Big Data Emerging Technologies from Coursera

What's inside

Syllabus

Big Data Rankings & Products

The first module “Big Data Rankings & Products” focuses on the relation and market shares of big data hardware, software, and professional services. This information provides an insight to how future industry, products, services, schools, and government organizations will be influenced by big data technology. To have a deeper view into the world’s top big data products line and service types, the lecture provides an overview on the major big data company, which include IBM, SAP, Oracle, HPE, Splunk, Dell, Teradata, Microsoft, Cisco, and AWS. In order to understand the power of big data technology, the difference of big data analysis compared to traditional data analysis is explained. This is followed by a lecture on the 4 V big challenges of big data technology, which deal with issues in the volume, variety, velocity, and veracity of the massive data. Based on this introduction information, big data technology used in adding global insights on investments, help locate new stores and factories, and run real-time recommendation systems by Wal-Mart, Amazon, and Citibank is introduced.

Big Data & Hadoop

The second module “Big Data & Hadoop” focuses on the characteristics and operations of Hadoop, which is the original big data system that was used by Google. The lectures explain the functionality of MapReduce, HDFS (Hadoop Distributed FileSystem), and the processing of data blocks. These functions are executed on a cluster of nodes that are assigned the role of NameNode or DataNodes, where the data processing is conducted by the JobTracker and TaskTrackers, which are explained in the lectures. In addition, the characteristics of metadata types and the differences in the data analysis processes of Hadoop and SQL (Structured Query Language) are explained. Then the Hadoop Release Series is introduced which include the descriptions of Hadoop YARN (Yet Another Resource Negotiator), HDFS Federation, and HDFS HA (High Availability) big data technology.

Spark

The third module “Spark” focuses on the operations and characteristics of Spark, which is currently the most popular big data technology in the world. The lecture first covers the differences in data analysis characteristics of Spark and Hadoop, then goes into the features of Spark big data processing based on the RDD (Resilient Distributed Datasets), Spark Core, Spark SQL, Spark Streaming, MLlib (Machine Learning Library), and GraphX core units. Details of the features of Spark DAG (Directed Acyclic Graph) stages and pipeline processes that are formed based on Spark transformations and actions are explained. Especially, the definition and advantages of lazy transformations and DAG operations are described along with the characteristics of Spark variables and serialization. In addition, the process of Spark cluster operations based on Mesos, Standalone, and YARN are introduced.

Spark ML & Streaming

The fourth module “Spark ML & Streaming” focuses on how Spark ML (Machine Learning) works and how Spark streaming operations are conducted. The Spark ML algorithms include featurization, pipelines, persistence, and utilities which operate on the RDDs (Resilient Distributed Datasets) to extract information form the massive datasets. The lectures explain the characteristics of the DataFrame-based API, which is the primary ML API in the spark.ml package. Spark ML basic statistics algorithms based on correlation and hypothesis testing (P-value) are first introduced followed by the Spark ML classification and regression algorithms based on linear models, naive Bayes, and decision tree techniques. Then the characteristics of Spark streaming, streaming input and output, as well as streaming receiver types (which include basic, custom, and advanced) are explained, followed by how the Spark Streaming process and DStream (Discretized Stream) enable big data streaming operations for real-time and near-real-time applications.

Storm

The fifth module “Storm” focuses on the characteristics and operations of Storm big data systems. The lecture first covers the differences in data analysis characteristics of Storm, Spark, and Hadoop technology. Then the features of Storm big data processing based on the nimbus, spouts, and bolts are described followed by the Storm streams, supervisor, and ZooKeeper details. Further details on Storm reliable and unreliable spouts and bolts are provided followed by the advantages of Storm DAG (Directed Acyclic Graph) and data stream queue management. In addition, the advantages of using Storm based fast real-time applications, which include real-time analytics, online ML (Machine Learning), continuous computation, DRPC (Distributed Remote Procedure Call), and ETL (Extract, Transform, Load) are introduced.

