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🔥Data Scientist Masters Program (Discount Code - YTBE15) - https://www.simplilearn.com/data-science-course?utm_campaign=1UKr6BhCCdg&utm_medium=DescriptionFirstFold&utm_source=Youtube 🔥Microsoft Azure - Data Analyst Course - https://www.simplilearn.com/in/data-analyst-course?utm_campaign=1UKr6BhCCdg&utm_medium=DescriptionFirstFold&utm_source=Youtube 🔥Microsoft Azure - Data Analyst Course - https://www.simplilearn.com/in/data-analyst-course?utm_campaign=1UKr6BhCCdg&utm_medium=DescriptionFirstFold&utm_source=Youtube 🔥Data Scientist Masters Program (Discount Code - YTBE15) - https://www.simplilearn.com/data-science-course?utm_campaign=1UKr6BhCCdg&utm_medium=DescriptionFirstFold&utm_source=Youtube In this video on Use Cases Of Mathematics In Data Science from Simplilearn, we delve into the fascinating world of mathematics in data science. We start with an introduction to the fundamental use cases of mathematics in data science, providing a solid foundation for understanding its importance. The video then covers Bayesian Inference, a powerful statistical method for updating the probability of a hypothesis as more evidence or information becomes available. Next, we discuss Hypothesis Testing, a key technique for making decisions or inferences about populations based on sample data. Following that, we explore Linear Regression, a fundamental tool for modeling the relationship between a dependent variable and one or more independent variables. Finally, we examine specific use cases in healthcare. 00:00:06 - Introduction to Use Cases Of Mathematics In Data Science 00:02:30 - Bayesian Inference 00:03:50 - Hypothesis Testing 00:08:40 - Linear Regression 00:13:32 - Use Cases In Healthcare ✅ How is mathematics used in data science? Mathematics underpins data science by providing tools for data analysis, model building, and optimization. Key areas include statistics for data summarization, linear algebra for data manipulation, calculus for optimizing models, and probability theory for assessing risks and uncertainties. ✅ What are the math foundations for data science? The math foundations for data science include linear algebra, which is essential for understanding data manipulation and algorithms; calculus, particularly for optimization techniques used in machine learning; probability and statistics, which are crucial for data analysis, inferential statistics, and predictive modeling; and discrete mathematics, which underpins algorithms and data structures. Together, these areas provide the theoretical framework and practical tools needed to analyze data, build models, and interpret results effectively. ✅Is data science math or CS? Data science is an interdisciplinary field that integrates principles from both mathematics and computer science. It involves the application of mathematical techniques for statistical analysis and data modeling, alongside leveraging computer science skills for data processing, algorithm development, and machine learning. ✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH #DataScience #Mathematics #DataScientist #2024 #Simplilearn ➡️ About Data Science Course in collaboration with IBM This Data Science course in collaboration with IBM propels your career to become a data scientist. Gain expertise in in-demand skills like Python, SQL, Excel, Machine Learning, Tableau, generative AI, and more. Dive deep into data interpretation nuances, master Machine Learning, and enhance programming skills to elevate your Data Science career. Key Features ✅ Simplilearn's JobAssist helps you get noticed by top hiring companies ✅ Masterclasses from IBM experts ✅ Dedicated live sessions by faculty of industry experts ✅ Industry-recognized Data Scientist Master’s certificate from Simplilearn ✅ Industry-recognized IBM certifications for IBM courses ✅ Ask-Me-Anything (AMA) sessions with IBM leadership ✅ Capstone from 3 domains and 25+ projects ✅ Exclusive hackathons conducted by IBM ✅ Lifetime access to self-paced learning content ✅ Program crafted to initiate your journey as a Data Scientist ✅ Integrated labs for hands-on learning experience Skills Covered ✅ Generative AI ✅ Prompt Engineering ✅ ChatGPT ✅ Exploratory Data Analysis ✅ Descriptive Statistics ✅ Inferential Statistics ✅ Explainable AI ✅ Conversational AI ✅ Large Language Models ✅ Model Building and Finetuning ✅ Ensemble Learning ✅ Data Visualization ✅ Database Management 👉 Learn More At: https://www.simplilearn.com/in/data-analyst-course?utm_campaign=18June2024UseCasesInDSOSL&utm_medium=Description&utm_source=youtube 🔥🔥
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