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Romeo Kienzler
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/oryh0h/advanced
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What's inside
Syllabus
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
Read more
Syllabus
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
Read more
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
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.
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
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches fundamental techniques required for more advanced deep learning models and set architectures
Course is hands-on and includes interactive materials
Requires learners to come in with extensive background knowledge
Suitable for those looking to develop professional skills or deep expertise in this topic
Taught by instructors who are recognized for their work in industry
Explores advanced techniques that are highly relevant to industry
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Reviews summary
Capstone for data science skills
Learners say this advanced capstone course in data science is well-structured and consists of engaging assignments such as a capstone project that helps learners practice machine learning processes. While some learners struggled with the difficulty of the final assignment and thought the course was not advanced enough, students overall enjoyed their learning experience and recommend the course.
Instructors are knowledgeable and passionate about the subject.
"Appreciate the trainers to come up with such training excercise"
"Very pleased to have a project for my portfolio though. Thank you to all the instructors who presented very clearly and especially to Romeo who has a real gift for getting across his real passion for the subject."
Capstone project is helpful for practicing data science skills.
"The capstone project was helpful in learning the materials"
"Nice training. I enjoyed it a lot. "
"This was quite enriching as I was able to perform data science analysis with the help of Pyspark and Tensorflow"
Some learners thought the course was not advanced enough.
"I believe that this is a great course for beginners, unfortunately the course is not advanced and is in need of updating."
"Not much work by the teachers: almost no videos, the texts are not very well written and not so interesting."
Some learners found the capstone project difficult.
"
Hard to follow ... found a lot of assistance in discussion forums
"
"The instructions for the final assignment need to be a lot clearer as it is a massive leap in difficulty when the labs in the previous modules/courses have been quite limited."
"I will not complete this course, I can't create a video explaining all the project, you should check the duration of the course, you can't create a video in (13.5 / 4) hours explaining all the project, moreover if the project is interesting."
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 Advanced Data Science Capstone with these
activities:
Volunteer as a tutor or mentor for students in the course
Show steps
Enhance your understanding by explaining concepts to others and supporting their learning.
Browse courses on
Mentoring
Show steps
-
Contact the course instructor or reach out to students directly
-
Offer your assistance with specific topics or general support
-
Be patient and provide constructive feedback
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron
Show steps
Gain a broader understanding of machine learning concepts and practical applications.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Obtain a copy of the book
-
Allocate time for reading
-
Take notes and highlight key concepts
Follow the Machine Learning Specialization on Coursera
Show steps
Gain in-depth knowledge of machine learning principles and techniques through a structured learning path.
Browse courses on
Online Learning
Show steps
-
Enroll in the Machine Learning Specialization
-
Complete the modules and assignments
-
Participate in discussion forums
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete the TensorFlow Machine Learning Crash Course
Show steps
Build a stronger foundation in applying TensorFlow to machine learning problems.
Browse courses on
TensorFlow
Show steps
-
Enroll in the TensorFlow Machine Learning Crash Course
-
Follow along with the video tutorials
-
Complete the hands-on exercises
Solve practice problems on LeetCode
Show steps
Sharpen your problem solving skills and reinforce algorithms and data structures learned in the course.
Browse courses on
Data Structures
Show steps
-
Create an account on LeetCode
-
Select a topic or skill to practice
-
Solve the problems and review solutions
Join a study group for the course
Show steps
Collaborate with peers to discuss concepts, solve problems, and exchange perspectives.
Show steps
-
Reach out to classmates or join an existing study group
-
Establish a regular meeting schedule
-
Prepare questions and topics for discussion
Create a comprehensive study guide
Show steps
Organize and synthesize course materials for effective revision and exam preparation.
Browse courses on
Study Guide
Show steps
-
Gather notes, assignments, and other relevant materials
-
Identify key concepts and summarize them
-
Organize the study guide logically
Develop a machine learning model for a real-world problem
Show steps
Apply your knowledge and skills to solve a practical problem using machine learning.
Show steps
-
Identify a suitable problem
-
Gather and prepare data
-
Train and evaluate machine learning models
-
Deploy and monitor the model
Volunteer as a tutor or mentor for students in the course
Show steps
Enhance your understanding by explaining concepts to others and supporting their learning.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Contact the course instructor or reach out to students directly
- Offer your assistance with specific topics or general support
- Be patient and provide constructive feedback
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron
Show steps
Gain a broader understanding of machine learning concepts and practical applications.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book
- Allocate time for reading
- Take notes and highlight key concepts
Follow the Machine Learning Specialization on Coursera
Show steps
Gain in-depth knowledge of machine learning principles and techniques through a structured learning path.
