Statistical Analysis Fundamentals using Excel
Master essential statistical concepts and Excel tools for business analytics. Learn data analysis, visualization, and forecasting techniques.
Master essential statistical concepts and Excel tools for business analytics. Learn data analysis, visualization, and forecasting techniques.
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 IBM Business Intelligence (BI) Analyst Professional Certificate 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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What you'll learn
Learn fundamental statistical concepts and their business applications
Master data visualization techniques for effective communication
Apply probability concepts to real-world business scenarios
Develop regression models for forecasting and prediction
Perform hypothesis testing for data-driven decisions
Skills you'll gain
This course includes:
2.15 Hours PreRecorded video
6 quizzes, 3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 5 modules in this course
This comprehensive course provides a foundation in statistical analysis using Microsoft Excel for business and data professionals. The curriculum covers essential topics including descriptive statistics, data visualization, probability distributions, and regression analysis. Through hands-on exercises and real-world examples, students learn to apply statistical methods for data-driven decision-making. The course emphasizes practical applications in business intelligence, focusing on techniques for analyzing sales performance, creating forecasts, and interpreting statistical results.
Introduction and Descriptive Statistics
Module 1 · 2 Hours to complete
Data Visualization
Module 2 · 1 Hours to complete
Introduction to Probability Distributions
Module 3 · 2 Hours to complete
Regression Analysis and Forecasting
Module 4 · 2 Hours to complete
Analysing Sales Performance and Forecasting
Module 5 · 4 Hours to complete
Fee Structure
Instructors
Data Science and Urban Analytics Expert at Toronto Metropolitan University
Murtaza Haider serves as Associate Dean of Graduate Programs and Professor of Data Science and Real Estate Management at Toronto Metropolitan University (formerly Ryerson University), where he also directs the Urban Analytics Institute. After earning his Master's in Transport Engineering and PhD in Civil Engineering from the University of Toronto, he began his academic career at McGill University, where he founded the Urban Systems Lab. His research spans business analytics, data science, housing markets, urban planning, and infrastructure development, with significant impact through his books "Real Estate Markets: An Introduction" (2020) and "Getting Started with Data Science" (2016). His educational influence extends globally through his IBM-collaborated data science courses, reaching over one million learners worldwide. As a syndicated columnist with Postmedia, his insights on real estate markets appear regularly in major Canadian newspapers. He maintains connections with industry as Director of Regionomics Inc., while holding an adjunct professorship at McGill University. His work combines academic research with practical applications in urban economics and data analytics
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At IBM Skills Network, we understand the importance of building hands-on, job-ready skills quickly to stay competitive in today’s fast-paced world. Our courses, developed by industry experts at the forefront of technological innovation, cover key areas such as AI, software development, cybersecurity, data science, and business management. With real-world insights and practical learning in every module, we equip both individuals and teams with the future-focused technical and business knowledge necessary for success in an ever-evolving landscape.
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4.4 course rating
15 ratings
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