Top 100+ Data Science Interview Questions and Answers

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Data science is the field of study that combines domain expertise, programming skills, and     knowledge of mathematics and statistics to extract meaningful insights from data.

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Following are the major differences between Data Science and Machine Learning:

1. Data Science is a field about processes and systems to extract data from structured and semi-structured data. Whereas, Machine Learning is a field of study that gives computers the capability to learn without being explicitly programmed.
2. Data Science need the entire analytics universe. While, Machine Learning is a combination of Machine and Data Science.
3. Data in Data Science maybe or maybe not evolved from a machine or mechanical process. In contrast, Machine Learning uses various techniques like regression and supervised clustering.
4. Data Science as a broader term not only focuses on algorithms statistics but also takes care of the data processing. Whereas, Machine Learning is only focused on algorithm statistics.

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A normal distribution is a probability distribution where the values are symmetric on either side of the mean of the data. This implies that values closer to the mean are more common than values that are further away from it.

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Bias is the simplifying assumptions made by the model to make the target function easier to approximate.

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The Naive Bayes Algorithm model is based on the Bayes Theorem. It describes the probability of an event. It is based on prior knowledge of conditions which might be related to that specific event.

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AB testing used to conduct random experiments with two variables, A and B. The goal of this testing method is to find out changes to a web page to maximize or increase the outcome of a strategy.

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It is a technique of measuring the linear relationship between the two variables. By linear relationship, we mean that an increase in a variable would lead to increase in the other variable and a decrease in one variable would lead to attenuation in the second variable as well. Based on this linear relationship, we establish a model that predicts the future outcomes based on an increase in one variable.

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Following are the major steps for Data analytics project:

1. Find an Interesting Topic: Many problems can be solved by analyzing data and improving the data but you should choose a topic that motivates and fascinates you.
2. Obtain and Understand Data: There are many online data sources where you can get free data sets to use in your project.
3. Data Preparation: To perform any analytical activity on any data it needs to be in a structured format. This step is known as Data Cleaning or Data Wrangling.
4. Data Modelling: In this step, you will begin building models to test your data. It seems the most interesting stage but remembers before this step you spend sufficient time and techniques in prior steps. You can use different modeling methods to determine which is more suitable for your data.
5. Model Evaluation: Once you have crafted your model you need to evaluate the model thoroughly. In this stage you have to determine if your model is working properly, did you get the desired outcome also if it meets the business requirements.
6. Deployment and Visualization: This is the final and the most crucial step of completing your data analytics project.  After setting a model that performs well you can deploy the model for different applications and in the business market.

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Back propagation is a widely used algorithm for training feedforward neural networks. It computes the gradient of the loss function with respect to the network weights.

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K-means clustering is an important unsupervised learning method. It is the technique of classifying data using a certain set of clusters which is called K clusters. It is deployed for grouping to find out the similarity in the data.

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Python will more suitable for text analytics as it consists of a rich library known as pandas. It allows you to use high-level data analysis tools and data structures, while R doesn’t offer this feature.

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A skewed distribution is a distribution where the values in the dataset are not normalized and the distribution curve is inclined towards one side. On the other hand, uniform distribution is a symmetric distribution where the probability of occurrence of each point is same for a given range of values in the dataset.

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Reinforcement Learning is a learning mechanism about how to map situations to actions. The end result should help you to increase the binary reward signal. In this method, a learner is not told which action to take but instead must discover which action offers a maximum reward.

Cluster sampling is a probability sampling technique where researchers divide the population into multiple groups (clusters) for research. So researchers then select random groups with a simple random or systematic random sampling technique for data collection and analysis.

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Advanced machine learning algorithms in data science utilize statistics to identify and convert data patterns into usable evidence. Data scientists use statistics to collect, evaluate, analyze, and draw conclusions from data, as well as to implement quantitative mathematical models for pertinent variables.

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Decision Tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart-like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.

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The P value means the probability, for a given statistical model that, when the null hypothesis is true, the statistical summary would be equal to or more extreme than the actual observed results. P-Value is a measure of how likely is that the observed data would have occurred by random chance. It conveys, under the premise of the Null hypothesis what is the likelihood of getting the observed data value.

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A confusion matrix is a table that is used to define the performance of a classification algorithm. A confusion matrix visualizes and summarizes the performance of a classification algorithm.

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Machine Learning means computers learning from data using algorithms to perform a task without being explicitly programmed. While, Deep Learning uses a complex structure of algorithms modelled on the human brain. This enables the processing of unstructured data such as documents, images, and text.

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Recall is the ability of a model to find all the relevant cases within a data set. Mathematically, we define recall as the number of true positives divided by the number of true positives plus the number of false negatives. Whereas, Precision is the ability of a classification model to identify only the relevant data points.