Preparing for a data science interview requires more than just theory. Recruiters look for practical skills in problem-solving, coding, and interpreting results. 

Two of the most tested tools are Python and SQL, as they form the backbone of analysis and reporting in business. A strong knowledge of these tools enables candidates to answer both conceptual and technical questions with confidence.

In this article, we will focus on Python interview questions for data science in SA, as well as SQL challenges that appear frequently. You will also find examples and strategies to prepare effectively for interviews.

Read more on In-Depth Comparison of Data Analyst Vs Data Scientist.

Problem Patterns for Data Science Interviews

When preparing for interviews, candidates often face coding problems that test their understanding of fundamental patterns. Recruiters use these questions to see how well you can structure logic and write efficient solutions. 

While the problems are often simple, the way you approach them reflects your confidence in using Python. Strong performance here indicates readiness for more complex data science challenges. These patterns usually appear as live coding questions or short exercises.

Here are the common problem patterns tested in interviews:

• String manipulation tasks: Reversing strings, checking palindromes, or counting characters often appear to check your grasp of string functions.
• List/dict comprehension: Candidates may be asked to restructure data using list or dictionary comprehension, which highlights code efficiency and effectiveness.
• Error handling for Python: Recruiters check whether you can write safe code using ‘try-except’ to handle unexpected input.
• Data structure manipulations: Combining lists, using sets, or removing duplicates measure your fluency with Python’s built-in data structures.
• Algorithmic thinking: Sorting and searching problems reveal your ability to balance accuracy and efficiency.

Pandas & NumPy Tasks in Interviews

Pandas and NumPy are cornerstones of data science workflows. Interviewers expect candidates to use these libraries to clean, reshape, and analyse datasets. Many Python pandas questions in SA focus on handling tabular data, while a NumPy interview in SA checks your ability to work with numerical operations. 

These tasks are important because they reflect daily responsibilities in data wrangling and computation. Knowing how to use both libraries with speed and accuracy is a strong advantage in technical interviews.

These are the typical Pandas and NumPy areas tested:

• Data cleaning with Pandas: Dropping nulls, fixing column names, and replacing values are common test cases for preparing a clean dataset.
• Aggregation and grouping: Candidates may need to use ‘group by’ to calculate totals or averages across categories.
• Merging datasets: Performing inner, outer, left, and right joins with Data Frames is a frequent task.
• NumPy calculations: Interviewers test matrix operations, statistics, or random number generation to measure efficiency with arrays.
• Performance focus: Explaining when NumPy arrays outperform Python lists shows awareness of optimisation.

Read more on Top 40 Most Asked Senior Data Scientist Interview Questions.

Common Pitfalls in Data Wrangling

Data wrangling plays a significant role in interviews because real-world data is often messy and incomplete. Many Python interview questions for data science in SA test whether you can avoid mistakes that lead to unreliable insights. 

Interviewers check not just technical fixes but also the reasoning behind them. Handling missing values, mismatched types, or outliers requires careful thinking. Showing awareness of these pitfalls highlights that you are not only coding but also ensuring analytical accuracy. 

This is a key factor for employers who value data-driven decision-making. Here are the main pitfalls tested in data wrangling questions:

• Improper handling of missing data: Candidates must choose the correct imputation or filtering instead of applying one-size-fits-all methods.
• Incorrect data type conversions: Failing to convert text fields into dates or numbers can result in incorrect data.
• Overlooking outliers: Extreme values need proper treatment, or they risk distorting averages and models.
• Inefficient joins or merges: Poorly structured joins slow down processing. Recruiters want to see correct and efficient approaches.
• Column misinterpretation: Hidden spaces or duplicate column names can break queries if not identified early.

Read more on Why is Data Science Important in Different Industries and Various Communities?

SQL Query Building

SQL is critical for extracting and shaping data stored in relational databases. Recruiters use SQL-based interview tasks to measure whether you can create accurate, efficient queries. Many of these appear in Python interview questions for data science in SA because Python and SQL are often combined in workflows. 

Tasks typically test filtering, grouping, and joining data. Accuracy is the first expectation, but clarity of code also matters because results must be reproducible by others. These skills are essential for working with customer data, financial transactions, and reporting pipelines.

Here are the common SQL query building tasks:

• Basic SELECT statements: Simple questions test whether you can fetch specific fields or records with ‘SELECT’.
• Sorting and filtering: Recruiters expect you to use ‘WHERE’ and ‘ORDER BY’ correctly to refine results.
• Aggregate functions: Calculating totals, averages, or counts with ‘GROUP BY’ is a frequent test.
• Complex joins & CTE in SA: You may need to combine data across multiple tables or use CTEs for clarity.
• Subqueries: Nested queries test whether you can manage dependencies logically.

Read more on What Qualifications Do You Need To Be A Data Scientist?

Window Functions & CTEs in SQL

For advanced SQL, many recruiters include window functions and Common Table Expressions (CTEs) in their requirements. These are tested because they help solve complex analytical problems that appear in real projects. 

In a case study for SQL in SA, candidates may need to create time-based summaries, rankings, or recursive outputs. Such problems test not only technical ability but also clarity of thought. If you can effectively use window functions and CTEs, it demonstrates that you’re prepared for high-level analysis tasks. Interviewers usually evaluate readability and correctness here.

