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Data science is one of the most exciting and fast-growing fields today. Companies everywhere are looking for skilled data scientists to help them make smart decisions using data. If you are preparing for a data science interview, it’s important to know what types of questions you might face. Employers want to see if you can think clearly, solve problems, and work with data tools like Python, SQL, and machine learning.
This page will help you get ready by sharing common data science interview questions and answers. You’ll find questions about statistics, coding, data analysis, and real-life business problems. These questions will test both your technical skills and how well you can explain your ideas.
Whether you are a beginner or already have some experience, practicing these questions can give you more confidence. Many interviews also include case studies, so we’ve included examples to help you prepare for those too.
Getting ready for a data science job interview takes time, but with the right preparation, you can succeed. Use this page to learn, practice, and improve your chances of landing your dream job in data science.
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gradient_start_position=”0″ gradient_end_position=”100″ gradient_type=”linear” radial_direction=”center center” linear_angle=”180″ background_color=”” background_image=”” background_position=”left top” background_repeat=”no-repeat” background_blend_mode=”none” render_logics=”” filter_type=”regular” filter_hue=”0″ filter_saturation=”100″ filter_brightness=”100″ filter_contrast=”100″ filter_invert=”0″ filter_sepia=”0″ filter_opacity=”100″ filter_blur=”0″ filter_hue_hover=”0″ filter_saturation_hover=”100″ filter_brightness_hover=”100″ filter_contrast_hover=”100″ filter_invert_hover=”0″ filter_sepia_hover=”0″ filter_opacity_hover=”100″ filter_blur_hover=”0″ animation_type=”” animation_direction=”left” animation_speed=”0.3″ animation_offset=”” last=”true” border_position=”all” first=”true”][fusion_accordion type=”accordions” boxed_mode=”no” border_size=”1″ border_color=”” background_color=”” hover_color=”” divider_line=”” title_font_size=”” icon_size=”” icon_color=”” icon_boxed_mode=”” icon_box_color=”#4f4f4f” icon_alignment=”right” toggle_hover_accent_color=”#3ed9df” hide_on_mobile=”small-visibility,medium-visibility,large-visibility” class=”interview_list” id=””][fusion_toggle title=”Question 21: What are the feature selection methods used to select the right variables?” open=”no” class=”” id=””]
Answer:
There are two main methods for feature selection, i.e, filter, and wrapper methods.
Filter Methods
This involves:
- Linear discrimination analysis
- ANOVA
- Chi-Square
The best analogy for selecting features is “bad data in, bad answer out.” When we’re limiting or selecting the features, it’s all about cleaning up the data coming in.
Wrapper Methods
This involves:
Forward Selection: We test one feature at a time and keep adding them until we get a good fit
Backward Selection: We test all the features and start removing them to see what works better
Recursive Feature Elimination: Recursively looks through all the different features and how they pair together
Wrapper methods are very labor-intensive, and high-end computers are needed if a lot of data analysis is performed with the wrapper method.
[/fusion_toggle][fusion_toggle title=”Question 22: How can you avoid overfitting your model?” open=”no” class=”” id=””]
Answer:
Overfitting refers to a model that is only set for a very small amount of data and ignores the bigger picture. There are three main methods to avoid overfitting:
- Keep the model simple—take fewer variables into account, thereby removing some of the noise in the training data.
- Use cross-validation techniques, such as k folds cross-validation.
- Use regularization techniques, such as LASSO, that penalize certain model parameters if they’re likely to cause overfitting.
[/fusion_toggle][fusion_toggle title=”Question 23: What are the differences between supervised and unsupervised learning?” open=”no” class=”” id=””]
Answer:
- Supervised learning uses known and labeled data as input. Whereas, Unsupervised learning uses unlabeled data as input.
- Supervised learning has a feedback mechanism. While, Unsupervised learning has no feedback mechanism.
- The most commonly used supervised learning algorithms are decision trees, logistic regression, and support vector machine. On the other hand, the most commonly used unsupervised learning algorithms are k-means clustering, hierarchical clustering, and apriori algorithm.
[/fusion_toggle][fusion_toggle title=”Question 24: What is variance in Data Science?” open=”no” class=”” id=””]
Answer:
Variance is the value that depicts the individual figures in a set of data which distributes themselves about the mean and describes the difference of each value from the mean value. Data Scientists use variance to understand the distribution of a data set.
[/fusion_toggle][fusion_toggle title=”Question 25: What is pruning in a decision tree algorithm?” open=”no” class=”” id=””]
Answer:
In Data Science and Machine Learning, Pruning is a technique which is related to decision trees. Pruning simplifies the decision tree by reducing the rules. Pruning helps to avoid complexity and improves accuracy. Reduced error Pruning, cost complexity pruning etc. are the different types of Pruning.
[/fusion_toggle][fusion_toggle title=”Question 26: What is entropy in a decision tree algorithm?” open=”no” class=”” id=””]
Answer:
Entropy is the measure of randomness or disorder in the group of observations. It also determines how a decision tree switches to split data. Entropy is also used to check the homogeneity of the given data. If the entropy is zero, then the sample of data is entirely homogeneous, and if the entropy is one, then it indicates that the sample is equally divided.
[/fusion_toggle][fusion_toggle title=”Question 27: What is a k-fold cross-validation?” open=”no” class=”” id=””]
Answer:
The k-fold cross validation is a procedure used to estimate the model’s skill in new data. In k-fold cross validation, every observation from the original dataset may appear in the training and testing set. K-fold cross-validation estimates the accuracy but does not help you to improve the accuracy.
