{"id":25588546,"date":"2024-07-18T11:57:54","date_gmt":"2024-07-18T06:27:54","guid":{"rendered":"https:\/\/entri.app\/blog\/?p=25588546"},"modified":"2024-07-18T11:59:54","modified_gmt":"2024-07-18T06:29:54","slug":"infosys-data-science-interview-questions","status":"publish","type":"post","link":"https:\/\/entri.app\/blog\/infosys-data-science-interview-questions\/","title":{"rendered":"Infosys Data Science Interview Questions"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69e63182bdb98\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69e63182bdb98\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/entri.app\/blog\/infosys-data-science-interview-questions\/#Infosys_Data_Science_Interview\" >Infosys Data Science Interview\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/entri.app\/blog\/infosys-data-science-interview-questions\/#Infosys_Data_Science_Interview_Preparation_Tips\" >Infosys Data Science Interview Preparation Tips:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/entri.app\/blog\/infosys-data-science-interview-questions\/#Why_Join_Infosys_as_a_Data_Scientist\" >Why Join Infosys as a Data Scientist?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/entri.app\/blog\/infosys-data-science-interview-questions\/#Top_infosys_Data_Science_Interview_Questions_and_Answers\" >Top infosys Data Science Interview Questions and Answers<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/entri.app\/blog\/infosys-data-science-interview-questions\/#Infosys_Data_Science_Interview_Questions_Conclusion\" >Infosys Data Science Interview Questions: Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<p>Like any other global technology company, the interview process at Infosys also consists of many rounds to assess the technical and behavioral skills of the applicant. It usually consists of 5 rounds: Online assessment round, technical interview round, behavioral interview round, Managerial interview round, and the HR round. In this article we will concentrate on the technical round and provide you with the Infosys Data Science Interview Questions.<\/p>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Enhance your data science skills with us! Join our free demo today!<\/a><\/strong><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Infosys_Data_Science_Interview\"><\/span><strong>Infosys Data Science Interview\u00a0<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The data science interview process at Infosys typically involves five key rounds: Online Assessment, Technical Interview, Behavioral Interview, Managerial Interview, and HR Interview. Here is a detailed explanation of each round:<\/p>\n<h3><strong>Online Assessment Round<\/strong><\/h3>\n<p>The online assessment round tests your basic skills and aptitude. It includes questions on logical reasoning and quantitative ability. There are also programming questions in languages like Python or R. Domain-specific questions test knowledge of statistics and probability. This round ensures candidates have a solid foundational understanding.<\/p>\n<h3><strong>Technical Interview Round<\/strong><\/h3>\n<p>The technical interview assesses your problem-solving skills and technical expertise. You&#8217;ll face coding problems and need to optimize code. There are in-depth questions on machine learning algorithms. Case studies involve real-world data science problems. Discussing past projects is also a key component.<\/p>\n<h3><strong>Behavioral Interview Round<\/strong><\/h3>\n<p>The behavioral interview evaluates your soft skills and cultural fit. Expect questions on teamwork and conflict resolution. Scenario-based questions gauge your decision-making skills. Experience-based questions explore how you&#8217;ve handled challenges. This round focuses on your interpersonal skills.<\/p>\n<h3><strong>Managerial Interview Round<\/strong><\/h3>\n<p>The managerial interview assesses your fit for the team and organization. Questions focus on project management and leadership skills. You&#8217;ll discuss aligning projects with business objectives. Domain-specific knowledge may be tested. This round evaluates strategic thinking and leadership potential.