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Karthik Muthuraman
and
Romeo Kienzler
Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others.
In this short course you'll gain practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression,...
Read more
Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others.
In this short course you'll gain practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression,...
Read more
Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others.
In this short course you'll gain practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering.
The course culminates in a project where you will apply your Spark skills to an ETL for ML workflow use-case.
NOTE: This course requires that you have foundational skills for working with Apache Spark and Jupyter Notebooks. The Introduction to Big Data with Spark and Hadoop course from IBM will equip you with these skills and it is recommended that you have completed that course or similar prior to starting this one.
Register for this course and see more details by visiting:
OpenCourser.com/course/wi74bl/data
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches practical skills in working with Apache Spark for Data Engineering and Machine Learning applications, enabling application of business and technical skills to unstructured data
Develops skills in performing ETL tasks, Regression, Classification, and Clustering using Spark MLlib, Spark Structured Streaming, and more
Includes hands-on labs and interactive materials, providing practical experience in applying Apache Spark skills
Builds on foundational skills in Apache Spark and Jupyter Notebooks, making it suitable for learners with some prior experience
Emphasizes the use of Apache Spark for Big Data applications, which may not be relevant for learners without an interest in Big Data
Requires completion of the Introduction to Big Data with Spark and Hadoop course from IBM, which may pose a barrier to access for learners who have not taken that course
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Reviews summary
Big data exploration using apache spark
This course is part of a Data Engineering certificate from IBM and prepares learners to use Apache Spark for big data exploration. Most learners found this course to be a positive learning experience. However, some students were frustrated with technical difficulties, unclear instructions, and a lack of beginner-friendly content.
This course prepares you to use Spark for Big Data exploration.
"In this short course you'll gain practical skills when you learn how to work with Apache Spark for Data Engineering and Machine Learning (ML) applications."
"You will work hands-on with Spark MLlib, Spark Structured Streaming, and more to perform extract, transform and load (ETL) tasks as well as Regression, Classification, and Clustering."
If you lack experience with Spark or ML, this course may be more challenging than anticipated.
"It offers very little information, The labs are not well explained, this course doesn't add any value for the specialization."
"The course content does not prepare you well for the final project, it can be completed but with a lot of extra outside research, I don't think this is fair as the rest of the courses in the IBM Data Engineering certificate don't really require this"
Be prepared for unclear instructions, especially in the final project.
"The final project instructions are mess. Everything else is as usual in coursera - talk, talk, talk, click, click, click. And the test: apples are oranges or apples are apples, choose your option."
"The final project shouldn't be the place where you see a decision tree."
Avoid this course if you aren't prepared to encounter technical difficulties like buggy labs and unresponsive support.
"Assignments remain offline for more than a week. No refunds offered, no staff responses"
"This course is terrible and plagued with technical issues. The third lab is next to impossible to complete for this reason."
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 Data Engineering and Machine Learning using Spark with these
activities:
Spark Skills for Data Engineering and Machine Learning
Show steps
Practice your Spark skills by completing the Introduction to Big Data with Spark and Hadoop course from IBM to gain a foundational understanding of Spark and Jupyter Notebooks before starting this course.
Browse courses on
Apache Spark
Show steps
-
Review the course description and syllabus for the Introduction to Big Data with Spark and Hadoop course.
-
Complete the hands-on exercises and activities in the course.
-
Review and complete the quizzes and assignments in the course.
Peer Practice Sessions on Spark Skills
Show steps
Engage with peers in practice sessions to discuss Spark concepts, share knowledge, and work through problems together to reinforce your understanding.
Browse courses on
Collaboration
Show steps
-
Find a study partner or group of peers who are also taking this course.
-
Schedule regular practice sessions to discuss course material, complete exercises together, and ask questions.
-
Take turns leading the sessions and presenting your understanding of different concepts.
Guided Tutorials on Spark MLlib, Spark Structured Streaming, and More
Show steps
Supplement your learning by following guided tutorials on Spark MLlib, Spark Structured Streaming, and more to enhance your understanding of these topics as they relate to this course.
Browse courses on
Spark MLlib
Show steps
-
Search for online tutorials on Spark MLlib.
-
Find tutorials that cover concepts relevant to this course, such as Regression, Classification, and Clustering.
-
Complete the tutorials and practice the concepts hands-on.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Drills on ETL for ML Workflow Use-Cases
Show steps
Reinforce your understanding of ETL for ML workflow use-cases by completing practice drills and exercises that simulate real-world scenarios.
Browse courses on
ETL
Show steps
-
Identify online resources or platforms that provide practice drills on ETL for ML.
-
Select practice drills that cover the specific techniques and concepts taught in this course.
-
Complete the practice drills and evaluate your performance.
Create a Visual Representation of Spark Concepts
Show steps
Solidify your understanding of Spark concepts by creating visual representations, such as diagrams, charts, or infographics, that illustrate key concepts and relationships.
Browse courses on
Visualization
Show steps
-
Identify a specific Spark concept or topic that you want to visualize.
