Fidelity Investments, a renowned financial services company, harnesses the power of data analytics to drive informed decisions, manage risks, and deliver exceptional client experiences. If you’re eyeing a role in their data analytics team, understanding the typical interview questions and crafting insightful answers can significantly boost your chances. Let’s explore some common data analytics interview questions and their strategic responses tailored for Fidelity Investments.
Table of Contents
Technical Questions
Question: Explain residual plots.
Answer: Residual plots are graphical representations used to assess the fit of a regression model by plotting the differences between observed and predicted values (residuals) against the predicted values or another relevant variable. They help identify patterns, suggesting issues like non-linearity, heteroscedasticity, or outliers. Ideally, a well-fitted model’s residual plot shows a random scatter of points around the horizontal axis, indicating that the model’s assumptions are likely met.
Question: What is Linear Regression with Stochastic Gradient Descent?
Answer: Linear Regression with Stochastic Gradient Descent (SGD) is a method for finding the best-fitting line through a set of points by minimizing the cost function, using a technique that updates the model’s parameters iteratively with a subset (or single instance) of the data at each step. This approach, unlike traditional batch gradient descent which uses the entire dataset to update parameters, makes SGD faster and more scalable for lar
Question: Explain Bias-Variance trade-off.
Answer: The Bias-Variance trade-off is a fundamental concept in machine learning that describes the balance between two types of errors a model can make: bias (error from erroneous assumptions in the model) and variance (error from sensitivity to small fluctuations in the training set). High bias can cause underfitting, where the model is too simple to capture the underlying patterns. High variance can cause overfitting, where the model captures noise along with the signal. The trade-off is the tension between these errors; reducing one typically increases the other. The goal is to find a sweet spot where both bias and variance are minimized to improve model performance on unseen data.
Question: Explain different ways to tackle high bias.
Answer: To tackle high bias, which indicates underfitting, you can:
Increase Model Complexity: Use a more complex model to capture the underlying patterns of the data better.
Add More Features: Incorporate additional predictors into the model if available, to provide more information for making predictions.
Reduce Regularization: If regularization is applied, reducing its strength can allow the model more flexibility to fit the data.
Feature Engineering: Improve or add new features through transformations, combinations, or by creating polynomial features to provide more insights to the model.
Increase Training Time: Allow more time for learning algorithms, especially those that iterate over the training set, to converge to a better solution.
Question: Is CLT or Law of Large Numbers applicable in the case of Random Forest?
Answer: Yes, the Law of Large Numbers (LLN) applies to Random Forests, ensuring that with more trees, the ensemble’s average predictions stabilize. Additionally, the Central Limit Theorem (CLT) is relevant as it shows the aggregated predictions tend toward a normal distribution, reducing variance for a more accurate overall prediction.
Question: How would you design NN as logistic regression?
Answer: To design a Neural Network (NN) as logistic regression, you would:
Input Layer:
Set up an input layer with nodes corresponding to the number of features in the dataset.
Output Layer:
Use a single output node for binary prediction (0 or 1).
Activation Function:
Apply the sigmoid function to the output node for probability conversion, akin to logistic regression.
Loss Function & Optimization:
Opt for binary cross-entropy loss fun
Question: Difference between softmax and sigmoid activation functions.
Answer:
Use Cases:
Sigmoid:
- Used for binary classification tasks where the output is between 0 and 1.
Softmax:
- Used for multi-class classification tasks where the output represents probabilities for each class, ensuring the sum of probabilities across all classes equals 1.
Output Range:
Sigmoid:
- Outputs values between 0 and 1, making it suitable for binary classification as it represents the probability of the input belonging to one class.
Softmax:
- Outputs a vector of probabilities, where each value represents the probability of the input belonging to each class. The sum of all probabilities is 1, aiding in multi-class classification.
Activation Pattern:
Sigmoid:
- Produces a single activation, with one node representing one class.
Softmax:
- Distributes activations across multiple classes, ensuring the highest probability class is chosen as the predicted class.
Question: Explain Hyperparameters in NN.
Answer: Hyperparameters in a Neural Network are predefined settings that control its architecture and learning behavior. They include the number of layers and neurons, activation functions, learning rate, batch size, epochs, regularization parameters, optimizer choice, and dropout rate. Adjusting these settings impacts the model’s ability to learn, generalize, and avoid overfitting, making hyperparameter tuning crucial for optimal NN performance.
Question: Difference between GBM and XGBoost.
Answer:
Regularization:
GBM:
- Uses basic regularization techniques such as max depth and min child weight.
XGBoost:
- Offers more advanced regularization techniques such as L1 and L2 regularization.
Handling Missing Values:
GBM:
- Requires imputation of missing values before training.
XGBoost:
- Can handle missing values internally, saving preprocessing time.
Speed and Performance:
GBM:
- Generally slower than XGBoost due to the sequential training of trees.
