Essential Mathematics for Data Science
Master the core mathematical concepts crucial for data science, AI, and machine learning in this comprehensive course.
Master the core mathematical concepts crucial for data science, AI, and machine learning in this comprehensive course.
This intermediate-level course provides a solid foundation in the mathematical principles underpinning data science, artificial intelligence, and machine learning. Participants will explore key concepts in probability, statistics, optimization, and linear algebra, essential for understanding and implementing advanced data science techniques. The curriculum covers a wide range of topics, from basic probability theory to complex machine learning algorithms, including market basket analysis, recommender systems, and feature selection methods. Students will learn about probability distributions, hypothesis testing, optimization techniques like gradient descent, and fundamental linear algebra operations crucial for AI and ML model development. The course emphasizes practical applications, with hands-on exercises using Excel to conduct hypothesis testing, optimization, and linear algebra operations. Designed for both students and practitioners, this course serves as an excellent preparation for a career in data analytics and provides a strong foundation for further studies in AI and machine learning.
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What you'll learn
Understand the role of probability theory, optimization, and linear algebra in AI and ML
Apply probability distributions like binomial and normal in machine learning model development
Conduct hypothesis tests such as Z-test and t-test for ML model development
Explain and apply optimization and linear algebra concepts in ML and AI contexts
Use Excel to perform hypothesis testing, optimization, and linear algebra operations
Understand feature selection techniques to avoid overfitting and underfitting in ML models
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course offers a comprehensive introduction to the mathematical foundations of data science, artificial intelligence, and machine learning. It covers key areas including probability theory, statistics, optimization, and linear algebra, all essential for understanding and implementing advanced data science techniques. The curriculum begins with basic probability concepts and progresses to more complex topics such as random variables, probability distributions, and the central limit theorem. Students will learn about crucial machine learning concepts like feature selection, hypothesis testing, and optimization algorithms including gradient descent. The course also delves into linear algebra fundamentals necessary for AI and ML model development. Throughout the course, there's a strong emphasis on practical applications, with students learning to use Excel for hypothesis testing, optimization, and linear algebra operations. This course is designed to provide a robust foundation for those pursuing careers in data analytics or further studies in AI and ML.
Fee Structure
Instructor
Distinguished Analytics Expert and Data Science Scholar
U Dinesh Kumar serves as Professor of Decision Sciences at the Indian Institute of Management Bangalore, where he chairs the MBA program in Business Analytics. His expertise in business analytics and artificial intelligence has earned him recognition as one of India's Top 10 Most Prominent Analytics Academicians. Through his prolific academic contributions, including 38 case studies published by Harvard Business Publishing (seven of which are bestsellers) and over 70 research articles, he continues to shape the field of data science education. His books "Business Analytics: The Science of Data-Driven Decision Making" and "Machine Learning Using Python" have become Amazon India bestsellers, reflecting his ability to make complex analytical concepts accessible. As Chairperson of IIMB's Business Analytics program, he leads initiatives to advance data-driven decision making while bridging the gap between theoretical analytics and practical business applications.
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