<\/p>\n<h3><strong>HR Round<\/strong><\/h3>\n<p>The HR round covers cultural fit and compensation details. Discuss your alignment with Infosys&#8217;s values. You&#8217;ll talk about salary expectations and benefits. Work conditions and potential relocation are also covered. Final background checks are done before making an offer.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Infosys_Data_Science_Interview_Preparation_Tips\"><\/span><strong>Infosys Data Science Interview Preparation Tips:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h4><strong>Online Assessment<\/strong><\/h4>\n<ul>\n<li><strong>Practice Aptitude Tests<\/strong>: Improve logical and quantitative skills.<\/li>\n<li><strong>Review Programming Basics<\/strong>: Focus on Python or R.<\/li>\n<li><strong>Study Data Science Concepts<\/strong>: Cover statistics and probability.<\/li>\n<li><strong>Take Mock Tests<\/strong>: Simulate real test conditions.<\/li>\n<\/ul>\n<h4><strong>Technical Interview<\/strong><\/h4>\n<ul>\n<li><strong>Brush Up on Core Topics<\/strong>: Focus on data science fundamentals.<\/li>\n<li><strong>Practice Coding Problems<\/strong>: Use platforms like LeetCode.<\/li>\n<li><strong>Prepare Case Studies<\/strong>: Understand real-world applications.<\/li>\n<li><strong>Review Past Projects<\/strong>: Be ready to discuss details.<\/li>\n<\/ul>\n<h4><strong>Behavioral Interview<\/strong><\/h4>\n<ul>\n<li><strong>Reflect on Experiences<\/strong>: Think about past challenges.<\/li>\n<li><strong>Practice Common Questions<\/strong>: Use STAR method.<\/li>\n<li><strong>Prepare Scenarios<\/strong>: Develop decision-making examples.<\/li>\n<li><strong>Focus on Soft Skills<\/strong>: Highlight teamwork and communication.<\/li>\n<\/ul>\n<h4><strong>Managerial Interview<\/strong><\/h4>\n<ul>\n<li><strong>Understand Role Expectations<\/strong>: Know what the team needs.<\/li>\n<li><strong>Think Strategically<\/strong>: Align your skills with business goals.<\/li>\n<li><strong>Show Leadership Examples<\/strong>: Discuss how you&#8217;ve led projects.<\/li>\n<li><strong>Review Domain Knowledge<\/strong>: Be ready for specific questions.<\/li>\n<\/ul>\n<h4><strong>HR Round<\/strong><\/h4>\n<ul>\n<li><strong>Research Infosys Culture<\/strong>: Understand their values.<\/li>\n<li><strong>Clarify Compensation Expectations<\/strong>: Know your worth.<\/li>\n<li><strong>Prepare Questions<\/strong>: Ask about the role and company.<\/li>\n<li><strong>Ensure Background Details<\/strong>: Be honest and clear.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Why_Join_Infosys_as_a_Data_Scientist\"><\/span><span data-sheets-root=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Introduction\\nWhy Join infosys as a Data Scientist\\n infosys Data Science Interview Preparation Tips\\nTop infosys Data Science Interview Questions and answers&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:769,&quot;3&quot;:{&quot;1&quot;:0},&quot;11&quot;:4,&quot;12&quot;:0}\"><strong>Why Join Infosys as a Data Scientist?<\/strong><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h4><strong>Career Growth Opportunities<\/strong><\/h4>\n<ul>\n<li><strong>Diverse Projects<\/strong>: Work on various industry-specific projects.<\/li>\n<li><strong>Global Exposure<\/strong>: Collaborate with international teams.<\/li>\n<li><strong>Skill Development<\/strong>: Access continuous learning programs.<\/li>\n<\/ul>\n<h4><strong>Innovation and Technology<\/strong><\/h4>\n<ul>\n<li><strong>Latest Tools<\/strong>: Use the newest data science tools.<\/li>\n<li><strong>Research Projects<\/strong>: Engage in R&amp;D projects.<\/li>\n<li><strong>AI and ML<\/strong>: Work on advanced AI and machine learning projects.<\/li>\n<\/ul>\n<h4><strong>Strong Corporate Culture<\/strong><\/h4>\n<ul>\n<li><strong>Inclusive Environment<\/strong>: Join a diverse workplace.<\/li>\n<li><strong>Ethical Standards<\/strong>: Be part of a company with strong values.<\/li>\n<li><strong>Employee Well-being<\/strong>: Access health and wellness programs.<\/li>\n<\/ul>\n<h4><strong>Collaborative Work Environment<\/strong><\/h4>\n<ul>\n<li><strong>Team Collaboration<\/strong>: Work with talented professionals.<\/li>\n<li><strong>Mentorship<\/strong>: Get guidance from experienced mentors.<\/li>\n<li><strong>Cross-Functional Teams<\/strong>: Collaborate across different functions.<\/li>\n<\/ul>\n<h4><strong>Impactful Work<\/strong><\/h4>\n<ul>\n<li><strong>Business Solutions<\/strong>: Solve real-world business problems.