XGBoost:
- Optimized for speed and performance, with parallel tree construction and handling of missing values.
Question: What is sampling, in statistics?
Answer: Sampling in statistics refers to the process of selecting a subset of individuals, items, or observations from a larger population. The goal of sampling is to gather information about the population while using a smaller, more manageable dataset. This subset, known as the sample, should ideally represent the characteristics and variability of the entire population.
There are various sampling methods, including:
- Random Sampling: Each member of the population has an equal chance of being selected.
- Stratified Sampling: Dividing the population into subgroups (strata) and then selecting samples from each subgroup.
- Cluster Sampling: Dividing the population into clusters and then randomly selecting entire clusters for the sample.
- Systematic Sampling: Selecting every nth item from the population after an initial random start.
Question: Describe an LSTM in one sentence.
Answer: An LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture designed to model and predict sequential data while effectively handling the vanishing gradient problem by using memory cells with gated units.
SQL question
Question: What is the difference between INNER JOIN and LEFT JOIN?
Answer:
INNER JOIN returns rows when there is at least one match in both tables.
LEFT JOIN returns all rows from the left table, and the matched rows from the right table.
Question: How do you remove duplicates from a table?
Answer: Using the DISTINCT keyword in a SELECT statement: SELECT DISTINCT column1, column2 FROM table_name;
Question: Explain the difference between WHERE and HAVING clauses.
Answer: WHERE is used to filter rows before grouping, and HAVING is used to filter groups after grouping has occurred.
Question: What is a subquery? Provide an example.
Answer: A subquery is a query nested within another query.
Example: SELECT * FROM table1 WHERE column1 IN (SELECT column1 FROM table2);
Question: How do you find the second highest salary in a table?
Answer: Using a subquery: SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees);
Question: Explain the difference between UNION and UNION ALL.
Answer: UNION removes duplicate rows, while UNION ALL includes all rows, even duplicates.
Question: What is the purpose of the GROUP BY clause?
Answer: GROUP BY is used to group rows that have the same values into summary rows, typically with aggregate functions like COUNT, SUM, AVG.
Question: How do you update values in a table based on a condition?
Answer: Using the UPDATE statement with a WHERE clause: UPDATE table_name SET column1 = value1 WHERE condition;
Question: Explain the difference between a primary key and a foreign key.
Answer: A primary key uniquely identifies a record in a table, while a foreign key establishes a relationship between two tables.
Basic Statistics Questions
Question: What is the Central Limit Theorem (CLT) and why is it important?
Answer: The Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution. It is crucial because it allows us to make inferences about population parameters based on sample statistics, enabling hypothesis testing and confidence interval estimation.
Question: Explain the concept of p-value in hypothesis testing.
Answer: The p-value is the probability of obtaining results as extreme as the observed results, assuming that the null hypothesis is true. A smaller p-value indicates stronger evidence against the null hypothesis, suggesting that we reject the null in favor of the alternative hypothesis.
Question: What is the difference between Type I and Type II errors?
Answer: Type I error occurs when we reject a true null hypothesis (false positive), while Type II error occurs when we fail to reject a false null hypothesis (false negative).
Question: Describe the difference between correlation and causation.
Answer: Correlation refers to a relationship between two variables where changes in one variable are associated with changes in another variable. Causation, on the other hand, implies that changes in one variable directly cause changes in another variable. Correlation does not imply causation, as there could be other variables influencing the relationship.
Question: How would you explain the concept of variance and standard deviation to a non-technical person?
Answer: Variance measures the average squared deviation from the mean, indicating the spread or dispersion of data points around the mean. Standard deviation is the square root of variance, providing a measure of the average distance of data points from the mean. In simpler terms, they help us understand how much individual data points vary from the average.
Question: What is the purpose of regression analysis?
Answer: Regression analysis is used to understand the relationship between a dependent variable and one or more independent variables. It helps in predicting the value of the dependent variable based on the values of the independent variables, and also identifies the strength and direction of the relationships.
Other Topics to Prepare
- Questions on ML, DL.
- SVM and perceptron
- NLP Questions
- SQL database Questions
General Questions
Question: What do you know about our company?
Question: Walk me through one of your projects?
Question: What was the largest dataset you have ever worked with? And what did you learned?
Question: Questions based on work experience.
Conclusion
By preparing thoughtful answers to these data analytics interview questions tailored for Fidelity Investments, you demonstrate your understanding of financial analytics principles, industry knowledge, and problem-solving skills. Remember to emphasize your ability to translate data insights into actionable strategies that drive business growth and enhance client satisfaction.
As you embark on this exciting journey into the world of data analytics at Fidelity Investments, let your passion for data-driven decision-making and your innovative mindset shine through. Best of luck on your interview and future endeavors in the dynamic field of financial analytics at Fidelity!
