Об этом курсе: 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.
Автор: IBM
Преподаватели: Romeo Kienzler, Chief Data Scientist, Course Lead
IBM Watson IoT
| Основные сведения | Курс 4 из 4 — Advanced Data Science with IBM Specialization |
| Уровень | Продвинутые функции |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
Программа курса
НЕДЕЛЯ 1
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 видео, 6 материала для самостоятельного изучения
- Lecture: A warm welcome
- Lecture: Overview of Architectural Methodologies for DataScience
- Lecture: Lightweight IB Cloud Garage Method for Data Science
- Lecture: Data Sources and Use Cases
- Lecture: Initial Data Exploration
- Lecture: Architectural Decisions Document (ADD)
- Examen par les pairs: Assignment 1
- Demande de discussion: Discussion Promt Week 1
Оцениваемый: Milestones Checklist Week 1
НЕДЕЛЯ 2
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 материала для самостоятельного изучения
- Lecture: Extract Transform Load (ETL)
- Lecture: Data Cleansing
- Lecture: Feature Engineering
- Demande de discussion: Discussion Prompt Week 2
- Examen par les pairs: Assignment 2
Оцениваемый: Milestones Checklist Week 2
НЕДЕЛЯ 3
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 материала для самостоятельного изучения
- Lecture: Model Definition
- Lecture: Model Training
- Demande de discussion: Discussion Promt Week 3
- Examen par les pairs: Assignment 3
Оцениваемый: Milestones Checklist Week 3
НЕДЕЛЯ 4
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 материала для самостоятельного изучения
- Lecture: Model Evaluation
- Lecture: Model Deployment
- Lecture: Data Product (optional)
- Lecture: Create ADD - Architectural Decisions Document
- Lecture: Create a Video of your final presentation
- Demande de discussion: Discussion Promt Week 4
- Examen par les pairs: Assignment 4
Оцениваемый: Milestones Checklist Week 4
Оцениваемый: Project Asset Submission
Часто задаваемые вопросы
Как это работает
Travail en cours
Chaque cours fonctionne comme un manuel interactif en proposant des vidéos préenregistrées, des quiz et des projets.
Aide de la part de vos pairs
Connectez-vous à des milliers d'autres étudiants et débattez sur des idées, discutez le contenu du cours et obtenez de l'aide pour en maîtriser les concepts.
Certificats
Obtenez une reconnaissance officielle pour votre travail et partagez votre réussite avec vos amis, vos collègues et vos employeurs.
Авторы
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.
Стоимость
| Приобрести курс | |
|---|---|
| Получить доступ к материалам курса | Доступен |
| Получить доступ к оцениваемым материалам курса | Доступен |
| Получить итоговую оценку | Доступен |
| Получите сертификат о прохождении курса, ссылкой на который можно делиться с другими людьми | Доступен |
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