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Yoav Freund
In data science, data is called "big" if it cannot fit into the memory of a single standard laptop or workstation.
The analysis of big datasets requires using a cluster of tens, hundreds or thousands of computers. Effectively using such clusters requires the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and corresponding computational models, such as Hadoop, MapReduce and Spark.
In this course, part of the Data Science MicroMasters program, you will learn what the bottlenecks are in massive parallel computation and how to use spark to minimize these bottlenecks.
Read more
In data science, data is called "big" if it cannot fit into the memory of a single standard laptop or workstation.
The analysis of big datasets requires using a cluster of tens, hundreds or thousands of computers. Effectively using such clusters requires the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and corresponding computational models, such as Hadoop, MapReduce and Spark.
In this course, part of the Data Science MicroMasters program, you will learn what the bottlenecks are in massive parallel computation and how to use spark to minimize these bottlenecks.
Read more
In data science, data is called "big" if it cannot fit into the memory of a single standard laptop or workstation.
The analysis of big datasets requires using a cluster of tens, hundreds or thousands of computers. Effectively using such clusters requires the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and corresponding computational models, such as Hadoop, MapReduce and Spark.
In this course, part of the Data Science MicroMasters program, you will learn what the bottlenecks are in massive parallel computation and how to use spark to minimize these bottlenecks.
You will learn how to perform supervised an unsupervised machine learning on massive datasets using the Machine Learning Library (MLlib).
In this course, as in the other ones in this MicroMasters program, you will gain hands-on experience using PySpark within the Jupyter notebooks environment.
Register for this course and see more details by visiting:
OpenCourser.com/course/h1b1ya/big
What's inside
Learning objectives
- Programming spark using pyspark
- Identifying the computational tradeoffs in a spark application
- Performing data loading and cleaning using spark and parquet
- Modeling data through statistical and machine learning methods
- Programming spark using pyspark
- Identifying the computational tradeoffs in a spark application
- Performing data loading and cleaning using spark and parquet
- Modeling data through statistical and machine learning methods
Traffic lights
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Read about what's good
what should give you pause and
possible dealbreakers
Provides a strong foundation for learners interested in data science
Taught by Yoav Freund, recognized expert in machine learning and data science
Leverages PySpark, a powerful library for data analysis and machine learning
Provides hands-on experience through Jupyter notebooks, an industry-standard environment for data science
May require prior programming experience or willingness to learn
Assumes familiarity with basic statistics and linear algebra
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Reviews summary
Practical big data analytics with spark
According to learners, this course offers a strong foundation in Big Data Analytics using Spark, particularly excelling in hands-on PySpark application within Jupyter notebooks. Many find the content comprehensive and practical, providing invaluable experience for real-world scenarios. Students appreciate the clear explanations and well-designed projects, which make the challenging topics of distributed computing and machine learning with MLlib accessible. While some older feedback mentioned outdated content or setup difficulties, recent reviews suggest the course continues to be a highly relevant and rewarding experience for professionals seeking to upskill in Spark.
Explanations are generally clear, though some find it academic.
"The instructor explanations were clear and easy to follow, even for complex topics."
"Sometimes the explanations felt a bit too academic for direct practical application, but the demos compensated."
"I found the instructors knowledgeable, and their delivery made the material digestible."
Delivers a solid and thorough understanding of Spark concepts.
"Excellent course overall, providing a solid foundation in big data and Spark. The content is comprehensive."
"Fantastic introduction to Spark and big data concepts. I gained a strong understanding of distributed computing."
"This course really helped solidify my understanding of the core principles of Spark and MLlib."
The course provides extensive practical experience with PySpark.
"I truly enjoyed the practical approach to PySpark. The Jupyter notebooks were great and helped solidify the concepts."
"The hands-on coding was plentiful and challenging in a good way. It really helped me apply Spark in my job."
"The practical exercises are invaluable. I particularly appreciated the focus on real-world scenarios through the labs."
Best for those with some background; can be challenging.
"Not for beginners; it assumes some prior knowledge of Python and data concepts. MLlib section was challenging."
"I found some of the earlier assignments a bit too easy, but the course picked up pace and difficulty later on."