<\/li>\n<li><strong>Social Responsibility<\/strong>: Participate in social initiatives.<\/li>\n<li><strong>Sustainable Practices<\/strong>: Work for a company committed to sustainability.<\/li>\n<\/ul>\n<h4><strong>Competitive Compensation and Benefits<\/strong><\/h4>\n<ul>\n<li><strong>Attractive Salary<\/strong>: Receive a competitive salary.<\/li>\n<li><strong>Comprehensive Benefits<\/strong>: Access health insurance and retirement plans.<\/li>\n<li><strong>Work-Life Balance<\/strong>: Enjoy flexible working hours and remote work options.<\/li>\n<\/ul>\n<h4><strong>Learning and Development<\/strong><\/h4>\n<ul>\n<li><strong>Training Programs<\/strong>: Participate in continuous learning.<\/li>\n<li><strong>Certifications<\/strong>: Earn industry-recognized certifications.<\/li>\n<li><strong>Knowledge Sharing<\/strong>: Engage in workshops and sessions.<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Enhance your data science skills with us! Join our free demo today!<\/a><\/strong><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Top_infosys_Data_Science_Interview_Questions_and_Answers\"><\/span><strong>Top infosys Data Science Interview Questions and Answers<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><strong>What is Python and R?<\/strong><\/h3>\n<ul>\n<li><strong>Python<\/strong>: A programming language known for its simplicity and versatility. It has many libraries for data science, such as pandas, NumPy, and scikit-learn.<\/li>\n<li><strong>R<\/strong>: A programming language focused on statistics and data visualization. It has powerful packages like ggplot2 and dplyr for data analysis.<\/li>\n<\/ul>\n<h3><strong>What is L1 and L2 Regularization?<\/strong><\/h3>\n<ul>\n<li><strong>L1 Regularization (Lasso)<\/strong>: Adds a penalty equal to the absolute value of the coefficients. It can shrink some coefficients to zero, which helps in feature selection.<\/li>\n<li><strong>L2 Regularization (Ridge)<\/strong>: Adds a penalty equal to the square of the coefficients. It shrinks coefficients but doesn\u2019t usually set them to zero.<\/li>\n<\/ul>\n<div class=\"flex flex-grow flex-col max-w-full\">\n<div class=\"min-h-[20px] text-message flex w-full flex-col items-end gap-2 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"1899b93b-edf4-42b3-96ab-da8cf258e229\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[3px]\">\n<div class=\"markdown prose w-full break-words dark:prose-invert light\">\n<h3><strong>What is Multicollinearity?<\/strong><\/h3>\n<ul>\n<li>Multicollinearity happens when independent variables in a regression model are highly correlated. This makes it hard to determine the effect of each variable on the outcome, leading to unreliable results. It can be detected using correlation matrices or variance inflation factor (VIF), and fixed by removing or combining correlated variables.<\/li>\n<\/ul>\n<h3><strong>What is Data Science?<\/strong><\/h3>\n<ul>\n<li>Data Science involves extracting insights and knowledge from data using various techniques. It combines statistics, programming, and domain expertise to analyze and interpret complex data.<\/li>\n<\/ul>\n<h3><strong>Differentiate between Data Analytics and Data Science<\/strong><\/h3>\n<ul>\n<li><strong>Data Analytics<\/strong>: Focuses on analyzing existing data to find trends and insights.<\/li>\n<li><strong>Data Science<\/strong>: Encompasses data analytics but also includes creating algorithms, building models, and making predictions using data.<\/li>\n<\/ul>\n<h3><strong>What are the differences between supervised and unsupervised learning?<\/strong><\/h3>\n<ul>\n<li><strong>Supervised Learning<\/strong>: Uses labeled data (input-output pairs) to train models. Examples include classification and regression.<\/li>\n<li><strong>Unsupervised Learning<\/strong>: Uses unlabeled data to find patterns or groupings. Examples include clustering and dimensionality reduction.<\/li>\n<\/ul>\n<h3><strong>Explain the steps in making a decision tree.<\/strong><\/h3>\n<ol>\n<li><strong>Select the Best Feature<\/strong>: Choose the feature that best separates the data.<\/li>\n<li><strong>Split the Data<\/strong>: Divide the dataset based on the selected feature.<\/li>\n<li><strong>Repeat<\/strong>: Apply the process recursively to each subset of the data.