{"id":60,"date":"2021-09-21T00:12:04","date_gmt":"2021-09-21T00:12:04","guid":{"rendered":"https:\/\/wordpress-288344-1043469.cloudwaysapps.com\/the-top-5-marketing-tips-copy\/"},"modified":"2024-08-16T07:07:34","modified_gmt":"2024-08-16T07:07:34","slug":"whats-our-course-like","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/","title":{"rendered":"Data Science Course Overview"},"content":{"rendered":"<p><b>Summary:<\/b><span style=\"font-weight: 400;\"> Pickl.AI\u2019s Data Science course provides a structured, hands-on approach to learning essential topics like Python, Pandas, statistics, and machine learning. With quizzes, assignments, and career-focused variants, the course ensures a thorough understanding and readiness for the Data Science industry, tailored to individual professional goals.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_81 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><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><\/span><\/a><\/span><\/div>\n<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:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#Python_for_Data_Science\" >Python for Data Science<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#Pandas\" >Pandas<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#Introduction_to_Statistics\" >Introduction to Statistics<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#Introduction_to_machine_learning\" >Introduction to machine learning<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#Supervised_Learning-I\" >Supervised Learning-I<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#Supervised_Learning-II\" >Supervised Learning-II<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#Unsupervised_Learning\" >Unsupervised Learning<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#What_makes_PicklAIs_Data_Science_course_unique\" >What makes Pickl.AI&#8217;s Data Science course unique?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#How_does_PicklAIs_course_structure_benefit_Data_Science_learners\" >How does Pickl.AI&#8217;s course structure benefit Data Science learners?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#What_additional_benefits_do_PicklAIs_paid_courses_offer\" >What additional benefits do Pickl.AI&#8217;s paid courses offer?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/whats-our-course-like\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><b>Introduction<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The typical Data Scientist of today does not start their journey like an engineer, doctor, lawyer, or other professional. While the latter had expertise in dedicated formal degree courses, for a <\/span><a href=\"https:\/\/pickl.ai\/blog\/what-is-data-science-comprehensive-guide\/\"><span style=\"font-weight: 400;\">Data Science<\/span><\/a><span style=\"font-weight: 400;\"> enthusiast, the journey was quite a labyrinth on the web.\u00a0<\/span><\/p>\n<p>You could spend countless hours watching richly animated lectures on top online learning platforms, but without optimal retention due to various factors.<\/p>\n<p><span style=\"font-weight: 400;\">For example, think of learning to drive a car (no, not self-driving cars). It\u2019d only help so much to observe your peers, parents, and strangers without getting your hands on a steering wheel.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Similarly, think about an imaginary author who envisages writing the world\u2019s best book but does not feel it\u2019s the \u201cright time\u201d to start unless he has known all the classics and writing styles inside-out. This plagues <\/span><a href=\"https:\/\/pickl.ai\/blog\/how-to-become-a-data-scientist\/\"><span style=\"font-weight: 400;\">budding data scientists<\/span><\/a><span style=\"font-weight: 400;\"> who do not learn and apply hands-on what they learn.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Pickl.AI, with the aid of its experienced in-house experts, who have spent their formative years in an aberrative and up-and-coming industry while the market has been in the doldrums, has built a course tailored to these requirements.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Their intimacy with the demands of the Indian ecosystem, along with tacit knowledge, holds them in good stead to address the apprehensions of today&#8217;s students.<\/span><\/p>\n<div class=\"flex-1 overflow-hidden\">\n<div class=\"h-full\">\n<div class=\"react-scroll-to-bottom--css-osvzs-79elbk h-full\">\n<div class=\"react-scroll-to-bottom--css-osvzs-1n7m0yu\">\n<div class=\"flex flex-col text-sm md:pb-9\">\n<article class=\"w-full text-token-text-primary focus-visible:outline-2 focus-visible:outline-offset-[-4px]\" dir=\"auto\" tabindex=\"0\" data-testid=\"conversation-turn-139\" data-scroll-anchor=\"true\">\n<div class=\"text-base py-[18px] px-3 md:px-4 m-auto md:px-5 lg:px-1 xl:px-5\">\n<div class=\"mx-auto flex flex-1 gap-4 text-base md:gap-5 lg:gap-6 md:max-w-3xl\">\n<div class=\"group\/conversation-turn relative flex w-full min-w-0 flex-col agent-turn\">\n<div class=\"flex-col gap-1 md:gap-3\">\n<div class=\"flex max-w-full flex-col flex-grow\">\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=\"fa577b94-43b6-4d2a-8c4b-15de73f6d0c8\">\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>All our courses follow a bottom-to-top syllabus, presenting and teaching the subject matter gradually. We rigorously ensure that our approach avoids conceptual gaps, which often lead to discontent and dissatisfaction in the era of MOOCs.