These are the common window functions and CTE tasks asked in interviews:

• Ranking data: Using ‘ROW_NUMBER’ or ‘RANK’ to order employees or products is often a common test.
• Moving averages: Candidates may calculate rolling averages for sales or revenue.
• Partitioning data: Using ‘PARTITION BY’ ensures grouped results are handled correctly.
• Recursive CTEs: Common in hierarchical data, such as reporting structures.
• Simplifying queries: Using CTEs to enhance the readability of complex queries is highly valued.

Optimising Slow Queries in Python Interview Questions for Data Science in SA

Slow queries are a frequent challenge in real-world projects, which is why recruiters test optimisation skills. Many SQL optimisation tips in SA appear as part of interview tasks where candidates must explain why a query runs poorly and how to improve it. 

These tasks measure not just technical fixes but also whether you can think critically about performance. Efficient querying ensures databases can support large datasets without unnecessary delays. Knowing optimisation strategies reflects readiness for practical, scalable work. Interviewers value candidates who demonstrate both speed and accuracy.

Here are the common optimisation areas interviewers test:

• Indexing awareness: Using indexes reduces lookup times in large datasets.
• Query rewriting: Simplifying queries by reducing joins or utilising more effective filters enhances performance.
• Avoiding SELECT: Selecting only there quired columns keeps queries efficient.
• Execution plans: Reading execution plans shows awareness of performance bottlenecks.
• Data partitioning: Splitting data into smaller partitions is tested for handling large-scale queries.

Interpreting Query Results

Producing query results is one part of the task, but interpreting them correctly is just as important. Recruiters test whether you can explain what the results mean, validate assumptions, and link outputs back to the business context. 

Many Python interview questions for data science in SA include SQL results followed by interpretation tasks. This step checks whether you can see the bigger picture, not just write code. Being able to identify inconsistencies or highlight meaningful insights is a valued skill in data-driven decision-making.

Here are the areas tested in interpreting results:

• Spotting inconsistencies: Detecting outputs that look suspicious and explaining possible reasons shows awareness.
• Checking assumptions: Recruiters want to see that you confirm filters or groupings before making conclusions.
• Comparing outcomes: Explaining why two similar queries give different outputs highlights critical thinking.
• Business meaning: Candidates should connect query results to practical insights.
• Validation methods: Cross-checking with external data or benchmarks shows thoroughness.

Live Coding Strategies in Python Interview Questions for Data Science in SA

Live coding tests are used to measure how well you solve problems in real time. Many Python interview questions for data science in SA are presented this way, requiring candidates to explain their reasoning while typing code. Recruiters evaluate clarity, coding style, and ability to stay calm under pressure. 

Strong communication skills are essential because interviewers want to understand your thought process. Preparing strategies for live coding tasks helps avoid mistakes and demonstrates that you can handle challenges in a structured manner. This stage often makes a strong impression on employers.

Here are the main live coding strategies recruiters expect:

• Communicating while coding: Explaining your steps helps interviewers understand your thought process and logic.
• Readable code style: Writing code with proper indentation and meaningful names improves clarity.
• Testing early: Running smaller sections first reduces errors later.
• Time management: Splitting an enormous task into more minor problems shows organisation.
• Coding best practices: Applying functions, comments, and reusable structures shows maturity.

Sample Set of Python & SQL Interview Questions

Practising with sample questions is one of the best ways to prepare. Many Python interview questions for data science in SA reflect real-world coding and analysis problems. Practising a mix of Python and SQL problems improves not only technical confidence but also time management. 

Recruiters seek logical, efficient solutions that are supported by clear reasoning. By solving a wide variety of questions before your interview, you’ll be ready to handle unexpected twists in tasks. Strong practice habits are essential for standing out in competitive hiring processes.

Here are sample interview questions for practice:

• Write a Python function to check if two strings are anagrams.
• Using Pandas, calculate the median sales for each region in a dataset.
• Explain why NumPy arrays are more efficient than Python lists.
• How would you handle missing values in a dataset with 20% null entries?
• Write a SQL query to identify the top three customers based on their purchase amounts.
• Use a window function to calculate the cumulative revenue for each month.
• Explain the difference between an inner join and a left join with an example.
• How would you optimise a slow query in a large database table?
• Demonstrate error handling for Python in SA when parsing multiple files.
• Describe one case study for SQL in SA where CTEs simplified query logic.

Conclusion

Preparing for interviews means being prepared for practical problem-solving, not just theoretical knowledge. By reviewing Python interview questions for data science in SA, along with SQL-based challenges, you cover the most tested areas. 

From coding patterns and Pandas tasks to data wrangling, query building, optimisation, and live coding, these topics ensure well-rounded preparation. Practising interpretation and sample questions builds both confidence and clarity. 

Combining Python and SQL knowledge enables candidates to perform strongly in technical interviews and prepares them for real-world tasks.

At Digital Regenesys, the data science certificate course helps learners build technical confidence in Python, SQL, machine learning, and analytics. The course combines theory with hands-on practice, preparing students to work with data in real projects. 

With a focus on coding best practices and applied techniques, learners strengthen their ability to handle interviews as well as workplace challenges.

Visit the Digital Regenesys website to learn more about our courses and explore the opportunities available to enhance your skills.

Python Interview Questions for Data Science in SA – FAQs