Practicing Coding Interview Questions in Python
Sharpen your Python problem-solving for technical interviews.
This practice-focused Python course centers on interview-style problem solving, using hands-on exercises to reinforce core topics such as data structures, string manipulation and regular expressions, iterables, functions and lambda expressions, and scientific-computing tools like NumPy and pandas.
Its emphasis is practical: learners work through timed, applied problems to build reliable coding patterns, leverage generators and list comprehensions, and practise recursion and higher-order functions for interview readiness.
At a Glance
Practicing Coding Interview Questions in Python is a practice-focused course on DataCamp that helps learners prepare for Python coding interviews through hands-on problems and exercises.
It is taught by Kirill Smirnov and covers Python data structures, string manipulation, iterable objects, functions and lambda expressions, and scientific-computing topics using practical, interview-style tasks.
| Level | Beginner to Intermediate |
| Rating | 4.8 out of 5 |
| Duration | 5+ hours |
| Languages | English |
| Certificate | Statement of Accomplishment (certificate of completion) |
| Access | Access for as long as your DataCamp subscription is active |
| Course includes |
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| Price | Subscription-based; included with DataCamp subscription (monthly or annual plans) |
What This Course Teaches
This course frames outcomes as measurable competencies for Python coding interviews, focusing on practical implementation, data manipulation, and algorithmic problem solving.
Learners will implement core data structures and regex-based string solutions, apply list comprehensions and generators, use functions and lambda patterns, manipulate data with NumPy and pandas, and produce basic visualizations to validate solutions.
How the Course Is Structured
Practicing Coding Interview Questions in Python is organized as a compact set of focused chapters that group related interview-style exercises into topic-specific modules; the syllabus lists four main modules covering core Python foundations through scientific computing.
The full course comprises 4 modules and takes about 5+ hours to complete in total.
Curriculum overview
01 Python Data Structures and String Manipulation ▾
Refreshes core Python data structures and string handling, including practical regular-expression tasks for text extraction.
- What are the main data structures in Python?
- List methods
- Operations on sets
- Storing data in a dictionary
- What are common ways to manipulate strings?
- String indexing and concatenation
- Operations on strings
- Fixing string errors in a DataFrame
- How to write regular expressions in Python?
- Write a regular expression
- Find the correct pattern
- Splitting by a pattern
02 Iterable objects and representatives ▾
Covers iterable types, list comprehensions, combining iterables, and creating generators to build concise, memory-efficient pipelines.
- What are iterable objects?
- enumerate()
- Iterators
- Traversing a DataFrame
- What is a list comprehension?
- Basic list comprehensions
- Prime number sequence
- Coprime number sequence
- What is a zip object?
- Combining iterable objects
- Extracting tuples
- Creating a DataFrame
- What is a generator and how to create one?
- Shift a string
- Throw a dice
- Generator comprehensions
03 Functions and lambda expressions ▾
Focuses on defining flexible functions, using lambda expressions, applying map/filter/reduce, and understanding recursion in problem solving.
- How to pass a variable number of arguments to a function?
- Positional arguments of variable size
- Keyword arguments of variable size
- Combining argument types
- What is a lambda expression?
- Define lambda expressions
- Converting functions to lambda expressions
- Using a lambda expression as an argument
- What are the functions map(), filter(), reduce()?
- The map() function
- The filter() function
- The reduce() function
- What is recursion?
- Calculate the number of function calls
- Calculate an average value
- Approximate Pi with recursion
04 Python for scientific computing ▾
Introduces NumPy arrays versus lists, pandas DataFrame techniques, and basic plotting to support numerical and exploratory tasks.
- What is the difference between a NumPy array and a list?
- Incorrect array initialization
- Accessing subarrays
- Operations with NumPy arrays
- How to use the .apply() method on a DataFrame?
- Simple use of .apply()
- Additional arguments
- Functions with additional arguments
- How to use the .groupby() method on a DataFrame?
- Standard DataFrame methods
- BMI of villains
- NaN value imputation
- How to visualize data in Python?
- Explore feature relationships
- Plot a histogram
- Creating boxplots
- Final thoughts
Audience & Requirements
Practicing Coding Interview Questions in Python is aimed at learners who want targeted, hands-on preparation for Python-based technical interviews and role-specific coding assessments.
The course suits aspiring and early-career data scientists, job-seekers refreshing practical Python skills, and practitioners who want focused interview practice rather than a full-length textbook-style curriculum.
- Aspiring data scientists preparing for Python coding interviews.
- Early-career analysts or engineers refreshing core Python techniques.
- Job-seekers practicing interview-style, time-boxed coding problems.
- Practitioners who want focused, problem-oriented exercises rather than theory-first instruction.
- Prior completion of or familiarity with introductory Python (e.g., Python Toolbox).
- Working knowledge of regular expressions (or completion of a Regex in Python course).
- Basic pandas experience (e.g., Data Manipulation with pandas) for the data-focused exercises.
Final Verdict
Practicing Coding Interview Questions in Python is a practical, focused resource for anyone whose immediate goal is to sharpen Python problem-solving for interviews; it’s a practical, efficient pick for targeted interview prep rather than a comprehensive, theory-first curriculum.
Because the course sits on a major subscription platform and issues a completion credential, it represents good value for learners already using that ecosystem and for those who prefer hands-on practice over long-form coursework; if you need concentrated, interview-style exercises and already subscribe, this course is worth taking — if you need a deep, textbook-style foundations course, supplement it with a more expansive program.
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