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p><span style=\"font-weight: 400;\">Following are the modules of the course structure:<\/span><\/p>\n<h2 id=\"python-for-data-science\"><span class=\"ez-toc-section\" id=\"Python_for_Data_Science\"><\/span><b>Python for Data Science<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-13845\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Python-for-Data-Science.jpg\" alt=\"Python for Data Science\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Python-for-Data-Science.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Python-for-Data-Science-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Python-for-Data-Science-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Python-for-Data-Science-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Python-for-Data-Science-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Python-for-Data-Science-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Python-for-Data-Science-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Python-for-Data-Science-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Python-for-Data-Science-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Python-for-Data-Science-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/gigantic-python\/\"><span style=\"font-weight: 400;\">Python<\/span><\/a><span style=\"font-weight: 400;\"> is arguably the most-adopted <\/span><a href=\"https:\/\/pickl.ai\/blog\/best-programming-language-for-data-science\/\"><span style=\"font-weight: 400;\">programming language<\/span><\/a><span style=\"font-weight: 400;\"> by data scientists, owing to the flexibility and functionality it offers in its libraries. Apart from the introduction and setup of Jupyter Notebook, the module has three main sub-modules:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Introduction:<\/b><span style=\"font-weight: 400;\"> This chapter focuses on basic programming principles, including topics like data types, variables, conditionals, and functions. It also addresses the Pythonic syntax.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data Structures: <\/b><span style=\"font-weight: 400;\">Lists, dictionaries, sets, and iterative constructs are covered. This acts as an intermediary for learners who can now boast of knowing the basics well enough.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Libraries:<\/b><span style=\"font-weight: 400;\"> Matplotlib, a powerful visualisation library, and <\/span><a href=\"https:\/\/pickl.ai\/blog\/numpy-in-python-types-function\/\"><span style=\"font-weight: 400;\">NumPy<\/span><\/a><span style=\"font-weight: 400;\">, suited for various numeric applications, are elucidated here. These form the building blocks for the upcoming sections.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The section is strewn with quizzes to assess the learners&#8217; understanding. Notebooks used in the lectures and programming assignments are also provided for download.<\/span><\/p>\n<h2 id=\"pandas\"><span class=\"ez-toc-section\" id=\"Pandas\"><\/span><b>Pandas<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><a href=\"https:\/\/pandas.pydata.org\/\"><span style=\"font-weight: 400;\">Pandas<\/span><\/a><span style=\"font-weight: 400;\"> is a software library for <\/span><a href=\"https:\/\/pickl.ai\/blog\/data-manipulation-types-examples\/\"><span style=\"font-weight: 400;\">data manipulation<\/span><\/a><span style=\"font-weight: 400;\"> and analysis. <\/span><span style=\"font-size: revert;\">Professionals find it indispensable due to its simple yet powerful syntax, which enhances their ability to handle large datasets effectively.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Various popular courses leave out this section, which affects the learner negatively whenever an unseen line of code pops up as a step of a larger problem. <\/span><span style=\"font-size: revert;\">We have dedicated ten lectures to illustrating and explaining the nuances of the package. <\/span><span style=\"font-weight: 400;\">The section concludes with a quiz and an in-depth programming assignment.