CodeSignal DSF Assessment (Data Science Frameworks Questions)

If you're eyeing a data science role at a big company like BCG X or other top firms, chances are you'll come across the CodeSignal DSF (Data Science Frameworks) assessment. These assessments are becoming more common for testing your skills in real-world scenarios. So, what exactly should you expect? In this blog, we'll break down the types of questions and challenges you'll face during the test. Whether it's coding tasks or problem-solving questions, we’ve got you covered with tips on how to prepare and tackle the assessment with confidence. Let’s dive into what this assessment is all about and how you can crush it!

5 Modules Covered in the CodeSignal Data Science Assessment

Evaluations based on the CodeSignal Data Science Framework are based on 5 key modules that cover a wide range of data science topics:

• Probability & Statistics
• Machine Learning Fundamentals
• Data Collection
• Data Processing
• Model Development and Evaluation Candidates will be expected to demonstrate key data science knowledge and skills by effectively solving questions within the modules.

How Much Time is Given for the CodeSignal DSF Evaluation?

The evaluation time for this framework is 90 minutes to balance the depth and breadth of content and candidate experience. And possible scores can range from 200 to 600.

CodeSignal DSF: Probability and Statistics

This module contains two scenario-based quiz questions with an average solve time of 5-10 minutes. The scenarios can cover:

• Probability and Random Variables
• Bayes Theorem
• Selection Bias
• Linear Regression
• Logistic Regression
• Descriptive Statistics
• Various distributions
• Other common statistical analyses

CodeSignal Data Science Questions: Probability and Statistics

Try these two sample Probability and Statistics Questions based on recent CodeSignal DSF Assessments.

Question 1: Probability of Passing an Exam

A class has 30 students, and each student has a 70% chance of passing an exam. What is the probability that at least 25 students pass?

Key Concepts:

• Use the binomial distribution formula: [ P(X = k) = \binom{n}{k} \cdot p^k \cdot (1-p)^{n-k} ] where ( n ) is the number of trials, ( p ) is the probability of success, and ( k ) is the number of desired successes.
• Compute ( P(X \geq 25) ), which is the sum of probabilities for 25 to 30 successes.

Function Example:

Example:

Output: ~0.15

Question 2: Rolling a Die

You roll a fair six-sided die 10 times. What is the probability of rolling a "6" exactly 3 times?

Key Concepts:

• This is a binomial probability problem where:
  • ( n = 10 ) (number of trials)
  • ( p = \frac{1}{6} ) (probability of success on each trial)
  • ( k = 3 ) (number of successes)
  • Use the binomial probability formula:

[ P(X = k) = \binom{n}{k} \cdot p^k \cdot (1-p)^{n-k} ] where ( \binom{n}{k} ) is the number of combinations.

Function Example:

Example:

Output: ~0.155

Not enough statistics questions? Check out these 20 Statistics Questions and Answers that are asked during the data science interview.

If you’re looking for more prep, specifically on the Probability and Statistics sections, this Amazon #1 best-selling book Ace the Data Science Interview is THE best resource on the market. I may be biased (co-author here!) but trust the hundreds of positive reviews and see how it has helped so many people.

CodeSignal DSF: Machine Learning Fundamentals

This module contains six scenario-based quiz questions with an average solve time of 5-10 minutes. The scenarios can cover:

• L1 vs L2 Regularization
• Reasons for overfitting
• Limitations of Bayes rule
• How to choose k in a KNN algorithm
• GBM vs Random Forest
• Neural Network fundamentals

CodeSignal Data Science Questions: Machine Learning Fundamentals

Try this sample Machine Learning Question based on recent CodeSignal DSF Assessments.

If you’re looking for more Machine Learning practice - try these 70 Machine Learning Interview Questions and Answers.

Question 1: Predicting with Linear Regression

You are given a dataset containing a single feature () and its corresponding target value (). Write a function to predict the target value for a given input using a simple linear regression model.

The formula for linear regression is: [ y = m \cdot x + b ] Where:

• ( m ): Slope of the line.
• ( b ): Intercept of the line.

Task

Write a function that:

1. Takes the slope ( m ), intercept ( b ), and an input value ( x ).
2. Returns the predicted value of ( y ).

Function Signature:

Input:

• m (float): The slope of the regression line.
• b (float): The intercept of the regression line.
• x (float): The input value.

Output:

• A float represents the predicted target value.

CodeSignal DSF: Data Collection

This module contains one coding question focusing on collecting the data from different sources. The question will have several files as input, and candidates must combine the f iles to return the data in a specified format. On average, candidates are expected to write approximately 20 lines of code and solve within 20-30 minutes. The scenarios can cover:

• Data Exploration
• Data manipulation concepts: Filtering, Sorting, Aggregation, Joining data frames, GroupBy mechanism
• Files formats and standard libraries/ tools to work with them

CodeSignal Data Science Questions: Data Collection

Try these two sample Data Collection Questions based on recent CodeSignal DSF Assessments.

