Data Science Interview Questions for TCS

by Snehali Patil Blog

Data Science Interview Questions for Cognizant

Data Science Interview Questions​

General Data Science Questions

  1. What is Data Science?
    Data Science is the process of extracting meaningful insights from structured and unstructured data using statistical analysis, machine learning, and programming. It helps businesses make data-driven decisions.
  2. What are the key components of a Data Science project?
    A Data Science project includes problem definition, data collection, data cleaning, exploratory data analysis (EDA), feature engineering, model building, evaluation, and deployment.
  3. How does Data Science differ from traditional software development?
    Unlike traditional software development, which follows predefined rules, Data Science involves statistical modeling, data-driven decision-making, and handling uncertainty using machine learning techniques.
  4. What is the role of a Data Scientist in a company?
    A Data Scientist analyzes large datasets to uncover patterns, builds predictive models, and provides insights that drive strategic business decisions.
  5. What is the importance of feature engineering?
    Feature engineering transforms raw data into meaningful features that improve model performance. It includes creating new variables, handling missing values, and encoding categorical data.

Python for Data Science

  1. What is the difference between lists and NumPy arrays?
    Lists in Python are flexible but inefficient for numerical computations, while NumPy arrays provide optimized mathematical operations and consume less memory.
  2. How does Pandas handle missing data?
    Pandas provides methods like dropna() to remove missing values and fillna() to replace them with mean, median, or interpolation techniques.
  3. What is the use of the groupby() function in Pandas?
    The groupby() function aggregates data based on a specified column, making it useful for summarizing and analyzing datasets.
  4. What is a lambda function in Python?
    A lambda function is an anonymous, single-expression function in Python used for short, simple operations like filtering and mapping data.
  5. How does Python manage memory in large datasets?
    Python manages memory using garbage collection, but for large datasets, memory-efficient libraries like NumPy, Pandas, and Dask optimize performance.

Machine Learning Basics

  1. What is overfitting in machine learning?
    Overfitting occurs when a model learns noise instead of patterns, performing well on training data but poorly on new data. Regularization and cross-validation help prevent it.
  2. What is the difference between classification and regression?
    Classification predicts categorical labels (e.g., spam or not spam), while regression predicts continuous values (e.g., house prices).
  3. What is a confusion matrix?
    A confusion matrix is a table that evaluates classification model performance by displaying true positives, true negatives, false positives, and false negatives.
  4. What is cross-validation, and why is it important?
    Cross-validation splits data into multiple training and testing sets to ensure a model generalizes well and prevents overfitting.
  5. What is an imbalanced dataset, and how do you handle it?
    An imbalanced dataset has significantly more samples in one class than another. Techniques like oversampling, undersampling, and SMOTE can balance the data.

Deep Learning & Neural Networks

  1. What is an activation function in neural networks?
    Activation functions introduce non-linearity in neural networks, enabling them to learn complex patterns. Common functions include ReLU, Sigmoid, and Tanh.
  2. What is backpropagation in deep learning?
    Backpropagation is an optimization algorithm that updates weights in a neural network by computing gradients and minimizing loss using techniques like gradient descent.
  3. What is the difference between ANN and CNN?
    ANNs are general neural networks, while CNNs are specialized for image processing, using convolutional layers to detect spatial features.
  4. What is LSTM, and why is it useful?
    Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) that retains long-term dependencies, making it useful for time-series forecasting and NLP tasks.
  5. How does dropout prevent overfitting?
    Dropout randomly disables neurons during training, forcing the network to generalize better by preventing reliance on specific features.

Big Data & Spark

  1. What is Apache Spark, and why is it popular?
    Apache Spark is a fast, in-memory distributed computing framework used for big data processing, outperforming Hadoop MapReduce.
  2. What is an RDD in Spark?
    A Resilient Distributed Dataset (RDD) is an immutable, fault-tolerant collection of objects that can be processed in parallel in Spark.
  3. What is the difference between MapReduce and Spark?
    MapReduce processes data in batches and stores intermediate results, while Spark uses in-memory computation, making it much faster.
  4. What is partitioning in Spark?
    Partitioning splits large datasets into smaller chunks distributed across nodes, optimizing parallel computation.
  5. How does Spark handle real-time streaming data?
    Spark Streaming processes real-time data streams using micro-batches and DStreams to handle continuous input.

Statistics & Probability

  1. What is the central limit theorem?
    The central limit theorem states that the sampling distribution of the mean approaches a normal distribution as the sample size increases.
  2. What is a p-value?
    A p-value determines the probability of obtaining observed results under a null hypothesis. A smaller p-value (<0.05) indicates statistical significance.
  3. What is a confidence interval?
    A confidence interval represents a range of values within which a population parameter is likely to fall, based on sample statistics.
  4. What is a Type I and Type II error?
    Type I error (false positive) rejects a true null hypothesis, while Type II error (false negative) fails to reject a false null hypothesis.
  5. What is a hypothesis test?
    Hypothesis testing evaluates statistical assumptions about a population based on sample data, using tests like t-tests and chi-square tests.

SQL for Data Science

  1. What is a primary key in SQL?
    A primary key uniquely identifies each record in a table and ensures data integrity.
  2. What is the difference between INNER JOIN and LEFT JOIN?
    INNER JOIN returns only matching records from both tables, while LEFT JOIN returns all records from the left table and matching ones from the right.
  3. What is a window function in SQL?
    Window functions perform calculations across a subset of rows related to the current row, useful for ranking and running totals.
  4. What is a CTE (Common Table Expression)?
    A CTE is a temporary named result set within a SQL query that improves readability and reusability.
  5. What is normalization in databases?
    Normalization organizes a database to reduce redundancy and improve data integrity by dividing tables into smaller ones.

Advanced Concepts

  1. What is a recommendation system?
    A recommendation system predicts user preferences based on historical data using collaborative or content-based filtering.
  2. What is A/B testing?
    A/B testing compares two variations of a feature or product to determine which performs better based on user behavior.
  3. What is Explainable AI (XAI)?
    Explainable AI provides transparency in AI model decisions using techniques like SHAP and LIME.
  4. What is the F1-score?
    The F1-score is the harmonic mean of precision and recall, balancing false positives and false negatives in classification problems.
  5. What is a GAN (Generative Adversarial Network)?
    A GAN consists of a generator and a discriminator working against each other to create realistic synthetic data.