Analyzing Data with R
This course is part of multiple programs. Learn more.
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.
4.5
(168 ratings)
11,247 already enrolled
Instructors:
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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
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Applied Data Science with R, Data Analytics and Visualization with Excel and R
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.
Innovator in Data Science and Machine Learning
Yiwen Li is a dynamic Software Engineer at IBM, where she excels as a developer advocate, data scientist, and product manager. With approximately three years of experience in the tech industry, she focuses on designing and developing data science solutions and machine learning models to address real-world challenges. Yiwen is an active speaker, having delivered engaging talks at prominent conferences such as JupyterCon, PyCon, and Global AI on Tour 2020, attracting hundreds of attendees. Her commitment to advancing the field of data science is evident through her contributions to educational platforms, where she shares her expertise in various courses related to data analysis and visualization. Passionate about bridging the gap between technology and practical application, Yiwen continues to make a significant impact in the realm of artificial intelligence and data-driven decision-making.
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Frequently asked questions
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