# Tesco Data Science Interview Questions and Answers

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In the world of data science, interviews serve as gateways to exciting opportunities, where candidates showcase their skills, knowledge, and problem-solving abilities. Tesco, a renowned multinational retailer, is no exception. For aspiring data scientists aiming to join Tesco’s innovative teams, preparation is key. To help you navigate this process, let’s delve into some common data science interview questions and their answers tailored for Tesco’s environment.

### Technical Interview Questions

#### Question: Explain Bias-variance trade off.

Answer: The bias-variance trade-off refers to the balance between errors from underfitting (high bias) and overfitting (high variance) in machine learning models. High bias occurs when a model is too simple, unable to capture the complexity of data. High variance happens when a model is too complex, capturing noise along with true patterns. Achieving a good trade-off involves finding the right level of model complexity to minimize both bias and variance, leading to optimal model performance.

#### Question: What is Regularisation?

Answer: Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the model’s cost function. This penalty discourages overly complex models by imposing a cost for large coefficients or parameters. It helps to generalize the model better on unseen data by controlling the model’s complexity and reducing the risk of overfitting. Common regularization methods include L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net, which combine both L1 and L2 penalties.

#### Question: What is Model comparison.

Answer: Model comparison involves assessing and selecting the best model among choices by comparing their performance metrics like accuracy, precision, recall, or AUC. Techniques include cross-validation on various data subsets to prevent overfitting and ensure generalization to unseen data. The aim is to identify the model that offers the most accurate predictions or classifications for the given problem.

#### Question: Explain Random Forest.

Answer: Random Forest is an ensemble learning method that builds multiple decision trees using random subsets of data and features. It reduces overfitting by averaging predictions from these trees, improving accuracy and robustness. This technique is effective for classification and regression tasks, especially with large datasets and high-dimensional features.

#### Question: Explain Boosting.

Answer: Boosting is an ensemble learning technique where multiple weak learners, often simple decision trees, are combined sequentially to create a strong learner. Each subsequent model corrects errors made by the previous ones, focusing more on instances that were previously misclassified. This iterative process helps to improve the model’s predictive performance, particularly in classification and regression tasks. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost, known for their effectiveness in improving model accuracy.

### SQL Interview Questions

#### Question: What is SQL, and why is it important in data science?

Answer: SQL stands for Structured Query Language, and it’s essential in data science for managing and analyzing structured data stored in relational databases. It allows us to query databases to retrieve, manipulate, and transform data, enabling insights and decision-making from large datasets.

#### Question: What is the difference between SQL and NoSQL databases?

Answer: SQL databases are relational databases that store data in tables with predefined schemas, using SQL for queries. NoSQL databases are non-relational, often schema-less, storing data in various formats like key-value pairs, documents, or graphs. NoSQL databases offer more flexibility and scalability but sacrifice some of the strict consistency and transaction features of SQL databases.

#### Question: Explain the difference between INNER JOIN, LEFT JOIN, and RIGHT JOIN in SQL.

• INNER JOIN returns rows when there is at least one match in both tables.
• LEFT JOIN returns all rows from the left table and matching rows from the right table. If there is no match, NULL values are returned.
• RIGHT JOIN returns all rows from the right table and matching rows from the left table. If there is no match, NULL values are returned.

#### Question: How do you handle missing values in SQL?

Answer: Missing values can be handled using functions like IS NULL or IS NOT NULL to filter for NULL values, or using functions like COALESCE(column_name, default_value) to replace NULL values with a specified default value.

#### Question: What is a subquery in SQL?

Answer: A subquery, also known as a nested query, is a query nested within another query. It allows for more complex queries by performing operations on the result set of another query. Subqueries can be used in SELECT, INSERT, UPDATE, and DELETE statements.

#### Question: What is the difference between GROUP BY and ORDER BY in SQL?

GROUP BY is used to group rows that have the same values into summary rows, typically used with aggregate functions like SUM, COUNT, AVG.

ORDER BY is used to sort the result set of a query in ascending or descending order based on one or more columns.

#### Question: Explain the concept of normalization in databases.

Answer: Normalization is the process of organizing data in a database to reduce redundancy and dependency by dividing large tables into smaller tables and defining relationships between them. It aims to improve data integrity, reduce data duplication, and ensure data consistency.

#### Question: What is an index in SQL, and why is it important?

Answer: An index is a data structure used to improve the speed of data retrieval operations on a database table. It allows for quicker lookups by creating a sorted reference to the data in the table. Indexes are crucial for optimizing query performance, especially for large datasets, as they reduce the need for full-table scans.

### Python Interview Questions

#### Question: What is NumPy and why is it used in data science?

Answer: NumPy is a Python library for numerical computing that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. It is used in data science for tasks such as array manipulation, mathematical operations, and linear algebra computations.

