Data science internship interview questions with Answer

What is Data Science?

Data Science is an interdisciplinary field that uses various methods, processes, and systems to extract knowledge and insights from structured and unstructured data. It combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise.

Here are the typical stages of a data science project:

  1. Data Ingestion: Collecting raw structured and unstructured data from all relevant sources using various methods.
  2. Data Storage and Processing: Cleaning, deduplicating, transforming, and combining the data using ETL (extract, transform, load) jobs or other data integration technologies.
  3. Data Analysis: Conducting an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data.
  4. Modeling: Creating predictive models using machine learning and/or deep learning.
  5. Communication: Presenting insights as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand.

Data Science has applications across numerous industries, including healthcare, e-commerce, and more. It’s a fast-growing field with a variety of job opportunities.

Common data science interview questions from students

The Star Schema and Snowflake Schema are two types of multidimensional models used in data warehouses. Here are the key differences between them:

  1. Structure: Star schema has a central fact table connected to dimension tables, forming a star-like structure¹. Snowflake schema is a more normalized version of the star schema, where dimension tables are further normalized into multiple tables³.
  2. Model Direction: Star schema is a top-down model, while snowflake schema is a bottom-up model¹.
  3. Storage Space: Star schema uses more storage space, while snowflake schema uses less due to its normalized structure¹².
  4. Query Performance: Queries execute faster in star schema due to fewer JOINs needed because of fewer foreign keys. In contrast, snowflake schema requires more JOINs due to more foreign keys, leading to slower query performance¹².
  5. Normalization: Star schema uses denormalized dimension tables, while snowflake schema uses both normalized and denormalized tables¹².
  6. Design Complexity: Star schema’s design is simpler, while snowflake schema’s design is more complex¹.
  7. Data Redundancy: Star schema has high data redundancy, while snowflake schema has low data redundancy due to its normalized structure¹⁵.
  8. Understanding: Star schema is simpler and easier to understand, while snowflake schema is more complicated and challenging to understand².

In general, due to the complexity of the snowflake schema and the lower performances, the star schema is the preferred option whenever possible..

Can you give me an example of star schema and snowflake schema?

Sure, let’s start with the Star Schema:

Consider a sales data warehouse. The central fact table, SALES, contains measures of interest such as sales revenue, units sold, and profit margins. Each record in the fact table represents a specific event or transaction, such as a sale or order⁴.

The dimension tables in a star schema contain the descriptive attributes of the measures in the fact table. These attributes are used to slice and dice the data in the fact table, allowing users to analyze the data from different perspectives⁴. For example, the dimension tables might include:

  • Product dimension table: Product ID, Product Name, Product Category, Unit Price⁴.
  • Customer dimension table: Customer ID, Customer Name, Address, City, Zip⁴.
  • Employee dimension table: Emp ID, Emp Name, Title, Department, Region⁴.
  • Time dimension table: Order ID, Order Date, Year, Quarter, Month⁴.

Now, let’s move on to the Snowflake Schema:

In a snowflake schema, the dimension tables are normalized into multiple related tables, creating a hierarchical structure¹. For example, in a sales data warehouse, the product dimension table might be normalized into multiple related tables, such as product category, product subcategory, and product details¹.

Here’s an example of how the dimension tables might be broken down:

  • Employee dimension table: EmployeeID, EmployeeName, DepartmentID, Region, and Territory. The DepartmentID attribute links the Employee table with the Department dimension table¹.
  • Department dimension table: Details about each department, such as the Name and Location of the department¹.
  • Customer dimension table: CustomerID, CustomerName, Address, and CityID. The CityID attributes link the Customer dimension table with the City dimension table¹.
  • City dimension table: Details about each city such as city name, Zipcode, State, and Country¹.

Remember, the main difference between the two schemas is that the dimension tables in the snowflake schema are normalized to reduce redundancy, while the star schema uses denormalized dimension tables for simplicity and query performance¹.

When you type a URL into your browser and hit enter, a series of steps occur to fetch and display the webpage. Here’s a simplified version of the process:

  1. URL Entry: You enter a URL into your web browser¹.
  2. DNS Lookup: The browser looks up the IP address for the domain name via the Domain Name System (DNS). DNS is like a phonebook that maintains and maps the name of the website, i.e., URL, and the particular IP address it links to¹².
  3. TCP Connection: The browser initiates a TCP (Transfer Control Protocol) connection with the server².
  4. HTTP Request: The browser sends an HTTP request to the web server. This could be a GET or POST request².
  5. Server Processing: The server on the host computer handles that request and sends back a response. It assembles a response in some format like JSON, XML, and HTML².
  6. HTTP Response: The server sends out an HTTP response along with the status of the response².
  7. Rendering: The browser begins rendering the HTML content¹⁵.
  8. Done: Finally, the webpage is displayed in your browser².

