Statistics for Data Science with Python
Discover powerful data analysis techniques through Python programming, exploring fundamental statistical concepts and their applications in real datasets.
Discover powerful data analysis techniques through Python programming, exploring fundamental statistical concepts and their applications in real datasets.
Master fundamental statistical techniques for data science with this hands-on course. Using Python and Jupyter Notebooks, you'll explore data gathering, descriptive statistics, data visualization, probability distributions, hypothesis testing, ANOVA, and regression analysis. Ideal for aspiring data scientists, analysts, and researchers, this course provides practical skills in statistical thinking and data interpretation, culminating in a real-world inspired project to solidify your understanding.
Instructors:
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What you'll learn
Calculate and apply measures of central tendency and dispersion for grouped and ungrouped data
Summarize, present, and visualize data clearly for non-statisticians
Identify and conduct appropriate hypothesis tests for common data sets
Perform correlation tests and regression analysis
Demonstrate proficiency in statistical analysis using Python and Jupyter Notebooks
Apply statistical concepts to real-world data science problems
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
This course provides a comprehensive introduction to statistical methods essential for data science. Students will learn to gather, summarize, and visualize data using Python and Jupyter Notebooks. The curriculum covers descriptive statistics, probability distributions, hypothesis testing, ANOVA, and regression analysis. Participants will develop skills in statistical thinking and reasoning, applying these concepts to real-world scenarios. The course emphasizes hands-on practice, enabling learners to perform statistical analyses using Python and interpret results accurately. By the end, students will have a solid foundation in statistical methods for data analysis, preparing them for roles in data science, analytics, and research.
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
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Frequently asked questions
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