{"id":16914,"date":"2025-03-24T13:25:43","date_gmt":"2025-03-24T13:25:43","guid":{"rendered":"https:\/\/unp.education\/content\/?p=16914"},"modified":"2025-03-24T13:25:43","modified_gmt":"2025-03-24T13:25:43","slug":"advanced-data-science-questions-for-interviews","status":"publish","type":"post","link":"https:\/\/unp.education\/content\/advanced-data-science-questions-for-interviews\/","title":{"rendered":"Data Science Interview Questions Advanced"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"16914\" class=\"elementor elementor-16914\">\n\t\t\t\t<div class=\"elementor-element elementor-element-68d4b64 e-flex e-con-boxed e-con e-parent\" data-id=\"68d4b64\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-84894c2 elementor-widget elementor-widget-heading\" data-id=\"84894c2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\"> 1.What is the difference between supervised and unsupervised learning?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-41d41a9 elementor-widget elementor-widget-text-editor\" data-id=\"41d41a9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Supervised learning involves training a model using labeled data, where the model learns the relationship between input and output. For example, predicting house prices based on features like size and location. In unsupervised learning, there are no labels, and the model finds patterns or structures within the data, such as grouping customers into segments.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e35cd45 e-flex e-con-boxed e-con e-parent\" data-id=\"e35cd45\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d6f9777 elementor-widget elementor-widget-heading\" data-id=\"d6f9777\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2: What is overfitting in machine learning?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dcd159d elementor-widget elementor-widget-text-editor\" data-id=\"dcd159d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Overfitting occurs when a model performs well on training data but fails to generalize to new, unseen data. This happens when the model is too complex, capturing noise instead of the underlying pattern. To prevent overfitting, techniques such as cross-validation, pruning, and regularization can be used.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-33caf40 e-con-full e-flex e-con e-child\" data-id=\"33caf40\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t<div class=\"elementor-element elementor-element-1874771 e-con-full e-flex e-con e-child\" data-id=\"1874771\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-246cf61 elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"246cf61\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Ready to take you Data Science and Machine Learning skills to the next level? Check out our comprehensive Mastering Data Science and ML with Python course.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-db843fc e-con-full e-flex e-con e-child\" data-id=\"db843fc\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4469b57 elementor-widget elementor-widget-image\" data-id=\"4469b57\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"300\" height=\"169\" src=\"https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-300x169.png\" class=\"attachment-medium size-medium wp-image-15815\" alt=\"Mastering Data Science and ML with Python\" srcset=\"https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-300x169.png 300w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-1024x576.png 1024w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-768x432.png 768w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-600x338.png 600w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194.png 1280w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-49b987a elementor-align-center elementor-widget elementor-widget-button\" data-id=\"49b987a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/unp.education\/course-overview\/mastering-data-science-and-ml-with-python\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Register Now<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3948d41 e-flex e-con-boxed e-con e-parent\" data-id=\"3948d41\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-003589a elementor-widget elementor-widget-heading\" data-id=\"003589a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3: Explain the concept of cross-validation<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8ad3936 elementor-widget elementor-widget-text-editor\" data-id=\"8ad3936\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves dividing the dataset into multiple subsets, training the model on some subsets, and testing it on the others. This helps ensure that the model performs well on different samples of data, improving its generalizability. Types of cross-validation include k-fold cross-validation and leave-one-out cross-validation.<\/p><p>\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6932789 e-flex e-con-boxed e-con e-parent\" data-id=\"6932789\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a3a940c elementor-widget elementor-widget-heading\" data-id=\"a3a940c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">4: What are precision and recall?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9893daa elementor-widget elementor-widget-text-editor\" data-id=\"9893daa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Precision is the ratio of correctly predicted positive observations to the total predicted positives. It answers the question, &#8220;Of all the positive predictions made, how many were correct?&#8221; Recall (or sensitivity) is the ratio of correctly predicted positive observations to all actual positives. It answers the question, &#8220;Of all the actual positive cases, how many did we predict correctly?&#8221;<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2b64f20 e-flex e-con-boxed e-con e-parent\" data-id=\"2b64f20\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7e416fc elementor-widget elementor-widget-heading\" data-id=\"7e416fc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">5: What is a confusion matrix?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4a0c263 elementor-widget elementor-widget-text-editor\" data-id=\"4a0c263\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>A confusion matrix is a table that is used to evaluate the performance of a classification algorithm. It shows the true positives, false positives, true negatives, and false negatives, helping you understand how well the model is performing. For example, a confusion matrix can be used to analyze how well a model is classifying spam emails versus non-spam emails.