Question 1: Filter Even Numbers from a List

You are given a list of integers. Write a function to collect only the even numbers from the list.

Task

Write a function that:

1. Takes a list of integers as input.
2. Returns a new list containing only the even numbers from the original list.

Function Signature:

Input:

• nums (list of int): A list of integers.

Output:

• A list of integers containing only the even numbers from the input.

Question 2: Collect Names Starting with a Specific Letter

You are given a list of names and a target letter. Write a function to collect all names that start with the given letter. The filtering should be case-insensitive.

Task

Write a function that:

1. Takes a list of names and a target letter as input.
2. Returns a new list containing only the names that start with the target letter.

Function Signature

Input:

• names (list of str): A list of names.
• letter (str): A single character representing the target letter.

Output:

• A list of strings containing names that start with the target letter.

CodeSignal DSF: Data Processing

This module contains one coding question focusing on implementing one or more data processing techniques. On average, candidates are expected to write 20-30 lines of code and solve within 15-20 minutes. The scenarios can cover:

• Familiarity with standard Python data science packages (e.g., sklearn, pandas, numpy) •
• Various data processing techniques, including but not limited to: Imputation, Discretization, Categorical Encoding, Variable Transformation, Scaling
• Missing data handling
• Outlier detection
• Data leakage

CodeSignal Data Science Questions: Data Processing

Try this sample Data Processing Question based on recent CodeSignal DSF Assessments.

Question 1: Average Age Calculation

You are given a list of dictionaries where each dictionary represents a person with their name and age. Write a function to calculate the average age of all the people in the list.

Task

Write a function that:

1. Takes a list of dictionaries containing (string) and (integer) as input.
2. Returns the average age as a float, rounded to 2 decimal places.

Function Signature

Input

• people (list of dict): A list where each dictionary has:
• name (str): The person's name.
• age (int): The person's age.

Output

• A float representing the average age of all the people, rounded to 2 decimal places.

CodeSignal DSF: Model Development and Evaluation

This module contains one coding question focusing on the model training and validation process. On average, candidates are expected to write 20-30 lines of code and solve this within 20-30 minutes. The scenarios can cover:

• Various data options for model training, including but not limited to: Training, Validation, and Development data split, Cross-validation, including Leave-one-out-cross-validation (LOOCV) and k-fold Cross Validation
• Familiarity with standard Python data science packages (e.g., sklearn, pandas, numpy)
• Common model evaluation metrics, including but not limited to: Accuracy, F1 Score, Gini Coefficient, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-Squared/Adjusted R-Squared, Hyperparameter tuning

CodeSignal Data Science Questions: Model Development and Evaluation

Try this sample Model Development and Evaluation Question based on recent CodeSignal DSF Assessments.

Question 1: Mean Squared Error (MSE)

You are building a machine learning model and want to evaluate its performance using Mean Squared Error (MSE). Write a function to calculate the MSE between the predicted values and the actual values. The formula for MSE is: [ \text{MSE} = \frac{1}{n} \sum_{i=1}^n (\text{pred}_i - \text{actual}_i)^2 ]

Where:

• ( \text{pred}_i ): Predicted value.
• ( \text{actual}_i ): Actual value.
• ( n ): Number of data points.

Task

Write a function that:

1. Takes two lists as input: one for predicted values and one for actual values.
2. Returns the MSE as a float, rounded to 2 decimal places.

Function Signature

Input

• predicted (list of float): A list of predicted values.
• actual (list of float): A list of actual values.

Output

• A float representing the Mean Squared Error, rounded to 2 decimal places.

CodeSignal Data Science Assessment Resources

If you’re interviewing at FAANG companies tackle these SQL interview questions to get started:

• Meta SQL Interview Questions
• Amazon SQL Interview Questions
• Apple SQL Interview Questions
• Netflix SQL Interview Questions
• Google SQL Interview Questions

You can also practice SQL interview questions by concept or topic:

• If you’re looking for something to challenge, try these Advanced SQL Interview Questions to help you excel in any SQL interview scenario.
• Want a more in-depth walkthrough of Window Functions and sample questions and answers? Try our blog and give yourself a refresher.
• Or if you want some visuals to help you with your SQL Joins review check out these SQL joins infographics!
• If reading blog pages just isn't your style try these 4 SQL games that make learning FUN!!

And if you’re looking for an all-around resource for conquering the Data Science Interview read this Amazon #1 Best selling book: Ace the Data Science Interview.