Data science interview questions and answers for 2022

While it may seem basic, this data science interview question draws on your experience. The way you answer this data science interview question can show how well you understand this field. Data science is a field of knowledge that comes from several disciplines. It is composed of several scientific processes, algorithms, machine learning tools and techniques. The data thus gathered is collated in an insightful manner and common patterns are discerned using mathematical and statistical techniques. The data science life cycle looks something like this:

• First, the problem is defined and the data needed for the problem is outlined.
• After that, the necessary data is collected through various sources.
• Then, the raw data collected is cleaned for inconsistencies and missing values.
• After that, the data is explored and a summary of the insights is collected.
• The cleansed data is then run through different algorithms such as text mining, recognition patterns, predictive analytics, etc.
• Finally, reports, charts, graphs, and other visualization techniques are used to present the results to the business stakeholders.

Data science uses various tools and techniques including data analytics to gather meaningful insights and present them to business stakeholders. On the other hand, data analytics is one of the techniques that analyzes raw data to determine trends and patterns. These trends and patterns can help guide businesses in making effective and efficient decisions. Data analytics uses historical and present data to understand current trends. Whereas, data science uses predictive analytics to determine future problems and drive innovations. Answering this data science interview question can distinguish you from the rookies.

Sampling is at the core of data science and hence, this data science interview question gives you the opportunity to display your core knowledge. When the data set is very large in size, it is not feasible to conduct an analysis on the entire data set. In such cases, it is critical to select a sample from the given population and conduct data analytics on the selected dataset. This requires caution as a representative sample that represents the true characteristics of the entire population must be selected. The two main sampling techniques used as per statistical needs are:

• Probability samplings such as cluster sampling, random sampling, and stratified sampling
• Non-probability samplings such as quota sampling, convenience sampling, and snowball sampling

This is an important data science statistics interview question. Let’s outline the differences:

This data science interview question establishes your own decision-making prowess. Below are the steps for building a decision tree:

• For input, take the whole data set
• Calculate the entropy of the class variables and predictor attributes
• Calculate entropy after splitting the attributes
• Calculate information gain of all attribute splits
• Select as root node the attribute with the highest information gain
• Repeat the process for all branches until you finalize the decision node of each branch

For example, if you want to make a decision tree for deciding whether you should buy a certain flat or not, this is how the decision tree may look like:

We can see from the decision tree that the flat will be bought if:

• The cost of the flat is less than INR 5000000
• The premises has walking track, gym, and swimming pool

Underfitting: Underfitting means that the statistical model does not fit the existing data set. Underfitting occurs when less training data is provided. The statistical model in underfitting is extremely weak in identifying the relationship in the data and thus, unable to identify any underlying trends. Underfitting can ruin the accuracy of the machine learning model. It can be avoided if more data is used and the number of features is reduced by using feature selection.

Overfitting: A statistical model is overfitted when a lot of data is used to train it. When too much data is used the model learns from the noise and inaccurate data as well, resulting in the inability of the model to categorize the data accurately. Overfitting occurs when non-parametric and non-linear methods are used. Solutions include using a linear algorithm and using parameters such as maximal depth.

Sometimes simple data science interview questions like the above can catch you off-guard, make sure you are prepared with such questions.

This data science interview question can establish your detail orientation.

Through this data science interview question, the interviewer wants to understand whether you have experience handling critical situations. The two main methods of feature selection are wrapper and filter methods.

Wrapper method:

The wrapper methods include

• Forward selection: One feature is tested at a time and added till a good fit is achieved
• Backward selection: All features are tested and those not fit are removed to find which fits best.
• Recursive feature elimination: Different features are checked and their pairs are tested to see how they work together recursively.

Wrapper methods need high-end computers and a lot of labor if the data sets for analysis are huge.

Filter method:

The filter methods include

• Chi-square
• ANOVA
• Linear discrimination analysis

Filter methods involve cleaning up the data. In order to select the most suitable features, various statistical methods are used by these filter methods.

When the data accuracy is questionable or there is uncertainty about the parameters of the given population, resampling is done. It is a method to improve the accuracy of the sample data and the quality of the model by training it on different datasets to handle variations. It is also used as a validation technique for models using random data subsets or during tests when labels for data points get substituted.

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.