-
Choose a visual format that effectively conveys the concept, such as a flowchart, diagram, or infographic.
-
Create the visual representation using appropriate tools or software.
Participate in a Kaggle Competition on Big Data
Show steps
Challenge yourself by participating in a Kaggle competition that involves working with Big Data to solve real-world problems, applying the skills and concepts learned in this course.
Browse courses on
Kaggle
Show steps
-
Identify a Kaggle competition that aligns with your interests and the topics covered in this course.
-
Read the competition description and familiarize yourself with the data and problem statement.
-
Develop a solution using the skills and knowledge gained from this course.
Contribute to an Open-Source Project on Apache Spark
Show steps
Enhance your understanding of Spark and contribute to the community by finding an open-source project related to Spark and making contributions, such as reporting bugs, suggesting improvements, or writing documentation.
Browse courses on
Open Source
Show steps
-
Identify an open-source project on GitHub or other platforms that is related to Apache Spark.
-
Review the project's documentation and codebase to understand its functionality.
-
Identify areas where you can contribute, such as fixing bugs, adding features, or improving documentation.
Spark Skills for Data Engineering and Machine Learning
Show steps
Practice your Spark skills by completing the Introduction to Big Data with Spark and Hadoop course from IBM to gain a foundational understanding of Spark and Jupyter Notebooks before starting this course.
Browse courses on
Apache Spark
Show steps
Show steps
Show steps
- Review the course description and syllabus for the Introduction to Big Data with Spark and Hadoop course.
- Complete the hands-on exercises and activities in the course.
- Review and complete the quizzes and assignments in the course.
Peer Practice Sessions on Spark Skills
Show steps
Engage with peers in practice sessions to discuss Spark concepts, share knowledge, and work through problems together to reinforce your understanding.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Find a study partner or group of peers who are also taking this course.
- Schedule regular practice sessions to discuss course material, complete exercises together, and ask questions.
- Take turns leading the sessions and presenting your understanding of different concepts.
Guided Tutorials on Spark MLlib, Spark Structured Streaming, and More
Show steps
Supplement your learning by following guided tutorials on Spark MLlib, Spark Structured Streaming, and more to enhance your understanding of these topics as they relate to this course.
Browse courses on
Spark MLlib
Show steps
Show steps
Show steps
- Search for online tutorials on Spark MLlib.
- Find tutorials that cover concepts relevant to this course, such as Regression, Classification, and Clustering.
- Complete the tutorials and practice the concepts hands-on.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Drills on ETL for ML Workflow Use-Cases
Show steps
Reinforce your understanding of ETL for ML workflow use-cases by completing practice drills and exercises that simulate real-world scenarios.
Browse courses on
ETL
Show steps
Show steps
Show steps
- Identify online resources or platforms that provide practice drills on ETL for ML.
- Select practice drills that cover the specific techniques and concepts taught in this course.
- Complete the practice drills and evaluate your performance.
Create a Visual Representation of Spark Concepts
Show steps
Solidify your understanding of Spark concepts by creating visual representations, such as diagrams, charts, or infographics, that illustrate key concepts and relationships.
Browse courses on
Visualization
Show steps
Show steps
Show steps
- Identify a specific Spark concept or topic that you want to visualize.
- Choose a visual format that effectively conveys the concept, such as a flowchart, diagram, or infographic.
- Create the visual representation using appropriate tools or software.
Participate in a Kaggle Competition on Big Data
Show steps
Challenge yourself by participating in a Kaggle competition that involves working with Big Data to solve real-world problems, applying the skills and concepts learned in this course.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Identify a Kaggle competition that aligns with your interests and the topics covered in this course.
- Read the competition description and familiarize yourself with the data and problem statement.
- Develop a solution using the skills and knowledge gained from this course.
Contribute to an Open-Source Project on Apache Spark
Show steps
Enhance your understanding of Spark and contribute to the community by finding an open-source project related to Spark and making contributions, such as reporting bugs, suggesting improvements, or writing documentation.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an open-source project on GitHub or other platforms that is related to Apache Spark.
- Review the project's documentation and codebase to understand its functionality.
- Identify areas where you can contribute, such as fixing bugs, adding features, or improving documentation.
Career center
Learners who complete Data Engineering and Machine Learning using Spark will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models to solve business problems. This course can help you become a Machine Learning Engineer by providing hands-on experience with Apache Spark MLlib, Spark Structured Streaming, and more. You will learn how to perform Regression, Classification, and Clustering, which are essential for developing machine learning models.
Data Engineer
Data Engineer
A Data Engineer designs and builds data pipelines to transport data between various systems and applications, ensuring data quality and security. This course can help you become a Data Engineer by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, which are essential for building data pipelines.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. This course can help you become a Data Scientist by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for data science.
Big Data Analyst
Big Data Analyst
A Big Data Analyst analyzes large datasets to identify trends and patterns. This course can help you become a Big Data Analyst by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for analyzing big data.
