Analyzing Data with R
Learn to solve real-world data analysis problems using R programming through hands-on practice with airline performance data.
Learn to solve real-world data analysis problems using R programming through hands-on practice with airline performance data.
This practical course teaches data analysis using R programming language through real-world applications. Students learn the complete data analysis workflow, from data preparation and wrangling to exploratory analysis and model development. Using an airline performance dataset, participants gain hands-on experience in handling missing values, conducting statistical analysis, and building predictive models. The course emphasizes practical skills in data preprocessing, exploratory analysis, and model evaluation to deliver meaningful insights.
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What you'll learn
Master data preparation techniques including handling missing values and data normalization
Conduct comprehensive exploratory data analysis using statistical methods
Develop and evaluate predictive models using various regression techniques
Optimize model performance through regularization and grid search
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 comprehensive course guides learners through the complete data analysis process using R programming. Students work with real airline performance data to develop practical skills in data preparation, analysis, and modeling. The curriculum covers essential techniques for data wrangling, exploratory data analysis, and statistical modeling. Topics include handling missing values, data normalization, descriptive statistics, ANOVA, correlation analysis, and regression modeling. Special emphasis is placed on model evaluation and performance tuning.
Fee Structure
Instructors
Program Director at IBM, Champion for Open Source Data & AI and Inclusivity
Gabriela de Queiroz is a Program Director at IBM, leading a team of developers focused on Data & AI Open Source projects. She is dedicated to democratizing AI, building tools, and launching innovative open-source initiatives. Gabriela is passionate about making data science accessible to all and is actively involved with several organizations to promote an inclusive and diverse tech community.
2 Courses
A Distinguished Data Scientist Bridging Education Technology and AI Innovation
Tiffany Zhu currently serves as an ML Engineer at Meta after previously working as a Cognitive Developer at IBM. After earning her BS in Mathematical Sciences with a focus in Statistics and Computer Science from Carnegie Mellon University and MS in Data Science from Columbia University, she has built an impressive career spanning education technology and artificial intelligence. At IBM, she contributed significantly to open-source projects, including ParData, and developed course content for Data Analysis with R. Her earlier experience includes working as a Software Engineer at EAB, where she developed degree audit engines for college students, and internships at NASA and Global Engineering and Materials. Her work demonstrates a consistent commitment to making machine learning more accessible, evidenced by her contributions to IBM's accelerator catalog where she created the Comments Organizer asset using sentiment analysis and clustering techniques
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