Apply various data analysis and visualization skills and techniques you have learned by taking on the role of a data scientist working with real-world data sets.
An Advanced Course by IBM
Welcome to the "Data Science Capstone" course, an advanced-level offering from IBM that serves as the culminating experience for students in the IBM Data Science with R or IBM Data Analytics with Excel and R Professional Certificate Programs. This comprehensive course is designed to simulate real-world data science challenges, allowing you to apply and integrate the diverse skills you've acquired throughout your journey.
As a participant in this data-science-capstone, you'll step into the shoes of a newly hired data scientist faced with a complex challenge that demands a full spectrum of data science competencies. This immersive experience will push you to leverage your skills in data-collection, data-wrangling, exploratory-data-analysis, hypothesis-testing, data-visualization, and modeling-data to tackle authentic business problems using real-world-datasets.
While there are no strict prerequisites listed, students should have a solid foundation in:
The data-scientist-skills acquired in this course are directly applicable to a wide range of industries and roles. Graduates will be equipped to:
By completing this ibm-data-science capstone, students will have a portfolio-ready project that demonstrates their ability to apply data-scientist-skills to real-world challenges, significantly enhancing their employability in the competitive field of data science and analytics.
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Bridge the gap between what you know and the skills you need to ensure your success and efficiency in an office setting. This course is ideal for learners who are just starting out in their professional careers, those looking to add some hard skills to their soft skills, or those looking to make the leap from contractor to full-time employee.
"Basics of Data Science" gives a comprehensible overview of many fundamental concepts and tools of data science, including data quality and data preprocessing, supervised and unsupervised learning techniques including their evaluation, frequent itemsets and association rules, sequence mining, process mining, text mining, and responsible data science.