<\/span><\/p>\n<h2 id=\"introduction-to-statistics\"><span class=\"ez-toc-section\" id=\"Introduction_to_Statistics\"><\/span><b>Introduction to Statistics<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div class=\"flex-1 overflow-hidden\">\n<div class=\"h-full\">\n<div class=\"react-scroll-to-bottom--css-osvzs-79elbk h-full\">\n<div class=\"react-scroll-to-bottom--css-osvzs-1n7m0yu\">\n<div class=\"flex flex-col text-sm md:pb-9\">\n<article class=\"w-full text-token-text-primary focus-visible:outline-2 focus-visible:outline-offset-[-4px]\" dir=\"auto\" tabindex=\"0\" data-testid=\"conversation-turn-147\" data-scroll-anchor=\"true\">\n<div class=\"text-base py-[18px] px-3 md:px-4 m-auto md:px-5 lg:px-1 xl:px-5\">\n<div class=\"mx-auto flex flex-1 gap-4 text-base md:gap-5 lg:gap-6 md:max-w-3xl\">\n<div class=\"group\/conversation-turn relative flex w-full min-w-0 flex-col agent-turn\">\n<div class=\"flex-col gap-1 md:gap-3\">\n<div class=\"flex max-w-full flex-col flex-grow\">\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=\"4824f70b-c0f2-48a2-9363-ed2813b082b6\">\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>Statistics forms the foundation of data science because its concepts underpin all major paradigms in the field. We explain measures of central tendency, spread, position, histograms, and types of distributions, with a focus on the normal distribution and Student\u2019s-T distribution. Additionally, we cover the central limit theorem and hypothesis testing.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"flex-1 overflow-hidden\">\n<div class=\"h-full\">\n<div class=\"react-scroll-to-bottom--css-osvzs-79elbk h-full\">\n<div class=\"react-scroll-to-bottom--css-osvzs-1n7m0yu\">\n<div class=\"flex flex-col text-sm md:pb-9\">\n<article class=\"w-full text-token-text-primary focus-visible:outline-2 focus-visible:outline-offset-[-4px]\" dir=\"auto\" tabindex=\"0\" data-testid=\"conversation-turn-149\" data-scroll-anchor=\"true\">\n<div class=\"text-base py-[18px] px-3 md:px-4 m-auto md:px-5 lg:px-1 xl:px-5\">\n<div class=\"mx-auto flex flex-1 gap-4 text-base md:gap-5 lg:gap-6 md:max-w-3xl\">\n<div class=\"group\/conversation-turn relative flex w-full min-w-0 flex-col agent-turn\">\n<div class=\"flex-col gap-1 md:gap-3\">\n<div class=\"flex max-w-full flex-col flex-grow\">\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=\"98d6ce38-1751-4229-ab44-619692694004\">\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>Because the subject is theoretical, we follow each lecture in this module with a quiz. <span style=\"font-size: revert;\">The final quiz and the assignment complement this and reinforce the learning accumulated. Thus, the learner is set to dive deep into the advanced concepts.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"introduction-to-machine-learning\"><span class=\"ez-toc-section\" id=\"Introduction_to_machine_learning\"><\/span><b>Introduction to machine learning<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-13846\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Introduction-to-machine-learning.jpg\" alt=\"Introduction to machine learning\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Introduction-to-machine-learning.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Introduction-to-machine-learning-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Introduction-to-machine-learning-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Introduction-to-machine-learning-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Introduction-to-machine-learning-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Introduction-to-machine-learning-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Introduction-to-machine-learning-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Introduction-to-machine-learning-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Introduction-to-machine-learning-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2021\/09\/Introduction-to-machine-learning-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">All the relevant buzzwords\u2014learning process, <\/span><a href=\"https:\/\/www.ibm.com\/topics\/exploratory-data-analysis\"><span style=\"font-weight: 400;\">Exploratory Data Analysis<\/span><\/a><span style=\"font-weight: 400;\"> (EDA), feature selection, scaling and engineering, performance, and bias-variance\u2014are engaged in, emphasising building the right intuition. Contrary to popular perception, this is where data scientists spend m<\/span><span style=\"font-weight: 400;\">ost of their time<\/span><span style=\"font-weight: 400;\">. Thus, the philosophy and idea behind \u2018the preparatory stage\u2019 are communicated. There are five quizzes and two assignments to reinvigorate the fundamentals.<\/span><\/p>\n<h3 id=\"supervised-learning-i\"><span class=\"ez-toc-section\" id=\"Supervised_Learning-I\"><\/span><b>Supervised Learning-I<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/pickl.ai\/blog\/supervised-learning-vs-unsupervised-learning\/\"><span style=\"font-weight: 400;\">Supervised learning<\/span><\/a><span style=\"font-weight: 400;\"> is a machine learning technique that trains a model on labelled data. The model learns to map input data to the correct output by analysing examples. It then uses this learned mapping to predict outcomes for new, unseen data, making it highly effective for tasks like classification and regression.