Develop a deeper understanding of data science and machine learning through analyzing and visualizing data in a real-world business scenario.
Showcase your data science skills on your LinkedIn profile and resumé with this hands-on capstone project.
US$ 99 - US$ 199
To enable you to showcase all that you have learnt in the IBM Data Science Professional Certificate series of courses, this final course offers you the opportunity to complete a capstone project.
The project is divided into 5 parts:
During this project, you will use the techniques you have learnt throughout the courses in this program. Activities will include data ingestion, data exploration, data visualization, feature engineering, probabilistic modeling, model validation, and more.
At the end of this course, you will have completed a project that you can include on your resumé and LinkedIn profile which clearly showcases your knowledge and skills.
This capstone project is an excellent way to demonstrate the skills and knowledge you have learned in the IBM Data Science Professional Certificate Program. Please note, you need to have completed all the previous courses in this program to be able to enroll for this capstone project.
It is a self-paced project, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the project takes to complete, it is anticipated that if you work 3-4 hours per week, you will complete the course in 4 weeks. However, as long as the project is completed before the end date, you can work at your own pace.
The project's materials are available as soon as you enroll and will remain available for the duration of your enrollment. You will be given detailed instructions on how to complete it.
Once you have successfully completed the project, you will earn your IBM Certificate.
As part of our mentoring service, you will have access to valuable guidance and support throughout the project. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services may vary package wise.
You will be able to:
Before taking this course, you should have already completed the following courses:
Earn your certificate
Once you have completed this
you will earn your certificate.
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A capstone project provides learners with the opportunity to apply what they have learned in a course to a practical application. It is designed to present learners with a real-world challenge to find a solution. The objective is to enable you to showcase your understanding of the subject matter you have been taught. It is something you can make reference to on your resumé.
1. A capstone project prepares you for the future, and is a useful feature for preparing learners for their future studies. It allows you to utilize what you have learned during the course.
2. A capstone project provides a platform upon which a learner can develop further. It highlights the level of your base knowledge and facilitates options for your career when you finish your education. It can also assist you in identifying your specific interests in order that you can pursue a career path that is appropriate for you.
Once you have completed your Data Science and Machine Learning Capstone Project, you will have learned how to use real-world data science tools to create a showcase project. Plus, you can then demonstrate to employers that you are job ready for a role in data science. You will earn a Certificate of Completion.
As you complete the course, you will be confidently able to:
• Apply your knowledge of data science and machine learning to a real-life scenario.
• Analyze and visualize data using Python.
• Perform a feature engineering exercise using Python.
• Build and validate a predictive machine learning model using Python.
• Create and share actionable insights to real-life data problems.
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