Statistics for Data Science with Python
Master Python-based statistical analysis from descriptive stats to hypothesis testing and regression. Perfect for data science beginners.
Master Python-based statistical analysis from descriptive stats to hypothesis testing and regression. Perfect for data science beginners.
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 Data Science Fundamentals with Python and SQL 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
Calculate and apply measures of central tendency and dispersion
Summarize and visualize data clearly for non-statisticians
Identify appropriate hypothesis tests for common data sets
Conduct hypothesis tests correlation tests and regression analysis
Demonstrate proficiency in statistical analysis using Python
Skills you'll gain
This course includes:
1.6 Hours PreRecorded video
12 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 9 modules in this course
This comprehensive course introduces basic principles of statistical methods and procedures for data analysis. Students learn data gathering, descriptive statistics, data visualization, probability distributions, hypothesis testing, ANOVA, and regression analysis using Python and Jupyter Notebooks. The curriculum emphasizes practical application through hands-on analysis, making it suitable for aspiring Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers with no prior statistics background.
Course Introduction and Python Basics
Module 1 · 1 Hours to complete
Introduction & Descriptive Statistics
Module 2 · 1 Hours to complete
Data Visualization
Module 3 · 1 Hours to complete
Introduction to Probability Distributions
Module 4 · 1 Hours to complete
Hypothesis testing
Module 5 · 1 Hours to complete
Regression Analysis
Module 6 · 1 Hours to complete
Project Case: Boston Housing Data
Module 7 · 4 Hours to complete
Final Exam
Module 8 · 50 Minutes to complete
Other Resources
Module 9 · 55 Minutes to complete
Fee Structure
Instructors
Data Scientist Aije Egwaikhide: Empowering Women in STEM and Innovating AI Solutions at IBM
Aije Egwaikhide is a fantastic example of how dedication and passion can lead to a successful career in tech! With her background in Economics and Statistics, paired with advanced qualifications in Business and Management Analytics, she’s truly paving the way in the field of data science. Her work at IBM, particularly in creating innovative machine learning solutions for the Oil and Gas sector, is an inspiring achievement.
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
Testimonials
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4.5 course rating
393 ratings
Frequently asked questions
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