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">After understanding the rudiments, this section introduces and trains learners in the two most widely adapted Machine Learning techniques: <\/span><a href=\"https:\/\/pickl.ai\/blog\/a-tale-of-regression-and-regressiveness\/\"><span style=\"font-weight: 400;\">linear regression<\/span><\/a><span style=\"font-weight: 400;\"> and <\/span><a href=\"https:\/\/pickl.ai\/blog\/what-is-logistic-regression\/\"><span style=\"font-weight: 400;\">logistic regression<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<div class=\"flex-1 overflow-hidden\">\n<div class=\"h-full\">\n<div class=\"react-scroll-to-bottom--css-osvzs-79elbk h-full\">\n<div class=\"react-scroll-to-bottom--css-osvzs-1n7m0yu\">\n<div class=\"flex flex-col text-sm\">\n<article class=\"w-full text-token-text-primary focus-visible:outline-2 focus-visible:outline-offset-[-4px]\" dir=\"auto\" tabindex=\"0\" data-testid=\"conversation-turn-151\" data-scroll-anchor=\"true\">\n<div class=\"text-base py-[18px] px-3 md:px-4 m-auto md:px-5 lg:px-1 xl:px-5\">\n<div class=\"mx-auto flex flex-1 gap-4 text-base md:gap-5 lg:gap-6 md:max-w-3xl\">\n<div class=\"group\/conversation-turn relative flex w-full min-w-0 flex-col agent-turn\">\n<div class=\"flex-col gap-1 md:gap-3\">\n<div class=\"flex max-w-full flex-col flex-grow\">\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=\"faf268a2-de83-4005-86b6-4b05a6ef0f11\">\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>We provide separate videos on model training and evaluation for both topics to help you understand the conventional flow of action during real-world analysis. We include assumptions and variations to ensure a thorough comprehension.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h3 id=\"supervised-learning-ii\"><span class=\"ez-toc-section\" id=\"Supervised_Learning-II\"><\/span><b>Supervised Learning-II<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The module discusses additional concepts like decision trees, classification trees, <\/span><a href=\"https:\/\/pickl.ai\/blog\/bagging-vs-boosting-in-machine-learning\/\"><span style=\"font-weight: 400;\">bagging, and boosting<\/span><\/a><span style=\"font-weight: 400;\"> to equip prospective practitioners with other tools for achieving different end goals. This complements and based upon the previous sections and caps off the two-part module on supervised learning<\/span><\/p>\n<div class=\"flex-1 overflow-hidden\">\n<div class=\"h-full\">\n<div class=\"react-scroll-to-bottom--css-osvzs-79elbk h-full\">\n<div class=\"react-scroll-to-bottom--css-osvzs-1n7m0yu\">\n<div class=\"flex flex-col text-sm\">\n<article class=\"w-full text-token-text-primary focus-visible:outline-2 focus-visible:outline-offset-[-4px]\" dir=\"auto\" tabindex=\"0\" data-testid=\"conversation-turn-161\" data-scroll-anchor=\"true\">\n<div class=\"text-base py-[18px] px-3 md:px-4 m-auto md:px-5 lg:px-1 xl:px-5\">\n<div class=\"mx-auto flex flex-1 gap-4 text-base md:gap-5 lg:gap-6 md:max-w-3xl\">\n<div class=\"group\/conversation-turn relative flex w-full min-w-0 flex-col agent-turn\">\n<div class=\"flex-col gap-1 md:gap-3\">\n<div class=\"flex max-w-full flex-col flex-grow\">\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=\"7bc3423e-0ccf-43ff-aa06-4ee2adf814e7\">\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>A decision tree is a flowchart-like model that helps you make decisions and predictions. <span style=\"font-size: revert;\">Each branch represents a decision rule, and each leaf node represents an outcome. It&#8217;s used for classification (categorising data) and regression (predicting values).<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p><span style=\"font-weight: 400;\">Classification Trees are decision trees specifically used for classification tasks. The goal is to assign data into different categories based on input features. Each path from the root to a leaf represents a series of decisions that lead to a final classification.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bagging (Bootstrap Aggregating) is an ensemble learning method that improves the accuracy of <\/span><a href=\"https:\/\/pickl.ai\/blog\/how-to-build-a-machine-learning-model\/\"><span style=\"font-weight: 400;\">machine learning models<\/span><\/a><span style=\"font-weight: 400;\">. It involves training multiple versions of a model on different subsets of the data and then averaging their predictions. This reduces variance and helps prevent overfitting.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Boosting is another ensemble technique that combines multiple weak models to create a strong one. Unlike bagging, increasing focus on correcting errors made by previous models gradually improves the overall prediction accuracy. It\u2019s particularly effective in reducing bias.<\/span><\/p>\n<h3 id=\"unsupervised-learning\"><span class=\"ez-toc-section\" id=\"Unsupervised_Learning\"><\/span><b>Unsupervised Learning<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This section<\/span><span style=\"font-weight: 400;\"> covers clustering, including K-Means clustering, hierarchical clustering, association rule mining, and recommendation systems. This section, like the last one, is short on quizzes, but those will be offered eventually.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In addition to the curriculum stated above, you will be a part of an interactive and vibrant learning community, which will supplement your learning pursuits. Instructors and peers alike address posted doubts.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The learning portal is dynamic and allows you to learn anywhere and anytime at your convenience. We ensure that communication remains interactive and maintain our connection with learners even after they finish the course.