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. Please note: You are requested to create a short video presentation at the end of the course. This is mandatory to pass. You don't need to share the video in public.
This course is part of the Advanced Data Science with IBM Specialization
Advanced Data Science Capstone
Offered By
Syllabus - What you will learn from this course
Week
13 hours to complete
Week 1 - Identify DataSet and UseCase
In this module, the basic process model used for this capstone project is introduced. Furthermore, the learner is required to identify a practical use case and data set
1 video (Total 2 min), 7 readings, 2 quizzes
1 video
7 readings
A warm welcome10m
Overview of Architectural Methodologies for DataScience10m
Lightweight IBM Cloud Garage Method for Data Science10m
Data Sources and Use Cases10m
Initial Data Exploration10m
Architectural Decisions Document (ADD)10m
Process Model Guidelines10m
1 practice exercise
Milestones Checklist Week 1
Week
23 hours to complete
Week 2 - ETL and Feature Creation
This module emphasizes on the importance of ETL, data cleansing and feature creation as a preliminary step in ever data science project
3 readings, 2 quizzes
3 readings
Extract Transform Load (ETL)10m
Data Cleansing10m
Feature Engineering10m
1 practice exercise
Milestones Checklist Week 2
Week
32 hours to complete
Week 3 - Model Definition and Training
This module emphasizes on model selection based on use case and data set. It is important to understand how those two factors impact choice of a useful model algorithm.
2 readings, 2 quizzes
2 readings
Model Definition10m
Model Training10m
1 practice exercise
Milestones Checklist Week 3
Week
45 hours to complete
Model Evaluation, Tuning, Deployment and Documentation
One a model is trained it is important to assess its performance using an appropriate metric. In addition, once the model is finished, it has to be made consumable by business stakeholders in an appropriate way
5 readings, 3 quizzes
5 readings
Model Evaluation10m
Model Deployment10m
Data Product (optional)10m
Create ADD - Architectural Decisions Document10m
Create a Video of your final presentation10m
1 practice exercise
Milestones Checklist Week 4
4.6
14 Reviews33%
started a new career after completing these courses
33%
got a tangible career benefit from this course
Top reviews from Advanced Data Science Capstone
I liked the peer-graded environment.\n\nLike the final submission requirements. That's really helps in aquiring the skills like presentation skills, Documentation skills, project mangement
Like that course. It combine all you skills in a one project. It is very helpful for the understanding why and how ML can help for the business. Personal thanks for Romeo Kienzler!
Instructor
Romeo Kienzler
Chief Data Scientist, Course LeadIBM Watson IoT
About IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
About the Advanced Data Science with IBM Specialization
As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.
If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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