Ce cours fait partie de la Spécialisation Advanced Data Science with IBM
Advanced Data Science Capstone
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À propos de ce cours
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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.
Programme du cours : ce que vous apprendrez dans ce cours
1
Section
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 vidéo (Total 2 min), 6 lectures, 2 quiz
1 vidéo
6 lectures
A warm welcome10 min
Overview of Architectural Methodologies for DataScience10 min
Lightweight IBM Cloud Garage Method for Data Science10 min
Data Sources and Use Cases10 min
Initial Data Exploration10 min
Architectural Decisions Document (ADD)10 min
1 exercice pour s'entraîner
Milestones Checklist Week 1 min
2
Section
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 lectures, 2 quiz
3 lectures
Extract Transform Load (ETL)10 min
Data Cleansing10 min
Feature Engineering10 min
1 exercice pour s'entraîner
Milestones Checklist Week 2 min
3
Section
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 lectures, 2 quiz
2 lectures
Model Definition10 min
Model Training10 min
1 exercice pour s'entraîner
Milestones Checklist Week 3 min
4
Section
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 lectures, 3 quiz
5 lectures
Model Evaluation10 min
Model Deployment10 min
Data Product (optional)10 min
Create ADD - Architectural Decisions Document10 min
Create a Video of your final presentation10 min
1 exercice pour s'entraîner
Milestones Checklist Week 4 min
Enseignant
Romeo Kienzler
Chief Data Scientist, Course LeadIBM Watson IoT
À propos de 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.
À propos de la Spécialisation Advanced Data Science with IBM
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.
Foire Aux 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?
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