Course Summary
In this advanced data science capstone, you'll apply the skills you've learned in previous courses to solve a real-world data challenge. You'll work on a project from start to finish, including defining the problem, collecting and analyzing the data, building and evaluating models, and presenting your results.Key Learning Points
- Apply the skills you've learned in previous courses to solve a real-world data challenge
- Work on a project from start to finish, including defining the problem, collecting and analyzing the data, building and evaluating models, and presenting your results
- Collaborate with other learners and receive feedback from industry professionals
Related Topics for further study
Learning Outcomes
- Develop a real-world data science project from start to finish
- Apply data analysis and machine learning techniques to solve complex problems
- Collaborate with other learners and receive feedback from industry professionals
Prerequisites or good to have knowledge before taking this course
- Completion of previous courses in the Data Science specialization
- Familiarity with programming in R or Python
Course Difficulty Level
AdvancedCourse Format
- Project-based
- Collaborative
Similar Courses
- Applied Data Science Capstone
- Python Data Science Capstone
Related Education Paths
Related Books
Description
This project completer has proven a deep understanding on massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning and how to apply his knowledge in a real-world practical use case where he justifies architectural decisions, proves understanding the characteristics of different algorithms, frameworks and technologies and how they impact model performance and scalability.
Outline
- Week 1 - Identify DataSet and UseCase
- Capstone Introduction
- A warm welcome
- Overview of Architectural Methodologies for DataScience
- Lightweight IBM Cloud Garage Method for Data Science
- Data Sources and Use Cases
- Initial Data Exploration
- Architectural Decisions Document (ADD)
- Process Model Guidelines
- Week 2 - ETL and Feature Creation
- Extract Transform Load (ETL)
- Data Cleansing
- Feature Engineering
- Week 3 - Model Definition and Training
- Model Definition
- Model Training
- Model Evaluation, Tuning, Deployment and Documentation
- Model Evaluation
- Model Deployment
- Data Product (optional)
- Create ADD - Architectural Decisions Document
- Create a Video of your final presentation
Summary of User Reviews
The Advanced Data Science Capstone course on Coursera has received positive reviews from many users. It is highly regarded for its in-depth and practical approach to data science, enabling learners to apply their skills to real-world projects. One key aspect that users have highlighted is the course's emphasis on collaboration and teamwork, which helps to foster a supportive learning environment.Pros from User Reviews
- Practical and hands-on approach to data science
- Emphasis on collaboration and teamwork
- Real-world projects that apply learned skills
- Highly knowledgeable and engaging instructors
- Flexible learning schedule and access to course materials
Cons from User Reviews
- Challenging and time-consuming assignments
- Some technical issues with the platform
- Lack of personalized feedback from instructors
- Not suitable for beginners with no prior experience in data science
- Limited interaction with fellow students
Romeo Kienzler
4.6 Raiting
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