Joining a renowned financial institution like UBS in the field of data science and analytics requires a solid grasp of technical skills and a deep understanding of the financial industry. In this blog post, we’ll delve into some common interview questions along with concise answers tailored for your preparation journey at UBS.
Table of Contents
Technical Interview Questions
Question: Explain how Python differs from other programming languages
Answer:
- Python is known for its simplicity and readability, making it easy to learn and use.
- It emphasizes code readability with its clean and concise syntax, resembling pseudo-code.
- Python’s extensive libraries and frameworks for data analysis, machine learning, and web development set it apart as a versatile language.
- Compared to languages like Java or C++, Python requires fewer lines of code for tasks, fostering rapid development and prototyping.
Question: Explain different types of join.
Answer:
- Inner Join: Retrieves rows where there is a match in both tables based on the join condition, excluding unmatched rows.
- Left Join: Retrieves all rows from the left table and matched rows from the right table, filling unmatched rows with NULL values.
- Right Join: Retrieves all rows from the right table and matched rows from the left table, filling unmatched rows with NULL values.
- Full Join: Retrieves all rows when there is a match in either table, filling unmatched rows with NULL values.
- Cross Join: Generates the Cartesian product of both tables, combining every row of one table with every row of the other table.
Question: Who is a business analyst?
Answer: A Business Analyst is responsible for analyzing business processes, systems, and data to provide insights and recommendations for improving efficiency and achieving business goals. They gather requirements, create reports, and collaborate with stakeholders to ensure projects align with organizational objectives. Business Analysts play a key role in driving strategic decision-making and facilitating successful project implementation within a company.
Question: What are the types of generators?
Answer:
- Function Generators: Defined using def with yield statements, creating a sequence of values within a function.
- Generator Expressions: Similar to list comprehensions, using parentheses to generate values on the fly.
- Generator Functions: Defined with def and yield, allowing values to be yielded one at a time, conserving memory and enabling lazy evaluation.
Question: What is the Bias variance trade-off?
Answer: The variance tradeoff in machine learning is the balance between model complexity and generalization error.
- Bias refers to the error from a model’s oversimplification, leading to underfitting.
- Variance is the error from a model’s sensitivity to fluctuations in the training data, leading to overfitting.
- Finding the optimal balance minimizes both bias and variance, resulting in a model that generalizes well to unseen data.
Question: Difference between logistic regression and linear regression?
Answer:
- Linear Regression: Used for predicting continuous outcomes, such as house prices or temperature.
- Outcome: Continuous, numerical values.
- Model Output: Straight line equation.
- Evaluation: Mean Squared Error (MSE).
- Logistic Regression: Used for binary classification tasks, predicting probabilities of class membership.
- Outcome: Binary or categorical values.
- Model Output: S-shaped logistic curve, representing probabilities.
- Evaluation: Log Loss or Accuracy for classification.
Question: Explain a Gaussian classifier.
Answer: A Gaussian Classifier, or Gaussian Naive Bayes, is a probabilistic classification algorithm assuming features follow a Gaussian distribution.
- It calculates probabilities of a data point belonging to each class using Gaussian probability density.
- Each class has its mean and variance for features, and the highest probability class is assigned to the data point.
- Ideal for continuous data, it works best when features are normally distributed within classes.
Question: Difference between Statistical and Machine learning?
Answer:
Statistical Modeling:
- Focuses on inference, understanding relationships, and making predictions based on probability theory.
- Often used for hypothesis testing, estimating parameters, and analyzing the effect of variables on an outcome.
- Emphasizes model interpretability and understanding of underlying data distributions.
Machine Learning:
- Focuses on building predictive models by learning patterns and structures from data.
- Uses algorithms to make predictions and decisions without explicit programming.
- Prioritizes model accuracy and performance on unseen data, often with complex, black-box models.
Question: How does CNN work?
Answer: Convolutional Layers extract features from images using filters, detecting patterns like edges and textures. Pooling Layers reduce spatial dimensions while preserving important information through methods like MaxPooling. Activation Functions like ReLU introduce non-linearity, and Fully Connected Layers learn complex patterns for classification. The Output Layer provides predictions, assigning probabilities to classes for tasks like image classification.
Question: What’s stemming and lemmatization?
Answer:
Stemming: A text preprocessing technique that reduces words to their base or root form by removing suffixes.
It aims to normalize words to their common stem, even if the stem is not a valid word.
Example: “running” becomes “run”, “jumps” becomes “jump”.
Lemmatization: Similar to stemming, but produces valid words by considering the context and meaning of words.
It reduces words to their dictionary form, known as the lemma.