Browse courses on
Online Learning
Show steps
Show steps
Show steps
- Enroll in the Machine Learning Specialization
- Complete the modules and assignments
- Participate in discussion forums
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete the TensorFlow Machine Learning Crash Course
Show steps
Build a stronger foundation in applying TensorFlow to machine learning problems.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Enroll in the TensorFlow Machine Learning Crash Course
- Follow along with the video tutorials
- Complete the hands-on exercises
Solve practice problems on LeetCode
Show steps
Sharpen your problem solving skills and reinforce algorithms and data structures learned in the course.
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Create an account on LeetCode
- Select a topic or skill to practice
- Solve the problems and review solutions
Join a study group for the course
Show steps
Collaborate with peers to discuss concepts, solve problems, and exchange perspectives.
Show steps
Show steps
Show steps
- Reach out to classmates or join an existing study group
- Establish a regular meeting schedule
- Prepare questions and topics for discussion
Create a comprehensive study guide
Show steps
Organize and synthesize course materials for effective revision and exam preparation.
Browse courses on
Study Guide
Show steps
Show steps
Show steps
- Gather notes, assignments, and other relevant materials
- Identify key concepts and summarize them
- Organize the study guide logically
Develop a machine learning model for a real-world problem
Show steps
Apply your knowledge and skills to solve a practical problem using machine learning.
Show steps
Show steps
Show steps
- Identify a suitable problem
- Gather and prepare data
- Train and evaluate machine learning models
- Deploy and monitor the model
Career center
Learners who complete Advanced Data Science Capstone will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists use advanced data science techniques, such as those taught in this course, to solve complex problems and make informed decisions. This course provides a strong foundation in data processing, machine learning, and deep learning, which are essential skills for data scientists. Additionally, the course's emphasis on real-world use cases and project completion will help you develop the practical experience necessary to succeed in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design, develop, and deploy machine learning models. This course provides a comprehensive overview of machine learning concepts and algorithms, as well as hands-on experience with popular machine learning frameworks. The course's focus on model evaluation and tuning will help you build and deploy high-quality machine learning models.
Data Analyst
Data Analyst
Data analysts collect, clean, and analyze data to identify trends and patterns. This course provides a solid foundation in data analysis techniques, including data exploration, visualization, and statistical modeling. The course's emphasis on real-world use cases will help you develop the skills necessary to succeed as a data analyst.
Data Engineer
Data Engineer
Data engineers design and build data pipelines to collect, process, and store data. This course provides a comprehensive overview of data engineering concepts and technologies, as well as hands-on experience with popular data engineering tools. The course's emphasis on scalability and performance will help you build and maintain reliable and efficient data pipelines.
Business Intelligence Analyst
Business Intelligence Analyst
Business intelligence analysts use data to identify opportunities and solve problems for businesses. This course provides a strong foundation in data analysis and visualization techniques, as well as an understanding of business intelligence concepts. The course's emphasis on real-world use cases will help you develop the skills necessary to succeed as a business intelligence analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course provides a comprehensive overview of quantitative analysis techniques, as well as hands-on experience with popular financial modeling tools. The course's emphasis on model evaluation and validation will help you build and deploy robust quantitative models.
Risk Analyst
Risk Analyst
Risk analysts assess and manage risks for businesses and organizations. This course provides a strong foundation in risk management concepts and techniques, as well as an understanding of financial and operational risks. The course's emphasis on real-world use cases will help you develop the skills necessary to succeed as a risk analyst.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess and manage financial risks for insurance companies and other financial institutions. This course provides a comprehensive overview of actuarial science concepts and techniques, as well as hands-on experience with popular actuarial modeling tools. The course's emphasis on model evaluation and validation will help you build and deploy robust actuarial models.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to draw conclusions about the world around us. This course provides a strong foundation in statistical concepts and methods, as well as hands-on experience with popular statistical software packages. The course's emphasis on real-world use cases will help you develop the skills necessary to succeed as a statistician.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use mathematical and statistical models to optimize business processes and operations. This course provides a comprehensive overview of operations research concepts and techniques, as well as hands-on experience with popular operations research software packages. The course's emphasis on real-world use cases will help you develop the skills necessary to succeed as an operations research analyst.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software applications. This course provides a strong foundation in software engineering concepts and technologies, as well as hands-on experience with popular software development tools. The course's emphasis on scalability and performance will help you build and maintain reliable and efficient software applications.