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-8734d6e e-flex e-con-boxed e-con e-parent\" data-id=\"8734d6e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d022f7d elementor-widget elementor-widget-heading\" data-id=\"d022f7d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">6: How do you handle missing data in a dataset?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bbbb81d elementor-widget elementor-widget-text-editor\" data-id=\"bbbb81d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Handling missing data is crucial for accurate data analysis. Common methods include:<\/p><ul><li><span style=\"color: #000000;\"><strong>Mean\/median imputation<\/strong>:<\/span> Replacing missing values with the mean or median of the feature.<\/li><li><span style=\"color: #000000;\"><strong>Dropping rows<\/strong><\/span>: Removing rows with missing values, though this can reduce the dataset size.<\/li><li><span style=\"color: #000000;\"><strong>Using algorithms that handle missing data<\/strong>:<\/span> Certain algorithms, like decision trees, can handle missing values internally.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-93b1705 e-flex e-con-boxed e-con e-parent\" data-id=\"93b1705\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4c5346d elementor-widget elementor-widget-heading\" data-id=\"4c5346d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">7: Explain the bias-variance tradeoff<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9056ec9 elementor-widget elementor-widget-text-editor\" data-id=\"9056ec9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Bias is the error introduced by simplifying assumptions made by the model, while variance refers to the model&#8217;s sensitivity to fluctuations in the training data. A model with high bias oversimplifies the data, while a model with high variance overfits it. The tradeoff between bias and variance is a fundamental concept in machine learning. Balancing both is necessary to achieve good predictive performance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-12e37ce e-flex e-con-boxed e-con e-parent\" data-id=\"12e37ce\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0572cbc elementor-widget elementor-widget-heading\" data-id=\"0572cbc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\"> 8: What is the difference between Type I and Type II errors?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bdb67b5 elementor-widget elementor-widget-text-editor\" data-id=\"bdb67b5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Type I error (false positive) occurs when the model incorrectly rejects a true null hypothesis. Type II error (false negative) happens when the model fails to reject a false null hypothesis. For example, in medical testing, a Type I error would be diagnosing a healthy person with a disease, while a Type II error would be failing to detect the disease in a sick person.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c55e3e3 e-con-full e-flex e-con e-child\" data-id=\"c55e3e3\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t<div class=\"elementor-element elementor-element-9455398 e-con-full e-flex e-con e-child\" data-id=\"9455398\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0ce636b elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"0ce636b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Ready to take you Data Science and Machine Learning skills to the next level? Check out our comprehensive Mastering Data Science and ML with Python course.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f77c24d e-con-full e-flex e-con e-child\" data-id=\"f77c24d\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e09b703 elementor-widget elementor-widget-image\" data-id=\"e09b703\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"300\" height=\"169\" src=\"https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-300x169.png\" class=\"attachment-medium size-medium wp-image-15815\" alt=\"Mastering Data Science and ML with Python\" srcset=\"https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-300x169.png 300w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-1024x576.png 1024w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-768x432.png 768w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-600x338.png 600w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194.png 1280w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cf0481b elementor-align-center elementor-widget elementor-widget-button\" data-id=\"cf0481b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/unp.education\/course-overview\/mastering-data-science-and-ml-with-python\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Register Now<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0a15c18 e-flex e-con-boxed e-con e-parent\" data-id=\"0a15c18\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c52d717 elementor-widget elementor-widget-heading\" data-id=\"c52d717\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">9: Explain the concept of regularization?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0682ff9 elementor-widget elementor-widget-text-editor\" data-id=\"0682ff9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Regularization is a technique used to prevent overfitting by adding a penalty to the model&#8217;s complexity. Two common types of regularization are:<\/p><ul><li><span style=\"color: #000000;\"><strong>L1 regularization (Lasso)<\/strong>:<\/span> It adds the absolute value of coefficients as a penalty term to the loss function, driving some coefficients to zero, thus performing feature selection.<\/li><li><span style=\"color: #000000;\"><strong>L2 regularization (Ridge)<\/strong><\/span>: It adds the square of coefficients as a penalty term, shrinking coefficients but not eliminating them completely.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-742aacb e-flex e-con-boxed e-con e-parent\" data-id=\"742aacb\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8bebdcb elementor-widget elementor-widget-heading\" data-id=\"8bebdcb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">10: What is Principal Component Analysis (PCA)?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-84900ef elementor-widget elementor-widget-text-editor\" data-id=\"84900ef\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>PCA is a dimensionality reduction technique that transforms a dataset into a set of orthogonal components called principal components. It captures the most important variance in the data while reducing the number of features. PCA is commonly used in scenarios where high-dimensional data needs to be visualized or simplified.