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Our course has multiple variants. While Apprentice goes beyond the run-of-the-mill courses with live classes and case discussions, Wizard provides tips on job preparation through CV review, mock interviews, and guidance on an industry-relevant project.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">These are pinpoint and concise to ensure you continue to advance fruitfully in your professional aspirations. Please note that the assignments above are not provided for the Free and Dabbler variants.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">All in all, we would welcome all Data Science enthusiasts to join us in changing the way we perceive learning Data Science online. Take your time before making the decision. <\/span><span style=\"font-size: revert;\">Rest assured that investing in your future is the best decision you can make. With our assistance, we will leave no stone unturned to align it with your vision.<\/span><\/p>\n<p><b>Read Blog: <\/b><a href=\"https:\/\/pickl.ai\/blog\/developing-a-data-mindset\/\"><span style=\"font-weight: 400;\">Data Science Mindset<\/span><\/a><\/p>\n<h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><b>Frequently Asked Questions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"what-makes-pickl-ais-data-science-course-unique\"><span class=\"ez-toc-section\" id=\"What_makes_PicklAIs_Data_Science_course_unique\"><\/span><b>What makes Pickl.AI&#8217;s Data Science course unique?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Pickl.AI&#8217;s Data Science course stands out by offering a structured, hands-on approach that ensures no conceptual gaps. <\/span><span style=\"font-size: revert;\">Industry experts with deep knowledge of the Indian market designed this course. It includes practical assignments and quizzes to reinforce learning effectively.<\/span><\/p>\n<h3 id=\"how-does-pickl-ais-course-structure-benefit-data-science-learners\"><span class=\"ez-toc-section\" id=\"How_does_PicklAIs_course_structure_benefit_Data_Science_learners\"><\/span><b>How does Pickl.AI&#8217;s course structure benefit Data Science learners?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Pickl.AI&#8217;s course structure follows a bottom-to-top approach, gradually exposing learners to essential concepts. This ensures a smooth learning curve, avoiding the confusion often seen in other online courses, and enables better retention through practical exercises and assessments.<\/span><\/p>\n<h3 id=\"what-additional-benefits-do-pickl-ais-paid-courses-offer\"><span class=\"ez-toc-section\" id=\"What_additional_benefits_do_PicklAIs_paid_courses_offer\"><\/span><b>What additional benefits do Pickl.AI&#8217;s paid courses offer?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Paid versions of Pickl.AI&#8217;s courses, such as Apprentice and Wizard, offer live classes, case discussions, CV reviews, mock interviews, and project guidance. These benefits enhance career readiness and ensure learners are well-prepared for the Data Science industry.<\/span><\/p>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><b>Conclusion<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Pickl.AI\u2019s Data Science course offers a comprehensive, hands-on learning experience tailored to the needs of aspiring data scientists. With a structured curriculum covering essential topics like Python, Pandas, statistics, and machine learning, the course ensures no conceptual gaps. Learners benefit from quizzes, assignments, and interactive community support.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The course variants provide additional career-focused benefits, making Pickl.AI a valuable investment for anyone serious about a career in Data Science. <\/span><span style=\"font-size: revert;\">Whether you\u2019re just starting out or aiming to advance your skills, Pickl.AI\u2019s courses will align with your professional goals.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Pickl.AI\u2019s Data Science course: Hands-on learning in Python, ML, and more with career-focused benefits.\n","protected":false},"author":29,"featured_media":13847,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[46],"tags":[1438,93,1436,1433,1434,1432,1437,1435],"ppma_author":[2219,2184],"class_list":{"0":"post-60","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"tag-data-science-course-fees","9":"tag-data-science-course-overview","10":"tag-introduction-to-data-science","11":"tag-relevant-courses-for-data-science","12":"tag-what-classes-to-take-for-data-science","13":"tag-what-is-data-science-course-about","14":"tag-what-is-data-science-in-simple-words","15":"tag-what-order-to-learn-data-science"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.3 (Yoast SEO v27.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Overview of Data Science Courses for career growth<\/title>\n<meta name=\"description\" content=\"Pickl.AI\u2019s Data Science course offers hands-on learning, covering Python, Pandas, and machine learning. 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