Computer Scientist
Computer Scientist
Computer scientists conduct research on new computer technologies and develop new algorithms and software. This course provides a strong foundation in computer science concepts and technologies, as well as hands-on experience with popular computer science tools. The course's emphasis on research and innovation will help you develop the skills necessary to succeed as a computer scientist.
Data Management Analyst
Data Management Analyst
Data management analysts plan, implement, and maintain data management systems for businesses and organizations. This course provides a comprehensive overview of data management concepts and technologies, as well as hands-on experience with popular data management tools. The course's emphasis on data governance and security will help you build and maintain robust data management systems.
Database Administrator
Database Administrator
Database administrators design, install, and maintain database systems for businesses and organizations. This course provides a comprehensive overview of database concepts and technologies, as well as hands-on experience with popular database management systems. The course's emphasis on performance and scalability will help you build and maintain reliable and efficient database systems.
Information Security Analyst
Information Security Analyst
Information security analysts design, implement, and maintain security systems for businesses and organizations. This course provides a comprehensive overview of information security concepts and technologies, as well as hands-on experience with popular security tools. The course's emphasis on risk management and compliance will help you build and maintain robust security systems.
For more career information including salaries, visit:
OpenCourser.com/course/oryh0h/advanced
Reading list
We've selected 15 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
Advanced Data Science Capstone.
Save
The definitive reference on deep learning, covering the latest research and applications. Useful for advanced learners who want to explore the topic in more depth.
Provides a comprehensive overview of machine learning from a probabilistic perspective. Helpful for learners who want to gain a deep understanding of the mathematical foundations of machine learning.
Save
Provides a comprehensive overview of causal inference in statistics. Helpful for learners who want to learn how to identify and estimate causal effects.
Provides a comprehensive guide to machine learning using Scikit-Learn, Keras, and TensorFlow. Helpful for learners who want to learn how to build and deploy machine learning models.
Provides a comprehensive overview of Bayesian data analysis. Helpful for learners who want to learn how to apply Bayesian methods to real-world problems.
The comprehensive guide to Spark, covering the core concepts, APIs, and applications. Helpful for learners who want to use Spark for large-scale data processing tasks.
Provides a comprehensive overview of econometrics, covering both theory and applications. Helpful for learners who want to learn how to use econometric methods to analyze economic data.
Provides a practical guide to using MapReduce for large-scale text processing tasks. Helpful for learners who want to gain hands-on experience with the technology.
Provides a practical guide to interpretable machine learning models. Helpful for learners who want to understand how to build and interpret models that are explainable to humans.
Provides a hands-on introduction to data science, covering the entire data science pipeline from data collection to model deployment. Helpful for learners who want to gain a comprehensive understanding of the field.
Save
Provides a comprehensive overview of the field of big data, including its history, current applications, and future potential. Helpful for understanding the broader context of the course.
The authoritative guide to Hadoop, covering the architecture, installation, and administration of the platform. Useful as a reference for learners who want to deploy and manage Hadoop clusters.
Provides a practical introduction to machine learning using Python. Helpful for learners who want to gain hands-on experience with machine learning algorithms and techniques.
Provides a gentle introduction to TensorFlow, the popular deep learning library. Helpful for learners who are new to deep learning and want to get started with TensorFlow.
Introduces the fundamental concepts and techniques of data science, with a focus on business applications. Useful as a reference for foundational knowledge.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/oryh0h/advanced
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Level
Advanced
Via
Coursera
Institution
IBM
Instructor
Romeo Kienzler
Language
English
Transcripts
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This course belongs to a
collection
called
Advanced Data Science with IBM. It's comprised of four courses:
- Fundamentals of Scalable Data Science
- Advanced Data Science Capstone (viewing)
- Advanced Machine Learning and Signal Processing
- Applied AI with DeepLearning
Good to know
Teaches fundamental techniques required for more advanced deep learning models and set architectures
Course is hands-on and includes interactive materials
Requires learners to come in with extensive background knowledge
Suitable for those looking to develop professional skills or deep expertise in this topic
Taught by instructors who are recognized for their work in industry
Explores advanced techniques that are highly relevant to industry
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