"Perfect for professionals looking to upskill. It challenges you in a good way to grasp advanced concepts."
Older reviews note some outdated elements; newer reviews less so.
"Decent course, but felt a bit outdated in parts. Some libraries or approaches were not the absolute latest."
"I struggled with some setup instructions, which seemed to refer to older versions of software. Needs a major update."
"While generally good, I wish there was more focus on the most current optimization techniques in Spark."
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 Big Data Analytics Using Spark with these
activities:
Review Spark: The Definitive Guide
Show steps
Spark: The Definitive Guide will provide additional in-depth knowledge on spark and can help reinforce key concepts in this course
View
Spark: The Definitive Guide: Big Data...
on Amazon
Show steps
-
Read Chapters 1-3
Review spark memory management
Show steps
Reviewing spark memory management will increase retention of knowledge and reinforce concepts learned in this course.
Browse courses on
Big Data
Show steps
-
Read Apache Spark documentation on Memory Management
Create a Spark Application
Show steps
Creating a spark application will put into practice the concepts learned in this course and help to improve practical understanding and skills.
Show steps
-
Plan and design a spark application
-
Implement the spark application in pyspark
One other activity
Expand to see all activities and additional details
Show all four activities
Volunteer on a Spark Project
Show steps
Volunteering on a spark project will provide hands-on experience and opportunities to contribute to the spark community while reinforcing concepts learned in this course.
Show steps
-
Find a volunteer opportunity through spark meetups or online platforms
-
Contribute to spark projects or initiatives.
Review Spark: The Definitive Guide
Show steps
Spark: The Definitive Guide will provide additional in-depth knowledge on spark and can help reinforce key concepts in this course
View
Spark: The Definitive Guide: Big Data...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3
Review spark memory management
Show steps
Reviewing spark memory management will increase retention of knowledge and reinforce concepts learned in this course.
Browse courses on
Big Data
Show steps
Show steps
Show steps
- Read Apache Spark documentation on Memory Management
Create a Spark Application
Show steps
Creating a spark application will put into practice the concepts learned in this course and help to improve practical understanding and skills.
Show steps
Show steps
Show steps
- Plan and design a spark application
- Implement the spark application in pyspark
One other activity
Expand to see all activities and additional details
Show all four activities
Volunteer on a Spark Project
Show steps
Volunteering on a spark project will provide hands-on experience and opportunities to contribute to the spark community while reinforcing concepts learned in this course.
Show steps
Show steps
Show steps
- Find a volunteer opportunity through spark meetups or online platforms
- Contribute to spark projects or initiatives.
Career center
Learners who complete Big Data Analytics Using Spark will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst collects, cleans, and analyzes data to identify trends and patterns. This course can be a perfect starting point for one who wishes to be a Data Analyst by providing the necessary skills to acquire, process, and analyze big datasets. Through the course, you will learn how to use Spark and Pyspark, a popular tool for working with big data. In addition, you will learn how to use statistical and machine learning algorithms to model data and identify patterns, which are essential skills for a Data Analyst.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines. This course can be very helpful for a Data Engineer who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to load, clean, and transform data. In addition, you will learn how to use statistical and machine learning algorithms to model data, which can be useful for building data pipelines for machine learning applications.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, builds, and deploys machine learning models. This course can be helpful for a Machine Learning Engineer who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to train and deploy machine learning models. In addition, you will learn how to use statistical and machine learning algorithms to model data, which is essential for building machine learning models.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. This course can be helpful for a Data Scientist who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to acquire, process, and analyze big datasets. In addition, you will learn how to use statistical and machine learning algorithms to model data, which is essential for solving business problems using data.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be helpful for a Software Engineer who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to develop software applications. In addition, you will learn how to use statistical and machine learning algorithms to model data, which can be useful for building data-driven software applications.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical methods to analyze data and make predictions. This course can be helpful for a Quantitative Analyst who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to analyze big datasets. In addition, you will learn how to use statistical and machine learning algorithms to model data and make predictions.
Business Analyst
Business Analyst
A Business Analyst uses data to analyze business problems and make recommendations. This course can be helpful for a Business Analyst who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to acquire, process, and analyze big datasets. In addition, you will learn how to use statistical and machine learning algorithms to model data, which can be useful for building data-driven business recommendations.