[/fusion_toggle][fusion_toggle title=”Question 28: What is an RNN (recurrent neural network)?” open=”no” class=”” id=””]
Answer:
RNN is an algorithm that uses sequential data. RNN is used in language translation, voice recognition, image capturing etc. There are different types of RNN networks such as one-to-one, one-to-many, many-to-one and many-to-many. RNN is used in Google’s Voice search and Apple’s Siri.
[/fusion_toggle][fusion_toggle title=”Question 29: What is root cause analysis?” open=”no” class=”” id=””]
Answer:
Root cause analysis was initially developed to analyze industrial accidents but is now widely used in other areas. It is a problem-solving technique used for isolating the root causes of faults or problems. A factor is called a root cause if its deduction from the problem-fault-sequence averts the final undesirable event from recurring.
[/fusion_toggle][fusion_toggle title=”Question 30: What are recommender systems?” open=”no” class=”” id=””]
Answer:
Recommender systems are a subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product.
[/fusion_toggle][fusion_toggle title=”Question 31: What does it mean when the p-values are high and low?” open=”no” class=”” id=””]
Answer:
A p-value is the measure of the probability of having results equal to or more than the results achieved under a specific hypothesis assuming that the null hypothesis is correct. This represents the probability that the observed difference occurred randomly by chance.
- Low p-value which means values ≤ 0.05 means that the null hypothesis can be rejected and the data is unlikely with true null.
- High p-value, i.e values ≥ 0.05 indicates the strength in favor of the null hypothesis. It means that the data is like with true null.
- p-value = 0.05 means that the hypothesis can go either way.
[/fusion_toggle][fusion_toggle title=”Question 32: When is resampling done?” open=”no” class=”” id=””]
Answer:
Resampling is a methodology used to sample data for improving accuracy and quantify the uncertainty of population parameters. It is done to ensure the model is good enough by training the model on different patterns of a dataset to ensure variations are handled. It is also done in the cases where models need to be validated using random subsets or when substituting labels on data points while performing tests.
[/fusion_toggle][fusion_toggle title=”Question 33: Explain the terms KPI, lift, model fitting, robustness and DOE?” open=”no” class=”” id=””]
Answer:
KPI: KPI stands for Key Performance Indicator that measures how well the business achieves its objectives.
Lift: This is a performance measure of the target model measured against a random choice model. Lift indicates how good the model is at prediction versus if there was no model.
Model fitting: This indicates how well the model under consideration fits given observations.
Robustness: This represents the system’s capability to handle differences and variances effectively.
DOE: stands for the design of experiments, which represents the task design aiming to describe and explain information variation under hypothesized conditions to reflect variables.
[/fusion_toggle][fusion_toggle title=”Question 34: Define confounding variables?” open=”no” class=”” id=””]
Answer:
Confounding variables are also known as confounders. These variables are a type of extraneous variables that influence both independent and dependent variables causing spurious association and mathematical relationships between those variables that are associated but are not casually related to each other.
[/fusion_toggle][fusion_toggle title=”Question 35: What is a computational graph?” open=”no” class=”” id=””]
Answer:
A computational graph is also known as a “Dataflow Graph”. Everything in the famous deep learning library TensorFlow is based on the computational graph. The computational graph in Tensorflow has a network of nodes where each node operates. The nodes of this graph represent operations and the edges represent tensors.
[/fusion_toggle][fusion_toggle title=”Question 36: What are auto-encoders?” open=”no” class=”” id=””]
Answer:
Auto-encoders are learning networks. They transform inputs into outputs with minimum possible errors. So, basically, this means that the output that we want should be almost equal to or as close as to input as follows.
Multiple layers are added between the input and the output layer and the layers that are in between the input and the output layer are smaller than the input layer. It received unlabelled input. This input is encoded to reconstruct the input later.
[/fusion_toggle][fusion_toggle title=”Question 37: What are Exploding Gradients and Vanishing Gradients?” open=”no” class=”” id=””]
Answer:
Exploding Gradients: Let us say that you are training an RNN. Say, you saw exponentially growing error gradients that accumulate, and as a result of this, very large updates are made to the neural network model weights. These exponentially growing error gradients that update the neural network weights to a great extent are called Exploding Gradients.
Vanishing Gradients: Let us say again, that you are training an RNN. Say, the slope became too small. This problem of the slope becoming too small is called Vanishing Gradient. It causes a major increase in the training time and causes poor performance and extremely low accuracy.
[/fusion_toggle][fusion_toggle title=”Question 38: What is the difference between an error and a residual error?” open=”no” class=”” id=””]
Answer:
An error refers to the difference between the predicted value and the actual value. The most popular means for calculating errors in data science are Mean Absolute Error(MAE), Mean Squared Error(MSE), and Root Mean Squared Error(RMSE). While residual is the difference between a group of values observed and their arithmetical mean. An error is generally unobservable while a residual error can be visualized on a graph. Error represents how observed data differs from the actual population. While a residual represents the way observed data differs from the sample population data.
[/fusion_toggle][fusion_toggle title=”Question 39: What is TF/IDF vectorization?” open=”no” class=”” id=””]
Answer:
TF–IDF is short for term frequency-inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in information retrieval and text mining.
The TF–IDF value increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus, which helps to adjust for the fact that some words appear more frequently in general.
[/fusion_toggle][fusion_toggle title=”Question 40: Define the term Star Schema?” open=”no” class=”” id=””]
Answer:
It is a traditional database schema with a central table. Satellite tables map IDs to physical names or descriptions and can be connected to the central fact table using the ID fields; these tables are known as lookup tables and are principally useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve several layers of summarization to recover information faster.
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