If you’re preparing for a data science or analytics role at Cardinal Health, you’re likely gearing up for an interview process that will test both your technical expertise and your ability to apply that knowledge in the healthcare industry. To help you succeed, we’ve compiled a list of typical interview questions and answers that might come up during your discussion with Cardinal Health. While these questions aren’t specific to Cardinal Health, they are representative of the kind of inquiries you might encounter in a healthcare-focused data science role.
Table of Contents
Technical Interview Questions
Question: Explain the data science project lifecycle. How do you approach a new project?
Answer: The data science project lifecycle typically includes several phases: problem definition, data collection, data cleaning, data exploration, feature engineering, predictive modeling, and deployment. In a healthcare setting like Cardinal Health, this might start with identifying a specific problem such as predicting patient medication adherence. From there, you’d gather relevant data, clean and preprocess it, explore it to identify patterns or anomalies, create predictive models, and finally, deploy a solution that integrates with existing healthcare systems for real-time analytics.
Question: What are some common data-cleaning methods you use in your work?
Answer: Data cleaning is crucial in ensuring the accuracy of any analysis. Common methods include handling missing values, removing duplicate records, correcting inconsistencies in data, and transforming data types. In the context of healthcare, special attention might be paid to the accuracy and completeness of patient data, ensuring compliance with health data regulations such as HIPAA.
Question: Can you explain a time when you used predictive modeling to solve a problem?
Answer: In predictive modeling, I once developed a model to forecast patient no-shows in clinic appointments. By using historical data on appointment attendance, patient demographics, and weather conditions, I trained a logistic regression model that could predict with reasonable accuracy which patients were likely to miss appointments. This helped the clinic allocate resources more effectively and implement preemptive reminders to reduce no-show rates.
Question: What experience do you have with SQL? Provide an example of a complex query you’ve written.
Answer: SQL is fundamental in data manipulation and analysis. For instance, I’ve used SQL for complex data aggregations where I needed to join multiple tables, filter specific records, and apply aggregate functions to derive meaningful insights into healthcare provider performance across various dimensions. An example of a complex query could involve using subqueries to first filter out patients based on diagnostic codes, then joining this data with another table containing patient visits, and finally performing a group by operation to calculate the average visit duration per provider.
Question: Describe an experience where you had to explain a complex data science concept to a non-technical stakeholder.
Answer: In one instance, I needed to explain the concept of machine learning model sensitivity and specificity to clinical staff to help them understand the trade-offs between catching true positives and avoiding false alarms in a predictive model for patient risk. I used the analogy of a fire alarm being too sensitive versus one that might miss a real fire, which helped clarify the implications of different threshold settings in our model.
Pandas Interview Questions
Question: What is Pandas in Python? Why is it used?
Answer: Pandas is an open-source data analysis and manipulation library in Python, that provides data structures and operations for manipulating numerical tables and time series. It is widely used because it simplifies tasks related to data exploration, cleaning, transformation, and visualization, making it indispensable for data analysis and data science.
Question: What are the main data structures in Pandas, and how do they differ?
Answer: Pandas have two primary data structures:
- Series: A one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). Each element can be accessed using a label.
- DataFrame: A two-dimensional labeled data structure with columns that can hold different types of data. It is similar to a spreadsheet or SQL table and is the most commonly used pandas object.
Question: How can you handle missing data in Pandas?
Answer: Pandas provides several methods to handle missing data:
- dropna(): Removes rows or columns that contain null values.
- fillna(): Fills the missing values with a specific value or uses a method such as forward filling (ffill) or backward filling (bfill).
- isnull() or notnull(): These methods return a boolean mask indicating the presence or absence of data.
Question: Explain how you can select data in a DataFrame.
Answer: Data selection in DataFrame can be done in several ways:
- By Column Name: df[‘column_name’] or df.column_name for selecting a single column.
- By Row Index: df.iloc[index] for integer-location-based indexing.
- By Condition: df[df[‘column’] > value] to select rows meeting the logical condition.
- By Label: df.loc[row_label, column_label] for label-based indexing.
Question: Describe how you can concatenate and merge DataFrames in Pandas.
Answer:
- Concatenation: Concatenation is combining two or more DataFrames along an axis. pd.concat([df1, df2]) will append df2 to df1 vertically.
- Merge: Merging is combining DataFrames based on values of common columns (similar to SQL joins). pd.merge(df1, df2, on=’key’) will merge df1 and df2 on the column ‘key’.
Question: How do you handle large datasets with Pandas that do not fit in memory?
Answer: For large datasets, techniques include:
- Chunking: Process data in chunks suitable for available memory (using chunksize parameter in read_csv()).