Data Architect
Data Architect
A Data Architect designs and builds data systems. This course can be helpful for a Data Architect who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to design and build data systems. In addition, you will learn how to use statistical and machine learning algorithms to model data, which can be useful for building scalable and efficient data systems.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. This course may be helpful for a Statistician who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to analyze big datasets. In addition, you will learn how to use statistical and machine learning algorithms to model data, which is essential for drawing meaningful conclusions from data.
Database Administrator
Database Administrator
A Database Administrator designs, builds, and maintains databases. This course can be helpful for a Database Administrator who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to design and build databases. In addition, you will learn how to use statistical and machine learning algorithms to model data, which can be useful for building scalable and efficient databases.
Data Researcher
Data Researcher
A Data Researcher uses data to conduct research and develop new knowledge. This course can be helpful for a Data Researcher who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to conduct research. In addition, you will learn how to use statistical and machine learning algorithms to model data, which can be useful for drawing meaningful conclusions from data.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. This course may be helpful for a Financial Analyst who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to analyze financial data. In addition, you will learn how to use statistical and machine learning algorithms to model data, which can be useful for making informed investment decisions.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to analyze and solve business problems. This course may be helpful for an Operations Research Analyst who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to analyze business data. In addition, you will learn how to use statistical and machine learning algorithms to model data, which can be useful for building data-driven solutions to business problems.
Risk Analyst
Risk Analyst
A Risk Analyst uses data to analyze and manage risk. This course may be helpful for a Risk Analyst who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to analyze risk data. In addition, you will learn how to use statistical and machine learning algorithms to model data, which can be useful for building risk models.
Marketing Analyst
Marketing Analyst
A Marketing Analyst uses data to analyze marketing campaigns and make recommendations. This course may be helpful for a Marketing Analyst who wants to work with big data. The course will teach you how to use Spark, a popular tool for working with big data, and how to use it to analyze marketing data. In addition, you will learn how to use statistical and machine learning algorithms to model data, which can be useful for building data-driven marketing campaigns.
For more career information including salaries, visit:
OpenCourser.com/course/h1b1ya/big
Reading list
We've selected ten 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
Big Data Analytics Using Spark.
Is the definitive guide to Apache Spark. It covers everything from the basics of Spark to advanced topics such as machine learning and graph processing. It must-read for anyone who wants to use Spark for big data analysis.
Save
Provides a comprehensive overview of Apache Spark, including its architecture, programming model, and use cases. It valuable resource for anyone who wants to learn more about Spark and how to use it for big data analysis.
Provides a comprehensive overview of reinforcement learning, including its challenges and opportunities. It also covers best practices for designing and implementing reinforcement learning solutions.
Provides a practical introduction to machine learning with Apache Spark. It covers the basics of machine learning, and it shows how to use Spark to build and train machine learning models.
Save
Provides a comprehensive overview of big data analytics, including its challenges and opportunities. It also covers best practices for designing and implementing big data analytics solutions.
Save
Provides a comprehensive overview of data mining, including its challenges and opportunities. It also covers best practices for designing and implementing data mining solutions.
Save
Provides a comprehensive overview of deep learning, including its challenges and opportunities. It also covers best practices for designing and implementing deep learning solutions.
Provides a practical introduction to deep learning with Python. It covers the basics of deep learning, and it shows how to use Python to build and train deep learning models.
Provides a practical introduction to data science for business professionals. It covers the basics of data mining and data-analytic thinking, and it shows how to use these techniques to solve business problems.
Save
Provides a practical introduction to predictive analytics. It covers the basics of predictive analytics, and it shows how to use predictive analytics to solve business problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/h1b1ya/big
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Rating
4.3
Effort
9 to 12 hours per week for 10 weeks
Level
Advanced
Via
edX
Institution
The University of California, San Diego
Instructor
Yoav Freund
Language
English
Transcript
English
This course belongs to a
collection
called
Data Science. It's comprised of five courses:
- Probability and Statistics in Data Science using Python
- Machine Learning Fundamentals
- Big Data Analytics Using Spark (viewing)
- Python for Data Science, AI & Development
- Python for Data Science
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