#### Question: Explain the difference between lists and tuples in Python.

Lists are mutable, meaning their elements can be changed after creation, and they are defined using square brackets [ ].

Tuples are immutable, meaning their elements cannot be changed after creation, and they are defined using parentheses ( ). Tuples are generally faster and consume less memory than lists.

#### Question: What is Pandas, and how is it used in data science?

Answer: Pandas is a Python library for data manipulation and analysis, offering data structures like DataFrame and Series. It is used extensively in data science for tasks such as data cleaning, transformation, exploration, and analysis. Pandas provides powerful tools for handling tabular data, making it a fundamental tool in data science workflows.

#### Question: How would you handle missing values in a Pandas DataFrame?

Answer: Missing values can be handled in Pandas using methods such as isnull() to identify missing values, fillna() to fill missing values with a specified value, or dropna() to drop rows or columns with missing values.

#### Question: Explain the use of Matplotlib and Seaborn libraries in data visualization.

Answer: Matplotlib is a Python library for creating static, interactive, and publication-quality visualizations, offering a wide variety of plots such as line plots, scatter plots, histograms, and more. Seaborn is built on top of Matplotlib and provides a high-level interface for creating attractive and informative statistical graphics. These libraries are essential for visualizing data distributions, trends, relationships, and patterns.

#### Question: What is the purpose of the lambda function in Python?

Answer: A lambda function is an anonymous function defined using the lambda keyword, allowing for the creation of small, one-line functions without a formal name. It is often used for simple operations where defining a full function is unnecessary, such as in map(), filter(), or sort() functions.

#### Question: How do you split a dataset into training and testing sets in Python?

Answer: You can split a dataset into training and testing sets using libraries like Scikit-Learn. The train_test_split function from Scikit-Learn is commonly used, where you pass the features and target variable along with the desired test size or train size to split the data.

#### Question: Explain the concept of machine learning pipelines in Python.

Answer: Machine learning pipelines in Python, often implemented using Scikit-Learn, are a sequence of data preprocessing steps and a machine learning model combined into a single workflow. They allow for a more organized and efficient way to apply transformations to data, such as scaling, feature selection, and model fitting, ensuring consistency and reproducibility in the machine learning process.

### ML Interview Questions

#### Question: What is machine learning, and how is it different from traditional programming?

Machine learning is a branch of artificial intelligence where algorithms learn patterns and make predictions or decisions from data without explicit programming.

Unlike traditional programming, where rules are explicitly defined, machine learning algorithms learn from data to improve performance over time and handle complex, unstructured data.

#### Question: Explain the difference between supervised and unsupervised learning.

Supervised learning involves training a model on labeled data, where the model learns to predict the target variable based on input features. It aims to learn a mapping function from input to output.

Unsupervised learning involves training a model on unlabeled data, where the model learns patterns and structures from the data without explicit target variables. It aims to discover hidden patterns or groupings in the data.

#### Question: What is the purpose of cross-validation in machine learning?

Answer: Cross-validation is a technique used to assess the performance and generalization of a machine learning model. It involves splitting the data into multiple subsets, training the model on different subsets, and evaluating its performance on the remaining subset. This helps in estimating how the model will perform on unseen data and avoids overfitting.

#### Question: What is overfitting in machine learning, and how can it be prevented?

Overfitting occurs when a model learns the training data too well, capturing noise or random fluctuations that are not representative of the true underlying patterns.

It can be prevented by techniques such as cross-validation, regularization (e.g., L1, L2), reducing model complexity, using more data, and using ensemble methods like Random Forest or Gradient Boosting.

#### Question: Explain the difference between precision and recall.

Precision is the ratio of true positive predictions to the total number of positive predictions made by the model. It measures the accuracy of positive predictions.

Recall is the ratio of true positive predictions to the total number of actual positives in the data. It measures the ability of the model to correctly identify all positive instances.

#### Question: What is the ROC curve, and what does it represent?

The Receiver Operating Characteristic (ROC) curve is a graphical plot that illustrates the performance of a binary classifier as its discrimination threshold is varied.

It plots the true positive rate (TPR or recall) against the false positive rate (FPR) at various threshold settings. The area under the ROC curve (AUC) provides a single measure of the classifier’s performance, with higher AUC indicating better performance.

#### Question: How does a decision tree work, and what are its advantages and disadvantages?

A decision tree is a supervised learning algorithm that partitions the data into subsets based on features, aiming to create a tree-like model of decisions.

Advantages include interpretability, ease of understanding, and handling both numerical and categorical data. Disadvantages include overfitting with complex trees and being sensitive to small variations in the data.