- Categorical Data Types: Convert relevant columns to category type which consumes less memory.
- Dask: Use libraries like Dask which are designed for parallel computing and can handle larger-than-memory computations efficiently.
SQL Activation functions Interview Questions
Question: What are the different types of JOINs in SQL?
Answer: SQL supports several types of JOINs, including INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, and CROSS JOIN. Each type of JOIN combines rows from two or more tables based on a related column between them.
Question: How do you improve the performance of a SQL query?
Answer: To improve SQL query performance, you can use indexes to speed up data retrieval, avoid using SELECT *, write where clause conditions filter out rows early, and optimize joins by ensuring that the joining columns are indexed.
Question: Explain the difference between GROUP BY and ORDER BY.
Answer: GROUP BY aggregates records by the specified columns which provide output in summarized form, whereas ORDER BY sorts the result set of a query by specified column(s) either in ascending or descending order.
Question: What is a subquery, and when would you use one?
Answer: A subquery is a query nested inside another query. Use a subquery when you need to operate multiple steps, such as filtering results based on a calculation or condition tested in another SELECT statement.
Question: What is an activation function and why is it used in neural networks?
Answer: An activation function in neural networks introduces non-linearity into the output of a neuron. This is essential because it helps the network learn complex patterns during training. Common examples include ReLU, Sigmoid, and Tanh.
Question: What is the difference between ReLU and Sigmoid activation functions?
Answer: ReLU (Rectified Linear Unit) is preferred in hidden layers because it solves the vanishing gradient problem and speeds up training, offering better performance for deep networks. Sigmoid, meanwhile, outputs a value between 0 and 1 and is often used for binary classification in the output layer.
Question: Can you explain what a softmax function is and where it might be used?
Answer: The softmax function is an activation function that turns logits (numeric output from the last linear layer of a multi-class classification neural network) into probabilities by taking the exponential of each output and then normalizing these values by dividing by the sum of all the exponentials. It’s typically used in the output layer of a classifier to represent probabilities of classes.
Data Structure Interview Questions
Question: What are the main types of data structures?
Answer: The main types of data structures include arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Each has specific use cases and is chosen based on the requirements of the application, such as data access patterns and memory usage.
Question: Explain the difference between an array and a linked list.
Answer: An array is a collection of elements identified by index or key, stored in contiguous memory locations. This allows for fast access to elements but can lead to wasted memory or the need for resizing. A linked list, on the other hand, consists of nodes that are not stored contiguously; each node points to the next, making it easier to insert or delete elements dynamically but slower to access specific elements.
Question: How does a stack differ from a queue?
Answer: A stack is a data structure that follows the Last In First Out (LIFO) principle, where the last element added is the first one to be removed. A queue follows the First In First Out (FIFO) principle, where the first element added is the first one to be removed. This fundamental difference affects how data is accessed, inserted, or deleted in each structure.
Question: What is a hash table and how does it work?
Answer: A hash table is a data structure that maps keys to values using a hash function to compute an index into an array of buckets or slots, from which the desired value can be found. This allows for efficient data retrieval, insertion, and deletion operations, often approaching O(1) time complexity.
Question: Describe a binary tree and its properties.
Answer: A binary tree is a tree data structure where each node has at most two children, referred to as the left child and the right child. It is used in scenarios like hierarchical data, expression parsing, and dynamic data representation. Special types of binary trees include binary search trees, AVL trees, and red-black trees, which are designed to maintain specific properties to optimize certain operations like search, insertion, and deletion.
Question: What is a graph, and where might you use one?
Answer: A graph is a collection of nodes (or vertices) and edges connecting pairs of nodes. Graphs are used to represent networks, such as social connections, internet links, and road maps. They can be directed or undirected and are tools for solving various computational problems like shortest path, connectivity, and flow optimization.
Question: Explain depth-first search and breadth-first search.
Answer: Depth-first search (DFS) and breadth-first search (BFS) are algorithms used for traversing or searching tree or graph data structures. DFS explores as far as possible along a branch before backtracking, making it useful for tasks that need to explore all paths, like puzzle solving. BFS explores all the neighbors at the present depth before moving on to nodes at the next depth level, which is optimal for finding the shortest path on unweighted graphs.
Conclusion
Preparing for an interview at Cardinal Health involves not only solidifying your technical knowledge but also understanding how those skills apply to the healthcare industry. Demonstrating your ability to navigate the intersection of data science and healthcare will set you apart as a candidate capable of contributing meaningfully to Cardinal Health’s mission of improving the cost-effectiveness of healthcare. Remember, every interview is an opportunity to show how your skills can solve real-world healthcare challenges. Good luck!
