Data Science Course Notes

Enhance your learning journey with in-depth data science lecture notes. Deepen your understanding of core concepts in data science, statistics, probability, Python, and machine learning.

Course notes are the perfect complementary online data science study materials. They help you grasp fundamental concepts, refresh your memory when preparing for exams, and get a taste of our learning style.

Committed to your career success and development, we at 365 Data Science support you throughout your learning journey with free resources designed to enhance and guide your education. Our rich selection covers everything from data science notes for beginners to simplified explanations of advanced topics. Choose a subject you wish to master, download our free PDF course notes, and start learning.

Grey Cover of Understanding Data Literacy. This course notes resource is from 365 Data Science.
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Understanding Data Literacy

Thanks to the mass digitalization of the world, information has become the oil of the 21st century that sustains the engines of modern businesses. As of 2021, there are approximately 4,66 billion internet users! Imagine the amount of data, that consumers leave behind that just waits to be stored and processed by businesses. Therefore, in these free pdf course notes on Understanding Data Literacy, we are going to identify various types of data, the three defining properties of Big Data and the different methods for storing data.

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Grey Cover of Machine Learning with Naïve Bayes. This course notes resource is from 365 Data Science Team.
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Machine Learning with Naïve Bayes

Naïve Bayes Classifier is a supervised classification machine learning algorithm inspired by the Bayes Theorem. Its ability to make intuitive real time-predictions from small non-linear sets makes it perfect for consumer behavior predictions, recommendation systems and text analysis - news article categorization, email category filtering and sentiment analyses. In the free Machine Learning with Naïve Bayes pdf course notes we are going to build upon your sklearn Naïve Bayes skills by going over the algorithm’s computational capabilities, outlining the 7 steps in creating a supervised machine learning model and identifying 6 relevant metrics to use for performance evaluation.

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