<\/li>\n<li><strong>Stop<\/strong>: Stop when further splitting doesn\u2019t add value or when a pre-set condition is met (like tree depth).<\/li>\n<\/ol>\n<h3><strong>Differentiate between univariate, bivariate, and multivariate analysis.<\/strong><\/h3>\n<ul>\n<li><strong>Univariate Analysis<\/strong>: Analyzes one variable at a time (e.g., histograms).<\/li>\n<li><strong>Bivariate Analysis<\/strong>: Analyzes the relationship between two variables (e.g., scatter plots).<\/li>\n<li><strong>Multivariate Analysis<\/strong>: Analyzes more than two variables simultaneously (e.g., multiple regression).<\/li>\n<\/ul>\n<h3><strong>How should you maintain a deployed model?<\/strong><\/h3>\n<ol>\n<li><strong>Monitor Performance<\/strong>: Regularly check model accuracy and relevance.<\/li>\n<li><strong>Update with New Data<\/strong>: Retrain the model with new data to keep it accurate.<\/li>\n<li><strong>Handle Data Drift<\/strong>: Adjust for changes in data patterns over time.<\/li>\n<li><strong>Check for Bugs<\/strong>: Ensure the model is functioning as expected without errors.<\/li>\n<\/ol>\n<h3><strong>How is logistic regression done?<\/strong><\/h3>\n<ol>\n<li><strong>Prepare Data<\/strong>: Organize and clean your dataset.<\/li>\n<li><strong>Choose Features<\/strong>: Select relevant features for the model.<\/li>\n<li><strong>Apply Logistic Function<\/strong>: Use the logistic function to model the probability of the target variable.<\/li>\n<li><strong>Train the Model<\/strong>: Fit the model to the data using training data.<\/li>\n<li><strong>Evaluate<\/strong>: Check the model\u2019s performance using metrics like accuracy, precision, and recall.<\/li>\n<\/ol>\n<h3><strong>What is the significance of p-value?<\/strong><\/h3>\n<ul>\n<li>The p-value helps determine the significance of your results in hypothesis testing. A low p-value (typically &lt; 0.05) indicates strong evidence against the null hypothesis, suggesting the observed effect is statistically significant.<\/li>\n<\/ul>\n<h3><strong>Mention some techniques used for sampling.<\/strong><\/h3>\n<ol>\n<li><strong>Random Sampling<\/strong>: Every individual has an equal chance of being selected.<\/li>\n<li><strong>Stratified Sampling<\/strong>: Divide the population into subgroups and sample from each.<\/li>\n<li><strong>Systematic Sampling<\/strong>: Select every nth individual from a list.<\/li>\n<li><strong>Cluster Sampling<\/strong>: Divide the population into clusters and randomly sample clusters.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h3><strong>What is an Activation Function?<\/strong><\/h3>\n<ul>\n<li>An activation function is used in neural networks to decide whether a neuron should be activated or not. It adds non-linearity to the model, allowing it to learn complex patterns. Common examples include ReLU (Rectified Linear Unit), Sigmoid, and Tanh.<\/li>\n<\/ul>\n<h3><strong>Explain Naive Bayes<\/strong><\/h3>\n<ul>\n<li>Naive Bayes is a simple yet powerful classification algorithm based on Bayes&#8217; Theorem. It assumes that the features are independent of each other (hence &#8220;naive&#8221;). Despite this strong assumption, it works well for many problems like spam detection and text classification.<\/li>\n<\/ul>\n<h3><strong>What is a Confusion Matrix?<\/strong><\/h3>\n<ul>\n<li>A confusion matrix is a table used to evaluate the performance of a classification model. It shows the actual versus predicted classifications. The matrix includes four key terms: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). This helps in understanding the accuracy, precision, recall, and other performance metrics.<\/li>\n<\/ul>\n<h3><strong>What are Hyperparameters in Deep Learning?<\/strong><\/h3>\n<ul>\n<li>Hyperparameters are settings that control the training process of a machine learning model. Unlike model parameters, they are not learned from the data but are set before training. Examples include learning rate, number of epochs, batch size, and number of layers in a neural network. Tuning these hyperparameters can significantly affect the model&#8217;s performance.<\/li>\n<\/ul>\n<h3><strong>What is Hypothesis Testing?<\/strong><\/h3>\n<ul>\n<li>Hypothesis testing is a statistical method used to make decisions about a population based on sample data. It involves forming a null hypothesis (no effect or status quo) and an alternative hypothesis (some effect or difference). By calculating a p-value, we determine whether to reject the null hypothesis. It helps in assessing the validity of assumptions or claims.