This process involves the browser, your computer’s operating system, your internet service provider, the server where you host the site, and services running on that server¹. It’s important to understand where things can go wrong, where to look for performance issues, and ensure you’re offering a secure experience for your users¹.

Here are some array manipulation problems that you can practice:

  1. Find the largest three elements in an array¹.
  2. Find the second largest element in an array¹.
  3. Move all zeroes to the end of an array¹.
  4. Rearrange an array such that even positioned are greater than odd¹.
  5. Rearrange an array in maximum minimum form using Two Pointer Technique¹.
  6. Segregate even and odd numbers¹.
  7. Reversal algorithm for array rotation¹.
  8. Search, insert, and delete in an unsorted array¹.
  9. Search, insert, and delete in a sorted array¹.
  10. Sort an array of 0s, 1s, and 2s¹.
  11. Generate all subarrays¹.
  12. Find the missing integer¹.
  13. Count Pairs with the given sum¹.
  14. Find duplicates in an array¹.
  15. Sort an Array using the Quicksort algorithm¹.
  16. Find common elements in three sorted arrays¹.
  17. Find the first repeating element in an array of integers¹.
  18. Find the first non-repeating element in a given array of integers¹.
  19. Subarrays with equal 1s and 0s¹.
  20. Rearrange the array in alternating positive and negative items¹.

These are the questions I got when I interviewed for big companies (Yelp, Facebook, Square, Intel, eBay, etc)

complicated SQL questions that involve Joins and sub-queries
How you would test certain features and create metrics for them
What is A/B Testing?
Basic statistic questions
Why do you want to work at this company as a data scientist?
How did your previous work experiences prepare you for a role as a data scientist?
How do you overcome any professional challenges?
What tools and devices do you plan to use in your role as a data scientist?
What is selection bias, and why do you need to avoid it?
How do you organize big sets of data?
Is having large amounts of data always preferable?
What is root cause analysis?
How do you usually identify outliers within a data set?

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General Data Science Concepts:

  1. What is data science, and how does it differ from traditional statistics?
  2. Explain the concept of overfitting in machine learning.
  3. What is the bias-variance tradeoff, and why is it important in data science?
  4. Can you define A/B testing and its significance in data-driven decision-making?
  5. Differentiate between supervised and unsupervised learning.

Statistics and Probability:

  1. What is the Central Limit Theorem, and why is it crucial in statistics?
  2. Explain the difference between probability and likelihood.
  3. What is p-value, and how is it used in hypothesis testing?
  4. Define Bayesian statistics and its relevance in data science.
  5. Discuss the importance of confidence intervals in statistical analysis.

Programming and Tools:

  1. Which programming languages are commonly used in data science, and why?
  2. How would you handle missing data in a dataset using Python or R?
  3. Explain the purpose of libraries like NumPy and Pandas in data analysis.
  4. What is the role of Jupyter Notebooks in data science workflows?
  5. How would you implement a linear regression model in Python?

Data Cleaning and Preprocessing:

  1. Describe the steps involved in cleaning and preprocessing a dataset.
  2. How do you handle outliers in a dataset, and why is it important?
  3. What is data normalization, and when is it necessary?
  4. Explain the process of feature scaling and its impact on machine learning models.
  5. How do you handle imbalanced datasets?

Machine Learning Algorithms:

  1. Differentiate between classification and regression algorithms.
  2. Explain the working principle of a decision tree algorithm.
  3. What is the purpose of cross-validation in machine learning?
  4. Discuss the differences between bagging and boosting techniques.
  5. Can you explain the concept of ensemble learning?

Neural Networks and Deep Learning:

  1. What is the difference between a perceptron and a neural network?
  2. Explain the term “backpropagation” in the context of neural networks.
  3. Discuss the vanishing gradient problem in deep learning.
  4. What are convolutional neural networks (CNNs) used for?
  5. Explain the concept of transfer learning in deep learning.

SQL and Database Management:

  1. Write a SQL query to retrieve unique values from a column.
  2. Explain the differences between INNER JOIN and OUTER JOIN in SQL.
  3. How do you optimize a database query for better performance?
  4. What is normalization, and why is it important in database design?
  5. Discuss the ACID properties of database transactions.

