Applied Data Science Capstone
Self-paced course
Certification program
Price
Rating
Overview
This is the final course in the IBM Data Science Professional Certificate as well as the Applied Data Science with Python Specialization. This capstone project course will give you the chance to practice the work that data scientists do in real life when working with datasets.
In this course you will assume the role of a Data Scientist working for a startup intending to compete with SpaceX, and in the process follow the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting your results to stakeholders.
You will be tasked with predicting if the first stage of the SpaceX Falcon 9 rocket will land successfully. With the help of your Data Science findings and models, the competing startup you have been hired by can make more informed bids against SpaceX for a rocket launch.
In this course, there will not be much new learning, instead you’ll focus on hands-on work to demonstrate and apply what you have learnt in previous courses. By successfully completing this Capstone you will have added a project to your data science and machine learning portfolio to showcase to employers.
An excellent course! Requires you to bring together a large part of the material learned in previous courses.
Furthermore, adds new skills like creating a presentation using PowerPoint.
Very challenging but truly rewarding. I learnt a lot through the mistakes I made throughout the project but there is a lot of support on the forum and the internet to help with any issue.
Very in-depth and rewarding project - would be a little better if more specific guidance on what to exactly include in the notebook would be better. But overall very stimulating. Thanks!
You will learn
1
Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders
2
Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation
3
Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors
4
Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model
Learning outcomes
Post this credential on your LinkedIn profile, resume, or CV, and don’t forget to celebrate your achievement by sharing it across your social networks or mentioning it during your performance review
