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Ranjan Relan
As part of this guided project, you shall build your first Machine Learning Pipeline using DataIku tool without writing a single line of code. You shall build a prediction model which inputs COVID daily count data across the world and predict COVID fatalities.DataIku tool is a low code no code platform which is gaining traction with citizen data scientist to quickly build and deploy their models.
Register for this course and see more details by visiting:
OpenCourser.com/course/unerde/build
What's inside
Syllabus
Build your first Machine Learning Pipeline using DataIku
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills for building machine learning pipelines without needing to code
Introduces DataIku, an easy-to-use platform for citizen data scientists
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Save Build your first Machine Learning Pipeline using Dataiku to your list so you can find it easily later:
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Reviews summary
Very user friendly
Learners say this very user friendly course is a best platform to learn about Dataiku and has an overall good project. It includes a great overview on how to use DSS.
This course provides a solid overview of DSS.
"Great overview on how to use DSS."
This course is very user friendly.
"Best platform to learn"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Build your first Machine Learning Pipeline using Dataiku with these
activities:
Review fundamental concepts of machine learning
Show steps
Strengthen foundational knowledge in machine learning to enhance comprehension of pipeline development.
Browse courses on
Machine Learning
Show steps
-
Review online resources or textbooks on machine learning
-
Complete practice exercises or quizzes to test understanding
-
Discuss concepts with peers or instructors
Seek guidance from experienced data engineers or data scientists
Show steps
Connect with experts in the field to gain insights, receive guidance, and accelerate learning.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors through professional networks or online platforms
-
Reach out to mentors and express interest in their guidance
-
Schedule regular meetings or communication channels for mentorship
-
Seek advice on course-related challenges, career development, and industry best practices
Practice running DataIku pipeline components individually
Show steps
Build familiarity with individual components of the DataIku pipeline to enhance understanding of their functionality and integration.
Show steps
-
Review documentation for each component
-
Create sample datasets to test component functionality
-
Run components individually and observe their output
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete DataIku Academy tutorials on pipeline development
Show steps
Gain practical experience in building and deploying pipelines with guided instructions.
Show steps
-
Enroll in DataIku Academy
-
Complete tutorials covering pipeline design, data transformations, and model training
-
Apply techniques learned in tutorials to course project
Attend a virtual study group with classmates
Show steps
Engage with peers to share knowledge, discuss course material, and reinforce understanding through collaboration.
Show steps
-
Find classmates interested in forming a study group
-
Set up regular virtual meetings
-
Prepare questions or discussion topics
-
Take turns presenting material and leading discussions
Develop a COVID-19 prediction model using DataIku
Show steps
Apply knowledge gained in the course to create a tangible and valuable deliverable that demonstrates proficiency in pipeline development.
Show steps
-
Gather and preprocess COVID-19 data
-
Select and train machine learning models
-
Deploy the model using DataIku's deployment capabilities
-
Evaluate model performance and make improvements
Contribute to open-source machine learning projects
Show steps
Gain practical experience in machine learning and contribute to the community by working on open-source projects.
Browse courses on
Machine Learning
Show steps
-
Identify open-source machine learning projects that align with interests
-
Review project documentation and codebase
-
Make contributions to the project, such as bug fixes or feature enhancements
-
Engage with the project community through forums or issue trackers
Review fundamental concepts of machine learning
Show steps
Strengthen foundational knowledge in machine learning to enhance comprehension of pipeline development.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review online resources or textbooks on machine learning
- Complete practice exercises or quizzes to test understanding
- Discuss concepts with peers or instructors
Seek guidance from experienced data engineers or data scientists
Show steps
Connect with experts in the field to gain insights, receive guidance, and accelerate learning.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks or online platforms
- Reach out to mentors and express interest in their guidance
- Schedule regular meetings or communication channels for mentorship
- Seek advice on course-related challenges, career development, and industry best practices
Practice running DataIku pipeline components individually
Show steps
Build familiarity with individual components of the DataIku pipeline to enhance understanding of their functionality and integration.
Show steps
Show steps
Show steps
- Review documentation for each component
- Create sample datasets to test component functionality
- Run components individually and observe their output
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete DataIku Academy tutorials on pipeline development
Show steps
Gain practical experience in building and deploying pipelines with guided instructions.
Show steps
Show steps
Show steps
- Enroll in DataIku Academy
- Complete tutorials covering pipeline design, data transformations, and model training
- Apply techniques learned in tutorials to course project
Attend a virtual study group with classmates
Show steps
Engage with peers to share knowledge, discuss course material, and reinforce understanding through collaboration.
