Python coding interviews can be challenging. You will find tons of resources and blogs related to Python interview questions on the internet. A majority of these explain the basic foundations of the Python programming language and how its structures and libraries work. In order to really crack the interview, you are ought to go beyond the normal cadence of coding testing and prepare in a way that distinguishes you from the rest of the league during the interview rounds. In this article, we have identified the most popular topics within Python coding domains that data science interview panelists are often asking candidates. These Python interview questions for data science would help you ace the interview and get hired for the role of the most skilled and talented coder.
Always Start with Python Data Structures
The interview questions related to Python data structures continue to overwhelm interviewees. A majority of data science projects involving Python coding assignments deal with some of the most complex issues pertaining to multiple, disjointed data sources which need a better understanding of Python data structures.
Interview panels often begin their rounds of the questionnaire by probing into the basic inbuilt data structures. It is extremely important to define and distinguish between the various data structures such as Lists, dictionaries, Tuple, and sets.
There are a few variations of Python interview questions that can bewilder candidates who haven’t spent enough time on main data structures in Python, Operations, indexing and concatenation, and fixing errors in a DataFrame.
Don’t Ignore String Manipulation
Trivia: 9 out of 10 interview candidates are asked “are strings immutable?
Yes or No?”
String Manipulation is another domain that is vast and complex especially when you are asked to solve problems related to indexing, insertions, modifications, matching, and substring extraction. You can create any string by using the syntax str() constructor. For indexing, you will require .index().
In order to master string manipulation for interview rounds, focus on these areas:
- String concatenation
- Case representation
- Delimiters
- Splitting
Advance into MLflow DataFrames
Open source is a great community of data science and computing professionals who are getting together to build different kinds of applications using not just Python but also R and MATLAB. MLflow is an open source used for various python-based processes such as experimentation and reproducibility. Python engineers are increasingly hired to manage and analyze data using DataFrame. A DataFrame is a simple 2D tabulated structure of rows and columns that looks more or less like your Excel spreadsheets.
If you have knowledge of string methods for building DataFrames, you can easily scale the interview questions that are asked from open source domains, such as those involving MLflow projects.
Learn Rapid Integrations
It is impossible to move forward in a Python interview without having complete knowledge of integrations available for machine learning projects. In order to move forward with the project, you should track the developments in the DevOps and Open Source Cloud deployment industry. There are many companies that offer free resources and webinars on rapid integration projects. Commonly used ML projects using the Python platform involve the integrations featuring TensorFlow, PyTorch, Keras, Apache Spark, ONNX, H2O.ai, XGboost, and so on.
Building website-based with Python and APIs
There are many data science projects that leverage the data extracted from website pages and resources. These are gaining prominence in the recent times due to abundant efforts put in by Python developers in the field of API. API is an application programming interface that requires the use of a server for data retrieval and data extraction using code. In a Python environment, you will require specialized tools for API, called Requests Library. These tools are unavailable in the conventional Python library and therefore require access to the third-party source for installation. From making your API requests to building API status codes to the API documentation for Python data science assignments, you can bring a lot of value to any data operations team with your specialized skills in computing.
By answering the interview questions related to API and web scraping, you can actually impress the panel by demonstrating your strong fundamentals in your data management skills using Python.