Data Architect
Data Architect
A Data Architect designs and builds data architectures for organizations. This course can help you become a Data Architect by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for designing and building data architectures.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course can help you become a Software Engineer by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for developing software applications.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes to identify opportunities for improvement. This course can help you become a Business Analyst by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for analyzing business processes.
Project Manager
Project Manager
A Project Manager manages projects from start to finish. This course can help you become a Project Manager by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for managing projects.
Data Visualization Specialist
Data Visualization Specialist
A Data Visualization Specialist creates visual representations of data to communicate insights to stakeholders. This course can help you become a Data Visualization Specialist by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for creating data visualizations.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course can help you become a Database Administrator by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for managing databases.
Cloud Architect
Cloud Architect
A Cloud Architect designs and builds cloud-based solutions. This course can help you become a Cloud Architect by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for designing and building cloud-based solutions.
Data Security Analyst
Data Security Analyst
A Data Security Analyst protects data from unauthorized access, use, disclosure, disruption, modification, or destruction. This course can help you become a Data Security Analyst by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for protecting data.
Statistician
Statistician
A Statistician uses statistical methods to collect, analyze, interpret, and present data. This course can help you become a Statistician by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for statistical analysis.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical methods to improve the efficiency of business processes. This course can help you become an Operations Research Analyst by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for improving the efficiency of business processes.
Actuary
Actuary
An Actuary uses mathematical and statistical methods to assess risk and uncertainty. This course may be helpful for you if you want to become an Actuary by providing hands-on experience with Apache Spark for Data Engineering and Machine Learning (ML) applications. You will learn how to perform extract, transform and load (ETL) tasks, as well as Regression, Classification, and Clustering, which are essential for assessing risk and uncertainty.
For more career information including salaries, visit:
OpenCourser.com/course/wi74bl/data
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
Data Engineering and Machine Learning using Spark.
Comprehensive guide to Spark, covering everything from basic concepts to advanced topics. It valuable resource for anyone who wants to learn more about Spark and how to use it effectively.
Save
Provides a comprehensive overview of Apache Spark and its core components, including Spark SQL, Spark Streaming, and Spark MLlib.
Provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. This book valuable resource for anyone who wants to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers topics such as Bayesian inference, supervised learning, and unsupervised learning. This book valuable resource for anyone who wants to learn more about the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. This book valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of data science for business. It covers topics such as data collection, data cleaning, data analysis, and data visualization. This book valuable resource for anyone who wants to learn more about data science.
Save
Provides a practical guide to data science. It covers topics such as data collection, data cleaning, data analysis, and data visualization. This book valuable resource for anyone who wants to learn more about data science.
Provides a comprehensive overview of machine learning with Python. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. This book valuable resource for anyone who wants to learn more about machine learning.
Save
Provides a comprehensive overview of deep learning with R. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. This book valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of data visualization with Python. It covers topics such as data exploration, data visualization, and data storytelling. This book valuable resource for anyone who wants to learn more about data visualization.
Provides a comprehensive overview of Python for data analysis. It covers topics such as data loading, data cleaning, data manipulation, and data visualization. This book valuable resource for anyone who wants to learn more about Python for data analysis.
The official guide to Apache Spark, written by its creators.
Covers advanced topics in Spark, such as graph processing, machine learning, and stream processing.
Focuses on using Spark with R, a popular programming language for data science.
Provides a comprehensive overview of data engineering using Python, including data pipelines and data quality.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wi74bl/data
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Time
8 hours
Via
Coursera
Institutions
IBM
IBM Skills Network
Instructors
Karthik Muthuraman
Romeo Kienzler
This course belongs to a
collection
called
IBM Data Engineering. It's comprised of 13 courses:
- Relational Database Administration (DBA)
- Introduction to Data Engineering
- Python Project for Data Engineering
- Data Engineering and Machine Learning using Spark (viewing)
- Hands-on Introduction to Linux Commands and Shell Scripting
- Introduction to NoSQL Databases
- Introduction to Relational Databases (RDBMS)
- Data Engineering Capstone Project
- Getting Started with Data Warehousing and BI Analytics
- ETL and Data Pipelines with Shell, Airflow and Kafka
- Introduction to Big Data with Spark and Hadoop
- Databases and SQL for Data Science with Python
- Python for Data Science, AI & Development
Good to know
Teaches practical skills in working with Apache Spark for Data Engineering and Machine Learning applications, enabling application of business and technical skills to unstructured data
Develops skills in performing ETL tasks, Regression, Classification, and Clustering using Spark MLlib, Spark Structured Streaming, and more
Includes hands-on labs and interactive materials, providing practical experience in applying Apache Spark skills
Builds on foundational skills in Apache Spark and Jupyter Notebooks, making it suitable for learners with some prior experience
Emphasizes the use of Apache Spark for Big Data applications, which may not be relevant for learners without an interest in Big Data
Requires completion of the Introduction to Big Data with Spark and Hadoop course from IBM, which may pose a barrier to access for learners who have not taken that course
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