<\/li>\n<\/ul>\n<h3><strong>What are the popular libraries used in Data Science?<\/strong><\/h3>\n<ul>\n<li>The popular libraries used in Data Science are:<\/li>\n<\/ul>\n<table>\n<thead>\n<tr>\n<th>Python Libraries<\/th>\n<th>R Libraries<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>pandas<\/td>\n<td>ggplot2<\/td>\n<\/tr>\n<tr>\n<td>NumPy<\/td>\n<td>dplyr<\/td>\n<\/tr>\n<tr>\n<td>Matplotlib<\/td>\n<td>tidyr<\/td>\n<\/tr>\n<tr>\n<td>Seaborn<\/td>\n<td>caret<\/td>\n<\/tr>\n<tr>\n<td>scikit-learn<\/td>\n<td>shiny<\/td>\n<\/tr>\n<tr>\n<td>TensorFlow<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Keras<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>PyTorch<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>SciPy<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Statsmodels<\/td>\n<td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3><strong>What is variance in Data Science?<\/strong><\/h3>\n<ul>\n<li>Variance tells us how much each number in a dataset differs from the average. It shows the spread of values around the mean. Data scientists use variance to see how data is spread out and to understand its distribution.<\/li>\n<\/ul>\n<p style=\"text-align: center;\"><span data-sheets-root=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot; Introduction\\r\\nUnderstanding Data Visualization using Power BI \\r\\n Importance of Implementing Best Practices in Power BI Data Visualization\\r\\nPower bi data visualization Best practice ( list down the best practices )\\r\\nTechniques for Power BI Data Visualization\\r\\nconclusion\\r&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:769,&quot;3&quot;:{&quot;1&quot;:0},&quot;11&quot;:4,&quot;12&quot;:0}\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Ready to take your data science skills to the next level? Sign up for a free demo today!<\/a><\/strong><\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Infosys_Data_Science_Interview_Questions_Conclusion\"><\/span><strong>Infosys Data Science Interview Questions: Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Infosys data science interview questions cover fundamental topics like Python and R proficiency. They delve into machine learning concepts such as regularization and supervised versus unsupervised learning. Understanding these areas will prepare you well for technical assessments and discussions.<\/p>\n<div class=\"flex flex-grow flex-col max-w-full\">\n<div class=\"min-h-[20px] text-message flex w-full flex-col items-end gap-2 whitespace-pre-wrap break-words [.text-message+&amp;]:mt-5 overflow-x-auto\" dir=\"auto\" data-message-author-role=\"assistant\" data-message-id=\"877ffd42-0d60-4e5e-96d9-5a1f8e970f50\">\n<div class=\"flex w-full flex-col gap-1 empty:hidden first:pt-[3px]\">\n<div class=\"markdown prose w-full break-words dark:prose-invert light\">\n<p>Good luck with your interview process at Infosys!<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Like any other global technology company, the interview process at Infosys also consists of many rounds to assess the technical and behavioral skills of the applicant. It usually consists of 5 rounds: Online assessment round, technical interview round, behavioral interview round, Managerial interview round, and the HR round. In this article we will concentrate on [&hellip;]<\/p>\n","protected":false},"author":42,"featured_media":25588550,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[802,1864,1841],"tags":[],"class_list":["post-25588546","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","category-data-science-ml","category-entri-skilling"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Infosys Data Science Interview Questions - Entri Blog<\/title>\n<meta name=\"description\" content=\"In this article we will concentrate on the technical round and provide you with the Infosys Data Science Interview Questions.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/entri.app\/blog\/infosys-data-science-interview-questions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Infosys Data Science Interview Questions - 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