault tolerance using Resilient Distributed Datasets (RDDs) and lineage tracking to recompute lost data.
  4. What is a DAG (Directed Acyclic Graph) in Spark?
    A DAG represents the sequence of operations in Spark, optimizing execution by restructuring tasks efficiently.
  5. What is the role of partitions in Spark?
    Partitions split large datasets across multiple nodes, enabling parallel processing for improved performance.

Statistics & Probability

  1. What is a probability distribution?
    A probability distribution describes how values in a dataset are distributed, including common types like normal, binomial, and Poisson distributions.
  2. What is the difference between descriptive and inferential statistics?
    Descriptive statistics summarize data, while inferential statistics use samples to make predictions about a population.
  3. What is the importance of standard deviation in statistics?
    Standard deviation measures data spread, helping understand how values deviate from the mean.
  4. What is the law of large numbers?
    As sample size increases, the sample mean approaches the population mean, ensuring stable statistical estimates.
  5. What is hypothesis testing?
    Hypothesis testing evaluates statistical assumptions using tests like t-tests and chi-square tests to determine significance.

SQL for Data Science

  1. What is the difference between WHERE and HAVING in SQL?
    WHERE filters rows before aggregation, while HAVING filters aggregated results after GROUP BY.
  2. What is a self-join in SQL?
    A self-join joins a table with itself, useful for hierarchical data representation.
  3. What is a common table expression (CTE)?
    A CTE is a temporary result set within a SQL query that improves readability and reusability.
  4. What is a correlated subquery?
    A correlated subquery depends on an outer query and executes row-by-row, unlike independent subqueries.
  5. What is the difference between UNION and UNION ALL?
    UNION removes duplicates, while UNION ALL retains all rows, improving performance.

Advanced Concepts

  1. What is reinforcement learning?
    Reinforcement learning trains models using rewards and penalties, commonly applied in robotics and gaming.
  2. What is Explainable AI (XAI)?
    XAI enhances model transparency using interpretability techniques like SHAP and LIME.
  3. What is an Autoencoder?
    An autoencoder is an unsupervised neural network that compresses and reconstructs input data, often used for anomaly detection.
  4. What is feature selection, and why is it important?
    Feature selection removes irrelevant or redundant features, improving model accuracy and efficiency.
  5. What is the difference between bagging and boosting?
    Bagging reduces variance by training models in parallel, while boosting improves weak learners sequentially.