Mastering Data Analysis in Excel
Learn practical data analysis techniques using Excel, from binary classification to linear regression, with focus on business applications.
Learn practical data analysis techniques using Excel, from binary classification to linear regression, with focus on business applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Excel to MySQL: Analytic Techniques for Business Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
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What you'll learn
Design and implement predictive models based on data
Calculate and interpret business uncertainty measures
Apply binary classification techniques effectively
Master linear regression for business analysis
Use Excel functions for data analysis applications
Skills you'll gain
This course includes:
3.7 Hours PreRecorded video
14 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 7 modules in this course
This comprehensive course focuses on practical data analysis techniques using Microsoft Excel. Students learn essential concepts including binary classification, information theory, entropy measures, and linear regression. The curriculum emphasizes real-world business applications, culminating in a final project where learners develop predictive models for credit card applications. The course covers data analysis methods without requiring advanced Excel features like Macros or Pivot Tables, making it accessible while providing robust analytical skills.
About This Course
Module 1 · 30 Minutes to complete
Excel Essentials for Beginners
Module 2 · 2 Hours to complete
Binary Classification
Module 3 · 2 Hours to complete
Information Measures
Module 4 · 2 Hours to complete
Linear Regression
Module 5 · 3 Hours to complete
Additional Skills for Model Building
Module 6 · 1 Hours to complete
Final Course Project
Module 7 · 9 Hours to complete
Fee Structure
Instructors
Executive in Residence and Director of Quantitative Modeling at Duke University
Daniel Egger is an accomplished Executive in Residence and Director of the Center for Quantitative Modeling at Duke University, with over seventeen years of experience in creating innovative software products and services. As the founder and CEO of several venture-backed technology companies, he has a strong background in entrepreneurship and venture capital, having also served as a Managing Partner in a venture capital fund. Since 2003, Daniel has taught courses in entrepreneurship and venture capital at Duke's Master of Engineering Management Program, where he has played a key role in developing the curriculum. He was previously the Howard Johnson Foundation Entrepreneur-in-Residence in Duke’s Markets and Management Program for undergraduates. In addition to his teaching responsibilities, Daniel is committed to online education, with his data science courses on Coursera reaching over 400,000 students globally. His extensive experience and dedication to education position him as a significant contributor to the fields of engineering management and quantitative modeling.
Neuroscientist and Advocate for Ethical AI Development
Dr. Jana Schaich Borg is an Assistant Research Professor at Duke University, specializing in neuroscience and social cognition. She earned her Ph.D. in Neuroscience from Stanford University and focuses on developing innovative methods to infer network properties from high-dimensional multi-modal neural data. Her research explores critical areas such as social behavior, empathy, and moral decision-making, positioning her as a leading figure in the intersection of neuroscience and artificial intelligence. Dr. Schaich Borg is actively involved in interdisciplinary teams that leverage cutting-edge technologies to tackle complex challenges in understanding human behavior. She advocates for training programs that equip scientists to apply their research to real-world issues and educates entrepreneurs on supporting structures that foster disruptive innovation in biomedical science. Her commitment to ensuring that big data contributes positively to society drives her current projects, which include developing moral artificial intelligences and understanding the role of social synchrony in mental health. Through her work, Dr. Schaich Borg aims to bridge the gap between human values and AI development, ensuring ethical considerations are central to technological advancements.
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4.2 course rating
3,915 ratings
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