Show steps
Show steps
Show steps
- Find classmates interested in forming a study group
- Set up regular virtual meetings
- Prepare questions or discussion topics
- Take turns presenting material and leading discussions
Develop a COVID-19 prediction model using DataIku
Show steps
Apply knowledge gained in the course to create a tangible and valuable deliverable that demonstrates proficiency in pipeline development.
Show steps
Show steps
Show steps
- Gather and preprocess COVID-19 data
- Select and train machine learning models
- Deploy the model using DataIku's deployment capabilities
- Evaluate model performance and make improvements
Contribute to open-source machine learning projects
Show steps
Gain practical experience in machine learning and contribute to the community by working on open-source projects.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify open-source machine learning projects that align with interests
- Review project documentation and codebase
- Make contributions to the project, such as bug fixes or feature enhancements
- Engage with the project community through forums or issue trackers
Career center
Learners who complete Build your first Machine Learning Pipeline using Dataiku will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data, and then develop machine learning models to make predictions or recommendations. This course may be particularly useful for Data Scientists who are interested in using DataIku to build machine learning pipelines.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, test, and deploy machine learning models. They may also be involved in data collection and preparation, as well as developing and maintaining the infrastructure that supports machine learning systems. This course may be particularly useful for Machine Learning Engineers who are interested in using DataIku to build machine learning pipelines.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations to businesses on how to improve their operations. This course may be particularly useful for Data Analysts who are interested in using DataIku to build machine learning pipelines.
Business Analyst
Business Analyst
Business Analysts work with businesses to identify and solve problems. They use data analysis, process improvement, and other techniques to help businesses improve their performance. This course may be particularly useful for Business Analysts who are interested in using DataIku to build machine learning pipelines to solve business problems.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They work with stakeholders to define project goals and objectives, and then develop and implement project plans. They also track project progress and make adjustments as needed. This course may be particularly useful for Project Managers who are working on projects that involve machine learning.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with stakeholders to understand the requirements for a software system, and then design and implement the system to meet those requirements. This course may be particularly useful for Software Engineers who are working on projects that involve machine learning.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that supports data analysis. They work with data scientists and other data professionals to ensure that data is available, reliable, and secure. This course may be particularly useful for Data Engineers who are working on projects that involve machine learning.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use statistical methods to make predictions and recommendations. This course may be particularly useful for Statisticians who are interested in using DataIku to build machine learning pipelines.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve business problems. They work with businesses to improve their operations, such as by optimizing supply chains or scheduling staff. This course may be particularly useful for Operations Research Analysts who are interested in using DataIku to build machine learning pipelines to solve business problems.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make recommendations to investors and other stakeholders. They use financial models to forecast future performance and identify investment opportunities. This course may be particularly useful for Financial Analysts who are interested in using DataIku to build machine learning pipelines to analyze financial data.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about markets and customers. They use their findings to help businesses make informed decisions about products, marketing, and other business strategies. This course may be particularly useful for Market Researchers who are interested in using DataIku to build machine learning pipelines to analyze market data.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. They work with insurance companies, pension funds, and other financial institutions to develop and implement risk management strategies. This course may be particularly useful for Actuaries who are interested in using DataIku to build machine learning pipelines to assess risk and uncertainty.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. They use economic models to forecast economic trends and make recommendations to policymakers. This course may be particularly useful for Economists who are interested in using DataIku to build machine learning pipelines to analyze economic data.
For more career information including salaries, visit:
OpenCourser.com/course/unerde/build
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Build your first Machine Learning Pipeline using Dataiku .
This guide provides a solid foundation in machine learning using Python, covering common algorithms, techniques, and real-world applications.
This guide provides a comprehensive overview of Python for data analysis, covering data manipulation, data visualization, and machine learning.
Save
Provides a comprehensive overview of deep learning, covering fundamental concepts, architectures, and applications.
This guide provides a detailed overview of machine learning algorithms, covering a wide range of techniques and their applications.
This guide provides a gentle introduction to data analytics, covering data exploration, visualization, and analysis.
This guide provides a hands-on introduction to data science, covering Python programming, data manipulation, and machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/unerde/build
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ranjan Relan
Language
English
Good to know
Develops skills for building machine learning pipelines without needing to code
Introduces DataIku, an easy-to-use platform for citizen data scientists
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