Big Data Technologies:

  1. What is Hadoop, and how is it used in big data processing?
  2. Explain the role of Apache Spark in big data analytics.
  3. What are the advantages of using NoSQL databases in big data applications?
  4. Discuss the challenges associated with processing real-time data in big data systems.
  5. How do you handle distributed computing in a big data environment?

Data Visualization:

  1. What are the key principles of effective data visualization?
  2. Explain the differences between bar charts and histograms.
  3. How do you choose the right visualization technique for different types of data?
  4. Discuss the importance of color choices in data visualization.
  5. What is the purpose of using box plots in data analysis?

Data Ethics and Privacy:

  1. How do you approach ethical considerations when working with sensitive data?
  2. Explain the concept of “data anonymization” and its importance.
  3. What are the potential biases that may arise in machine learning models, and how can they be mitigated?
  4. Discuss the implications of GDPR on data science practices.
  5. How would you handle a situation where your model produces biased results?

Case Studies and Problem-Solving:

  1. Walk me through a data science project you have previously worked on.
  2. How would you approach solving a real-world business problem using data science?
  3. Discuss a situation where your model did not perform well and how you addressed it.
  4. Explain the steps you would take to validate the results of a machine learning model.
  5. How do you communicate complex technical findings to non-technical stakeholders?

Advanced Analytics:

  1. Explain the concept of time-series analysis and its applications.
  2. How do you implement clustering algorithms, and what are their use cases?
  3. Discuss the differences between L1 and L2 regularization in machine learning.
  4. What is anomaly detection, and how can it be applied in a practical scenario?
  5. How would you perform feature extraction in natural language processing (NLP)?

Behavioral and Situational Questions:

  1. How do you stay updated with the latest trends and advancements in data science?
  2. Describe a challenging problem you encountered during a data science project and how you solved it.
  3. Discuss a situation where you had to work under tight deadlines and how you managed it.
  4. How do you prioritize competing tasks in a fast-paced data science environment?
  5. Can you provide an example of a project where you collaborated effectively with a cross-functional team?

Business Acumen:

  1. How do you align data science initiatives with overall business goals?
  2. Discuss the role of data science in driving business strategy.
  3. Explain the importance of ROI (Return on Investment) in data science projects.
  4. How do you determine the success of a data science project from a business perspective?
  5. What challenges do you anticipate in implementing data science solutions in a corporate setting?

Industry-Specific Questions:

  1. How can data science be applied in the healthcare industry?
  2. Discuss the potential applications of data science in the finance sector.
  3. What role does data science play in optimizing supply chain management?
  4. How can data science contribute to the field of marketing and customer analytics?
  5. Explain the applications of data science in the energy sector.

Coding and Technical Assessments:

  1. Are you comfortable with coding challenges and technical assessments during interviews?
  2. How would you approach solving a coding problem related to data manipulation?
  3. Can you implement a basic machine learning model on a whiteboard or coding platform?
  4. What is your preferred programming language for data science tasks, and why?
  5. Have you worked with any specific data science libraries or frameworks?

Data Science Tools and Platforms:

  1. Discuss your experience with cloud computing platforms for data science.
  2. How do you choose between different machine learning frameworks for a project?
  3. Have you used any version control systems in your data science projects?
  4. What role do data science notebooks play in your workflow, and which ones do you prefer?
  5. Explain the advantages and disadvantages of using open-source tools in data science.

Future Trends in Data Science:

  1. What do you think are the emerging trends in artificial intelligence and machine learning?
  2. How will advancements in natural language processing impact data science applications?
  3. Discuss the potential impact of quantum computing on data science.
  4. What role do you see automated machine learning (AutoML) playing in the future?
  5. How can data science contribute to addressing global challenges, such as climate change?

Soft Skills and Communication:

  1. How do you approach explaining complex technical concepts to a non-technical audience?
  2. Discuss a situation where you had to communicate your findings to executives or stakeholders.
  3. How do you handle disagreements within a team when working on a data science project?
  4. Can you give an example of a time when you had to adapt to unexpected changes in a project?
  5. What strategies do you employ to ensure effective collaboration in a remote work setting?

Personal Development and Learning:

  1. How do you continue to develop your skills and knowledge in the field of data science?
  2. Are there specific online courses or certifications you recommend for aspiring data scientists?
  3. Discuss a book or research paper that has significantly influenced your approach to data science.
  4. What areas of data science do you feel you need further improvement or exploration?
  5. How do you balance staying up-to-date with industry trends while maintaining work-life balance?

These questions cover a broad spectrum of topics, reflecting the multifaceted nature of data science interviews for internships. Aspirants should be prepared to showcase their technical expertise, problem-solving skills, and ability to communicate complex concepts effectively.

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