Example: “running” becomes “run”, “better” becomes “good”.
Question: What is Python?
Answer: Python is a high-level, interpreted programming language known for its simplicity and readability.
It supports multiple programming paradigms like procedural, object-oriented, and functional programming.
Python is widely used in web development, data analysis, artificial intelligence, and scientific computing.
Question: What are the differences between lists and tuples in Python?
Answer: Lists are mutable, meaning their elements can be modified after creation.
Tuples are immutable, once created, their elements cannot be changed.
Lists use square brackets [], while tuples use parentheses ().
Question: What is SQL?
Answer: SQL (Structured Query Language) is a standard language for managing relational databases.
It is used for querying, updating, and managing data in databases like MySQL, PostgreSQL, and SQLite.
SQL allows users to retrieve specific information from databases using commands like SELECT, INSERT, UPDATE, and DELETE.
Question: What is the difference between INNER JOIN and LEFT JOIN in SQL?
Answer:
- INNER JOIN returns only the rows where there is a match in both tables based on the specified condition.
- LEFT JOIN returns all rows from the left table and the matched rows from the right table, with NULL values for unmatched rows.
- INNER JOIN is used to get the intersection of two tables, while LEFT JOIN retrieves all rows from the left table and matches rows from the right table.
Question: What is Machine Learning?
Answer:
- Machine Learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed.
- It focuses on developing algorithms that allow computers to learn patterns and make predictions or decisions based on data.
- ML algorithms are used in various applications such as image and speech recognition, recommendation systems, and predictive analytics.
Question: Explain the difference between supervised and unsupervised learning.
Answer:
- Supervised Learning involves training a model on labeled data, where the algorithm learns from input-output pairs.
- Unsupervised Learning involves finding patterns and structures in unlabeled data without explicit guidance.
- Supervised learning is used for tasks like classification and regression, while unsupervised learning is used for clustering and dimensionality reduction.
Question: Explain the difference between append() and extend() methods in Python lists.
Answer:
append() adds a single element to the end of a list.
extend() takes an iterable and adds its elements to the end of the list.
For example, list1.append(5) adds the element 5 to list1, while list1.extend([6, 7]) adds elements 6 and 7 to list1.
Question: What are lambda functions in Python?
Answer: Lambda functions are small, anonymous functions defined using the lambda keyword.
They can have any number of arguments but can only have one expression.
Lambda functions are often used for simple operations where defining a regular function would be unnecessary.
Question: Explain the difference between GROUP BY and ORDER BY in SQL.
Answer:
GROUP BY is used to group rows that have the same values into summary rows.
ORDER BY is used to sort the result set in ascending or descending order.
GROUP BY is typically used with aggregate functions like COUNT(), SUM(), etc., while ORDER BY is used to sort the result set based on specific columns.
Question: What is a subquery in SQL?
Answer: A subquery is a query nested within another SQL query.
It can be used to retrieve data from one or more tables based on a condition.
Subqueries can be used in SELECT, INSERT, UPDATE, and DELETE statements.
Question: What is the difference between classification and regression in Machine Learning?
Answer:
Classification: Predicts a discrete label or category, such as “spam” or “not spam”.
Regression: Predicts a continuous value, such as the price of a house or the temperature.
Classification algorithms include Logistic Regression, Decision Trees, and Support Vector Machines, while regression algorithms include Linear Regression, Random Forest Regression, etc.
Question: Explain the concept of regularization in Machine Learning.
Answer: Regularization is a technique used to prevent overfitting in machine learning models.
It adds a penalty term to the cost function to discourage the model from becoming too complex.
Two common types of regularization are L1 regularization (Lasso) and L2 regularization (Ridge), which help in controlling the complexity of the model.
Technical Interview Topics
- SQL questions
- Python
- Machine Learning
- Some Bayesian-related stat question
- Explain a few regression algorithms
- Resume-based questions on skills
- Hypothetical scenario on an ML application.
General Behavioral Questions
Que: Describe recent projects in detail
Que: Name one difficult situation you faced.
Que: What databases have you worked on?
Que: What other departments do you know in UBS
Que: What do I expect from this role?
Que: How did you select the best model during your recent project?
Conclusion
Preparing for a data science and analytics interview at UBS requires a blend of technical prowess, industry knowledge, and a keen understanding of UBS’s values. By familiarizing yourself with these interview questions and crafting thoughtful responses, you’re equipping yourself for success in the interview process. Remember, each question is an opportunity to showcase your expertise and passion for leveraging data to drive innovation and excellence at UBS. Best of luck on your journey!
