Data Science Coding Interview Questions

Looking at the data science job market trend in the last few years, it is becoming evident that data scientists are more than just data analysts. They also understand how studying datasets could lead to important decisions that can enhance a product or improve a business’s operations. Initially, data science interviews had a limited coding component. In recent years, however, these interviews have portrayed increasing emphasis on computer science fundamentals which involve a fair bit of coding. Most companies that hire professionals for data scientist roles nowadays require programming language credentials like Python or R programming certification. 

This is because data scientists often find themselves having to deal with several tools, techniques, and environments that require fine-tuning of sorts. A coding background comes in handy at this point. This is because, without coding, they would have to rely on external assistance for basic coding tasks. For instance, data scientists use artificial intelligence and machine learning techniques to draw accurate predictions from vast volumes of data. To operate machine learning tools, models, and libraries, the coding background becomes essential for a data scientist. Secondly, as their role often entails working with developers and software engineers, coding skills enable them to work effectively collaboratively with other stakeholders.    

Frequent Data Science coding interview questions

The following curated list of data science coding interview questions will enhance a candidate’s understanding of the Data Science interview, with some explanations to guarantee them a stellar interview.

• Explain string parsing in R language

A collection of combined letters and words is known as a string. Whenever you work with text, you should be able to concatenate words (string them together) and split them apart. In R, you can use the paste() function to concatenate a string and the strsplit() function to split.

• From the given points, how will you calculate the Euclidean distance in Python?

In Python, NumPy and SciPy are the modules to find the Euclidean distance between two points using the distance.euclidean() or math.dist() functions. 

plot1 = [1,3]

plot2 = [2,5]

Euclidean distance refers to the shortest distance between two points in any given dimension. It is arrived at by calculating the square root of the sum of squares of the difference between the two points and is calculated as follows:

euclidean_distance = sqrt( (plot1[0]-plot2[0])**2 + (plot1[1]-plot2[1])**2 )

• What are feature vectors?

In machine learning and pattern recognition, most features of objects are given a numerical representation to make it possible to perform statistical analysis using machine learning algorithms. These numerical representations are known as feature vectors. Therefore, a feature vector refers to the n-dimensional vector where n represents the number of dimensions required to describe the object.  

1. What is a logistic regression model?  

A logistic regression model is an algorithm used in statistical classification to predict the value of a binary dependent data variable by analyzing the relationship between one or more independent variables

• How can you avoid overfitting your model?

Overfitting is a modeling error that occurs when a model is too closely aligned to a limited set of data points such that it picks up both noise and signals to negatively compromise the accuracy with which the model will predict future data. To avoid overfitting, the following techniques can be applied. 

• Data model simplification. Keeping the model simple by taking fewer variables into account, thereby removing some of the noise in the training dataset. 
• Cross-validation. In this case, the training dataset is divided into partitions, the model is then run through each partition and the average error is calculated. For instance the k-fold cross-validation technique. 
• Regularization. Regularization techniques like the LASSO technique that penalize certain model parameters if they are likely to cause overfitting.
• Data augmentation. Using a larger dataset helps reduce overfitting. However, in situations where acquiring a larger dataset is not possible, augmenting the data set i.e. artificially creating new sets of data from the existing dataset. 

• Do gradient descent methods always converge to similar points?

Gradient descent is an optimization algorithm used to find the local minimum of a differentiable function. Gradient descent methods don’t always converge to similar points. This is because in some cases, these methods reach local minima or optima points and so do not get to the global optima point. This is determined by the data in play as well as the starting conditions.

https://www.youtube.com/embed/97sX-CAQWzc

• Briefly explain the decision tree algorithm

This is a supervised form of machine learning algorithm, best suited for solving classification problems due to its tree-like structural setup. It is made up of:

• Decision nodes are the intermediary splittable nodes or points at which a decision must be made. They are divided into root and branch nodes. The root nodes represent the starting point of the tree and the population to be analyzed while the branches represent possible alternative decisions, each with its consequence. 

• Leaf nodes represent the final outputs of the decisions taken whereby all data point labels are homogenous and no further splitting of the nodes is possible

1. What is pruning and what is its significance in a decision tree algorithm? 

Pruning techniques are employed in decision tree algorithms to reduce their size and complexity by eliminating redundant or not critical for the classification of instances. This improves the accuracy of the predictive performance of a decision tree. 

• Name the data sorting algorithms available in the R language

There are five basic ways or algorithms through which data can be sorted in R. They include:

• Bubble sort
• Insertion sort
• Selection sort
• Merge sort
• Quick sort

• Write a sorting algorithm for a numerical dataset in Python 

def sort(mylist):

  n = len(mylist)

  for i in range(n):

    for j in range(0, n-i-1):

      if mylist[j] > mylist[j+1]:

        mylist[j], mylist[j+1] = mylist[j+1], mylist[j]  

    print(mylist)

sort([80, 55, 70])

• What is the correct output for the following sequence of operations on stack data structure? 

push(5)

push(8)

pop

push(2)

push(5)

pop

pop

pop

push(1)

pop

Answer: Based on the fact that stack data structure follows the last-in-first-out principle, then the output becomes 8 5 2 5 1. 

• How would you ensure that the models you trained do not degrade with time? 

Model degradation refers to the decrease of the predictive performance of a model when applied over some time on new datasets in constantly changing environments. For this reason, it is important to continuously evaluate and retrain the model on new datasets to keep it updated.  

Conclusion

Coding interviews, like other technical interviews, require systematic and effective preparation. Hopefully, these questions have given you some insights into both what to expect in a coding interview for DS-related positions and how to prepare for them. Remember that sharpening your coding skills is tremendously rewarding not only for landing your dream job but also for excelling at it.