Posted on : 19 Mar, 2021, 11:25:38 AM
Created by : Somya Goswami
Data Scientist is one of the most popular and prominent careers globally. There is no surprise that the new era is all about Artificial Intelligence, Machine learning, and Data Science. Due to the great demand and low availability of data scientists, multinational companies are ready to pay the highest perks to data science experts and professionals.
If you are moving towards the path of becoming a Data Scientist, you must be qualified and fully prepared to impress the interviewer or prospective employers with your knowledge. In this guide, Wissenhive includes a list of the Data Science Interview questions that are most frequently asked in job interviews so candidates can frame their answers.
Data Science refers to an interdisciplinary area that utilizes a combination of scientific methods, algorithms, tools, processes, systems, and machine learning techniques to extract knowledge to find the hidden pattern from given raw data that contains structured, semi-structured, and unstructured data.
Data Science uses the theories and techniques from various fields such as Statistics, Information Science, Mathematics, Computer Science, and Domain knowledge to lead the information and data.
Supervised and unsupervised learning are the two different types of machine learning techniques, which allow building from basic to advanced models and solve different kinds of problems.
|Supervised Learning||Unsupervised Learning|
|Works with data that includes both expected output and inputs such as the labeled data||Work with data that includes no mapping from outputs to inputs, such as unlabeled data.|
|Used in creating models that can be employed to classify and predict things||Used in extracting meaningful information from the large volumes of data|
|Used commonly supervised learning algorithms such as decision tree, linear regression, etc||Used commonly Unsupervised learning algorithms such as Apriori algorithm, K-means clustering, etc|
A Decision Tree refers to the decision support tool that uses the tree-like model’s decisions and possible consequences, including resource cost, changing event outcomes, and utility. It is a flow chart to visualize the process of decision-making by planning out various courses of action and their potential outcomes.
Decision trees mostly used in decision analytics, operations research, identifying the strategies to reach goals, and machine learning.
Pruning in Decision Tree algorithm refers to a data compression technique in search algorithms and machine learning that diminishes the size volume of a decision tree by removing certain sections of the tree that are redundant and non-critical to classify instances. It helps in reducing the complexity and improving predictive accuracy by reduction of overfitting.
In the Decision Tree, entropy refers to a measure of randomness and impurity. It controls how the Decision tree algorithms decide to split the data. The entropy of the provided dataset tells us how impure or pure the value of the dataset is. It affects how the Decision tree draws its boundaries. In simple terms, it describes the variance in the dataset.
Linear regression helps in building the understanding of the linear relationship between the independent variables and the dependent. It is a supervised learning algorithm that assists in finding a linear relationship between the independent and the dependent variables.
In linear regression, understanding the behavior of how independent variable changes w.r.t the dependent variable. Single linear regression is an independent variable, but if they are more than one in numbers, it becomes multiple linear regression.
There are various required assumptions for linear regression. Let’s cover all the main assumptions one by one.
The Logistic regression or logit model measures is an appropriate analysis regression to manage when the dependent variable is binary (dichotomous). It refers to predictive analysis, which is used to collect and describe data with the explanation of the relationship between one or more ordinal, nominal interval and one dependent binary variable or ratio-level independent variables.
In the Data Science model, Bias refers to the type of error that occurs due to weak algorithms that are unable to capture new trends that exist and underlying patterns in the data. In other words, Bias occurs when algorithms are unable to understand complicated data and end up building a model based on simple assumptions, which becomes the reason for lower accuracy underfitting. Some algorithms that can lead to high bias are logistic regression, linear regression, etc.
There are six different types of biases that might occur during the sampling process, and those are
Random decision forests or random forests are built up of several decision trees. It is an ensemble learning method for many tasks such as classification regression that builds many decision trees at training time and outputs the class on average prediction (regression) or the mode of the classes (classification) of the individual trees.
If the data split into different packages to make a decision tree in each of the data groups, the random forest brings all the trees together.
There are the five basic steps to build a random forest model are
Dimension reduction and Dimensionality reduction is a process of data transformation from low-dimensional space into high dimensional space. It is a common field that deals with a huge number of observations and variables such as speech recognition, bioinformatics, signal processing, and neuroinformatics.
In the Data Science model, Variance refers to an error type that makes the model end up being too complex and noisy in data. Variance error occurs if algorithms used to build the model have high complexity, even through trends that are discovered and underlying patterns in the data.
It makes model sensitivity that performs poorly on the testing database and provides inaccuracy in testing and finishing in overfitting.
The power analysis is one of the integral components of the experimental designs, which helps in determining the raw sample size for a research study that requires finding out the effect of a given size from a cause with a particular level of assurance. Power analysis allows a specific probability in a constraint sample size.
