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.
Este curso faz parte do Programa de cursos integrados Advanced Data Science with IBM
Advanced Data Science Capstone
oferecido por
Programa - O que você aprenderá com este curso
Semana
13 horas para concluir
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 vídeo (total de (Total 2 mín.) min), 7 leituras, 2 testes
1 vídeos
7 leituras
A warm welcome10min
Overview of Architectural Methodologies for DataScience10min
Lightweight IBM Cloud Garage Method for Data Science10min
Data Sources and Use Cases10min
Initial Data Exploration10min
Architectural Decisions Document (ADD)10min
Process Model Guidelines10min
1 exercício prático
Milestones Checklist Week 1
Semana
23 horas para concluir
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 leituras, 2 testes
3 leituras
Extract Transform Load (ETL)10min
Data Cleansing10min
Feature Engineering10min
1 exercício prático
Milestones Checklist Week 2
Semana
32 horas para concluir
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 leituras, 2 testes
2 leituras
Model Definition10min
Model Training10min
1 exercício prático
Milestones Checklist Week 3
Semana
45 horas para concluir
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 leituras, 3 testes
5 leituras
Model Evaluation10min
Model Deployment10min
Data Product (optional)10min
Create ADD - Architectural Decisions Document10min
Create a Video of your final presentation10min
1 exercício prático
Milestones Checklist Week 4
33%
comecei uma nova carreira após concluir estes cursos
33%
consegui um benefício significativo de carreira com este curso
Melhores avaliações
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!
Instrutores
Romeo Kienzler
Chief Data Scientist, Course LeadIBM Watson IoT
Sobre 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.
Sobre o Programa de cursos integrados 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Qual é a política de reembolso?
Existe algum auxílio financeiro disponível?
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