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f8d2145 e-flex e-con-boxed e-con e-parent\" data-id=\"f8d2145\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5968624 elementor-widget elementor-widget-heading\" data-id=\"5968624\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">11: What is the difference between a histogram and a bar chart?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9bcb007 elementor-widget elementor-widget-text-editor\" data-id=\"9bcb007\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>A histogram represents the distribution of numerical data and groups the data into continuous intervals called bins. It is used to understand the underlying frequency distribution of the data. A bar chart, on the other hand, displays categorical data using rectangular bars, where the height of each bar is proportional to the category&#8217;s frequency.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7717577 e-con-full e-flex e-con e-child\" data-id=\"7717577\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t<div class=\"elementor-element elementor-element-484f95c e-con-full e-flex e-con e-child\" data-id=\"484f95c\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9583915 elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"9583915\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Ready to take you Data Science and Machine Learning skills to the next level? Check out our comprehensive Mastering Data Science and ML with Python course.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2e5c03e e-con-full e-flex e-con e-child\" data-id=\"2e5c03e\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-05f43f7 elementor-widget elementor-widget-image\" data-id=\"05f43f7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"300\" height=\"169\" src=\"https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-300x169.png\" class=\"attachment-medium size-medium wp-image-15815\" alt=\"Mastering Data Science and ML with Python\" srcset=\"https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-300x169.png 300w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-1024x576.png 1024w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-768x432.png 768w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194-600x338.png 600w, https:\/\/unp.education\/content\/wp-content\/uploads\/2024\/07\/Mastering-Data-Science-ML-with-Python_1721672148194.png 1280w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-75abe4b elementor-align-center elementor-widget elementor-widget-button\" data-id=\"75abe4b\" data-element_type=\"widget\" data-e-type=\"widget\" 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class=\"elementor-element elementor-element-69ef594 elementor-widget elementor-widget-heading\" data-id=\"69ef594\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Our Students Testimonials:<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ab860aa e-con-full e-flex e-con e-child\" data-id=\"ab860aa\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-5466192 e-con-full e-flex e-con e-child\" data-id=\"5466192\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-35eba59 e-con-full e-flex e-con e-child\" data-id=\"35eba59\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-613cc60 elementor-widget elementor-widget-video\" data-id=\"613cc60\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/aVxP3zF0YsE?si=Yz6aLB9NGCNKCBz6&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d34a2b0 e-con-full e-flex e-con e-child\" data-id=\"d34a2b0\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-75cfd98 elementor-widget elementor-widget-video\" data-id=\"75cfd98\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/Cv92eezCg9w?si=6ca76uOqbVoPdgEI&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9f64e27 e-con-full e-flex e-con e-child\" data-id=\"9f64e27\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-4271df5 e-con-full e-flex e-con e-child\" data-id=\"4271df5\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c5c7943 elementor-widget elementor-widget-video\" data-id=\"c5c7943\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/iALhOYlbkCQ?si=N8pEWOfhvEVPyEub&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e6710ec e-con-full e-flex e-con e-child\" data-id=\"e6710ec\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b9b82f3 elementor-widget elementor-widget-video\" data-id=\"b9b82f3\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/BGv6TJxGizc?si=N6C5kh1xYnG8b8Zr&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>1.What is the difference between supervised and unsupervised learning? Supervised learning involves training a model using labeled data, where the model learns the relationship between input and output. For example, predicting house prices based on features like size and location. In unsupervised learning, there are no labels, and the model finds patterns or structures within &#8230; <a title=\"Data Science Interview Questions Advanced\" class=\"read-more\" href=\"https:\/\/unp.education\/content\/advanced-data-science-questions-for-interviews\/\" aria-label=\"Read more about Data Science Interview Questions Advanced\">Read more<\/a><\/p>\n","protected":false},"author":7951,"featured_media":16963,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17,60,16,18],"tags":[],"class_list":["post-16914","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analysis","category-business-analystics","category-data-science","category-interview-preparation"],"_links":{"self":[{"href":"https:\/\/unp.education\/content\/wp-json\/wp\/v2\/posts\/16914","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/unp.education\/content\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/unp.education\/content\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/unp.education\/content\/wp-json\/wp\/v2\/users\/7951"}],"replies":[{"embeddable":true,"href":"https:\/\/unp.education\/content\/wp-json\/wp\/v2\/comments?post=16914"}],"version-history":[{"count":13,"href":"https:\/\/unp.education\/content\/wp-json\/wp\/v2\/posts\/16914\/revisions"}],"predecessor-version":[{"id":16964,"href":"https:\/\/unp.education\/content\/wp-json\/wp\/v2\/posts\/16914\/revisions\/16964"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/unp.education\/content\/wp-json\/wp\/v2\/media\/16963"}],"wp:attachment":[{"href":"https:\/\/unp.education\/content\/wp-json\/wp\/v2\/media?parent=16914"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/unp.education\/content\/wp-json\/wp\/v2\/categories?post=16914"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/unp.education\/content\/wp-json\/wp\/v2\/tags?post=16914"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}