Univariate analysis is a primary form of statistical data analysis methods. Data that contains only one variable and does not deal with the effect relationship or a cause then Univariate analysis method is used. The key purpose of Univariate analysis is to describe data within data by finding patterns. This technique is done by looking into
There are several ways to conduct univariate analysis, which mostly descriptive in nature are
Bivariate analysis is a better analytical technique than Univariate analysis. The simplest form of statistical (quantitative) analysis involves two variables analysis mostly represented as X and Y to manage the empirical relationship. It is helpful in testing hypotheses of association. This technique is done by looking into
Numerical & Numerical
Categorical & Categorical
Numerical & Categorical
There are multiple ways to conduct Bivariate analysis, which mostly descriptive in nature are
Multivariate analysis refers to a complex statistical analysis technique, which involves research and observation to use when there are more than two statistical outcome variables in the dataset at a time. It addresses the situation where various measurements are involved in every experimental unit, their structure information, and the relation between all the measurements.
There are several ways to conduct multivariate analysis, which mostly descriptive in nature are
Collaborating filtering or social filtering refers to a predictive process that happens behind recommendation engines to analyze actual and real information about the users with a similar feeling to estimate the probability that the targeted audience will enjoy in the form of video, product, or book.
It searches for the correct pattern by collaborating with multiple data sources, viewpoints, and various agents.
There are two specific techniques for the selection. Let’s cover one by one all the methods.
There are four main steps included in maintaining the quality and accuracy of the deployed model, and those steps include
|Factors||Data Science||Big Data|
|Concept||Analyzing data||Handling large data|
|Responsibility||Understand pattern within data and make decisions||Process huge volume of data and generate insight|
Usually, it contains order and customer tables that includes the following columns, and those are
Confusion matrix in the area of machine learning and the problem of statistical classifications that is also known as an error matrix refers to a specific table layout that provides visualization performance of algorithms. It compares the predicted value with the actual targeted value by the machine learning model and gives a holistic representation of the performance of the classification model with finding error catcher.
There are only two labels in binary classification algorithms, and those are True and False. Before measuring the accuracy, understanding a few key terms are important.
To calculate the accurate solution, we will divide the sum of classified observations correctly by the nos. of total observations.
The Naive Bayes Algorithm or naive refers to a probabilistic machine learning algorithm based on the Bayes Theorem, which describes the possibility of an event and classification tasks. Naive provides a strong assumption that is unrealistic for actual data, but it is very effective on complex problems.
A/B testing, also known as bucket testing or split testing which used for conducting random experiments and comparing two different variables. This testing method’s main objective is to discover changes to a web page to increase or enhance the best outcome of the strategy.
Ensemble learning is a processor by which various models, such as experts and classifiers, generate and combine strategically to solve particular computational intelligence problems. It is used to improve the entire model’s performance by reducing the likelihood of poor unwanted selection.
|Factors||Data Science||Artificial Intelligence|
|Scope||Involves various underlying data operations||Limited to the implementation of ML algorithms|
|Type of Data||Structural and Unstructual||Standardized in the form of vectors and embeddings|
Cross-validation is an advanced validation technique that evaluates how statistical analysis outcomes will generalize and work for an independent dataset. This technique is used in the background, where the main purpose is to forecast and estimate the model’s accuracy.
Some of the important steps that are used and involved in analytics projects are
To select and find important variables while working with data sets includes.
TF-IDF refers to a statistical measure that stands for Term Frequency/Inverse Document Frequency that evaluates how relevant a collection of documents is. It can be done by multiplying two metrics: word count appeared in the document and inverse document frequency of the word. TF/IDF is used often in information retrieval and text mining.
The Boltzmann machine is one of the simple learning algorithms that help in discovering those new features that represent complex regularities in the data training. Algorithms will permit you to optimize the weights fully, and the amount qualifies for the given problems.
The p-value refers to the possibility of obtaining test solutions at least as extreme as the observed outcomes under the assumption of a statistical null hypothesis test is correct. It is used as an alternate option to reject points to give the smallest level of importance at which the null hypothesis would be declined.
Root cause analysis is a problem-solving method used for identifying, isolating, and recognizing the root cause of problems or faults. It initially develops analyzing industrial accidents, but it’s is also used in other areas, and those industries are telecommunications, accident analysis, IT operations, industrial process control, healthcare industry, Medicine, etc.,
Deep learning is one of the sub-branch of machine learning. It has a unique aspect that brings efficiency and accuracy to the table while trained with vast amounts of data as the system is advanced and can match the human brain’s cognitive powers.
Deep learning algorithms train advanced machines and devices by learning from example. It includes industries such as eCommerce, health care, advertising, and entertainment that commonly use deep learning.
|Factors||Data Analytics||Data Science|
|Goals||Using existed information to uncover actionable data||Discovering new questions to drive innovation|
Here, we are done with the list of top 50 Data Science interview questions that the interviewer frequently asks. Fulfilling necessity criteria is important, but reviewing interview questions and answers in advance is the best way to calm the candidate’s nerves and boost confidence before going for an interview to give your best.
Let us know if you find difficulty understanding any question or any query that arises that is not mentioned in this article related to Data Science interview questions in the comment box.
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