Getting ready for a data science interview? It can feel overwhelming, but don’t worry, we’ve got you covered! This page will walk you through common interview questions, helping you understand key concepts in programming, statistics, machine learning, and data analysis.
Employers look for candidates who can work with data, solve problems, and explain their findings clearly. You might face coding challenges, case studies, or theoretical questions. The goal is to show that you can think logically and apply your skills to real-world situations. Besides technical knowledge, communication is just as important. Companies want people who can explain complex ideas in simple, understandable language.
By using this guide, you’ll feel more confident tackling tricky questions and showing off your strengths. Whether you’re new to data science or have experience, preparation is key to landing the job you want.
Answer: When there is an unequal distribution of data across categories, the data is said to be imbalanced. Imbalanced data produces inaccurate results and model performance errors. Additionally, when training a model using an imbalanced dataset, the model pays more attention to the highly populated classes and poorly identifies the less populated classes.
Answer: Raw data can be processed more than once. This is often done to clean or transform the data.
Answer: Enumeration is a process of assigning a numerical value to each member of a set or group. This can be used to count things or to identify members of a group.
Answer: The Hamming distance and the Levenshtein distance are two methods for comparing the distance between two binary strings. The number of bits that differ between two strings is defined as the Hamming distance. The number of edit operations (insert, delete, or replace) required to transform one string into another is represented by the Levenshtein distance.
Answer: R2 metrics is a statistical measure that represents the proportion of the variance in a data set that is explained by a linear regression model.
Answer: Descriptive statistics are numerical methods used to summarize and describe a given data set. They are used to quantify the data in order to better understand its characteristics.
Answer: Data augmentation is the process of artificially increasing the size of your training dataset by adding new, modified, or synthetic data samples. This can be done by adding more samples of the existing data, or by synthesizing new samples from the existing data.
Answer: A chi-square is a statistical test used to determine whether there is a significant difference between two groups/variables.
Answer: An ordered dictionary, also known as an OrderedDict, is a subclass of the built-in Python dictionary class that maintains the order of elements in which they were added. In a regular dictionary, the order of elements is determined by the hash values of their keys, which can change over time as the dictionary grows and evolves. An ordered dictionary, on the other hand, uses a doubly linked list to remember the order of elements, so that the order of elements is preserved regardless of how the dictionary changes.
Answer: Return is used to exit a function and return a value to the caller. When a return statement is encountered, the function terminates immediately, and the value of the expression following the return statement is returned to the caller. Yield, on the other hand, is used to define a generator function. A generator function is a special kind of function that produces a sequence of values one at a time, instead of returning a single value. When a yield statement is encountered, the generator function produces a value and suspends its execution, saving its state for later.
Answer: The terms of interpolation and extrapolation are extremely important in any statistical analysis. Extrapolation is the determination or estimation using a known set of values or facts by extending it and taking it to an area or region that is unknown. It is the technique of inferring something using data that is available. Interpolation on the other hand is the method of determining a certain value which falls between a certain set of values or the sequence of values. This is especially useful when you have data at the two extremities of a certain region but you don’t have enough data points at the specific point. This is when you deploy interpolation to determine the value that you need.
Answer: The power analysis is a vital part of the experimental design. It is involved with the process of determining the sample size needed for detecting an effect of a given size from a cause with a certain degree of assurance. It lets you deploy specific probability in a sample size constraint. The various techniques of statistical power analysis and sample size estimation are widely deployed for making statistical judgment that are accurate and evaluate the size needed for experimental effects in practice. Power analysis lets you understand the sample size estimate so that they are neither high nor low. A low sample size there will be no authentication to provide reliable answers and if it is large there will be wastage of resources.
Answer: No, they do not because in some cases it reaches a local minima or a local optima point. You will not reach the global optima point. This is governed by the data and the starting conditions.
Answer: It is a theorem that describes the result of performing the same experiment a large number of times. This theorem forms the basis of frequency-style thinking. It says that the sample mean, the sample variance and the sample standard deviation converge to what they are trying to estimate.
Answer: You want to update an algorithm when:
- You want the model to evolve as data streams through infrastructure.
- The underlying data source is changing.
- There is a case of non-stationarity.
Answer: This is one of the important data analyst interview questions. When two separate keys hash to a common value, a hash table collision occurs. This means that two different data cannot be stored in the same slot. Hash collisions can be avoided by:
- Separate chaining – In this method, a data structure is used to store multiple items hashing to a common slot.
- Open addressing – This method seeks out empty slots and stores the item in the first empty slot available.
Answer: Multi-source problems are a group of computational data composed of dynamic, unstructured, and overlapping data that is hard to go through or obtain patterns from. To tackle multi-source problems, you need to:
- Identify similar data records and combine them into one record that will contain all the useful attributes, minus the redundancy.
- Facilitate schema integration through schema restructuring.
Answer: The term “Boosting” refers to a set of algorithms where it is primarily used to enhance weak learners to perform better and make them strong learners. Using this concept, the algorithms are enhanced in such a way that the results are better compared to the initial stages of the algorithm. Boosting is a method where the weak algorithms are tweaked and enhanced in sequential order. As the process is sequential, the predecessor algorithm is always stronger. They are three different types of boosting:
- AdaBoost- Adaptive boosting
- Gradient boosting
- XG boost
Answer: The statisticians George Box and David Cox developed a procedure to identify an appropriate exponent (Lambda = l) to use to transform data into a “normal shape.” The Lambda value indicates the power to which all data should be raised.
Answer: Association analysis is the task of uncovering relationships among data. It is used to understand how the data items are associated with each other.