Applied Data Science Capstone

Applied Data Science Capstone

Applied Data Science Capstone This course is part of multiple programs. Learn more Close This course is part of multiple programs IBM Data Science Professional Certificate Applied Data Science Specialization OK Taught in English 22 languages available Some content may not be translated Instructors: Yan Luo +1 more Close Instructors Instructor ratings We asked all


The Applied Data Science Capstone is a comprehensive and challenging program that allows students to apply their knowledge and skills in data science to real-world problems. This capstone project is the culmination of the Data Science Specialization and serves as a final assessment of the student’s ability to analyze, visualize, and interpret large datasets.

The Applied Data Science Capstone requires students to work on a project that addresses a specific business or social problem using data science techniques. Students are required to define the problem, collect and clean data, perform exploratory data analysis, build and evaluate predictive models, and present their findings in a clear and concise manner.

Throughout the capstone project, students are guided by experienced instructors who provide feedback and support to help students successfully complete their projects. Additionally, students have access to a wide range of resources

Applied Data Science Capstone

This course is part of multiple programs.

Taught in English

Some content may not be translated

Yan Luo
Joseph Santarcangelo

Instructors: Yan Luo

156,068 already enrolled


Gain insight into a topic and learn the fundamentals


(6,973 reviews)



Intermediate level

Recommended experience

13 hours (approximately)
Flexible schedule
Learn at your own pace

What you’ll learn

  • Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders 

  • Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation

  • Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors

  • Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model 

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