IBM SPSS Statistics Project

The sixth and last module “IBM SPSS Statistics Project” focuses on providing experience on one of the most famous and widely used big data statistical analysis systems in the world. First, the lecture starts with how to setup and use IBM SPSS Statistics, and continues on to describe how IBM SPSS Statistics can be used to gain corporate data analysis experience. Then the data processing statistical results of two projects based on using the IBM SPSS Statistics big data system is conducted. The projects are conducted so the student can discover new ways to use, analyze, and draw charts of the relationship between datasets, and also compare the statistical results using IBM SPSS Statistics.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers the core concepts and tools used in the big data industry

Emphasizes the practical application of big data technologies, making it relevant for professionals in various industries

Taught by experienced instructors with expertise in big data, ensuring high-quality content

The course is project-based, providing hands-on experience in working with big data tools

Leverages industry-leading technologies such as Hadoop, Spark, and IBM SPSS Statistics

Requires a strong foundation in programming and data analysis concepts

Reviews summary

Well-received big data course

Learners largely praise this well-structured course on Big Data concepts and technologies, calling it informative, engaging, and accessible for beginners. They especially appreciate the clear explanations, practical examples, and enthusiastic instructor. However, some mention that the difficulty level can be challenging, and the final project could be improved. Overall, learners highly recommend this course for anyone seeking a comprehensive introduction to Big Data technologies.

Course is packed with up-to-date information on Big Data technologies.

"I have learned so much about the Big Data technologies in this course."

"Gives you a wide perspective regarding the Big Data"

"Amazing introduction course. I want to learn more about this fascinating area."

Course is well-suited for those new to Big Data.

"It is designed well for new learners on this topics. "

"This course gives a very good exposure to basics of Big data."

"The course was a very good intro in Big Data Technologies."

Instructor is knowledgeable, passionate, and engaging.

"Great course and excellent instructor."

"The lecturer explained everthing in details which is more understandable. Thank you I real enjoy the course."

"Professor knows EVERYTHING, nevertheless showing a fine humble attitude during the lectures, providing extensive references for further studies at the end of the videos."

Instructor presents complex Big Data concepts in a way that's easy to understand.

"The lecturer explained everthing in details which is more understandable."

"The course was insightful and well explained even for someone who is is not tech savvy as me."

"Professor knows EVERYTHING, nevertheless showing a fine humble attitude during the lectures, providing extensive references for further studies at the end of the videos."

Final project instructions are unclear and the tool used is only free for a limited time.

"Some quizzes require watching lectures from future weeks, which break the correct pace"

"The final assignment is formulated somewhat ambiguously and feels disconnected from the rest of the course."

"For this reasons, i gave three stars only, even if the course quality and the material is very good, the assignment could be improved a lot and is decreasing my final evaluation."

Course can be demanding at times, requiring dedication and effort.

"It's nice course to big jump into bigdata"

"A lot to learn and remember but a great course overall!"

"The course was insightful and well explained even for someone who is is not tech savvy as me."

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 Emerging Technologies with these activities:

Overview of big data systems

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Review the basic concepts of big data systems to enhance understanding of the course content.

Browse courses on Big Data

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Read the course overview and syllabus.
Watch introductory videos on big data.
Complete practice exercises on big data fundamentals.

Study Group Discussions

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Reinforce your understanding and challenge your perspectives by engaging in discussions with peers.

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Provide feedback and support to your peers.
Join an online study group or create your own.
Discuss course concepts, assignments, and industry trends.

Spark Exploration

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Enhance your understanding of Spark and its applications through guided tutorials.

Browse courses on Spark

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Find online tutorials on Spark basics.
Follow along with the tutorials, completing exercises and assignments.
Apply what you've learned to analyze sample datasets.

Two other activities

Expand to see all activities and additional details

Show all five activities

Big Data Coding Challenges

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Strengthen your coding skills in big data technologies through practice drills.

Browse courses on Spark

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Solve coding problems on platforms like LeetCode or HackerRank.
Participate in online coding competitions.
Implement big data algorithms and techniques in personal projects.

Real-Time Data Analysis Project

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Apply your knowledge to a real-world big data project and build confidence.

Browse courses on Big Data Analytics

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Choose a dataset and define your project goals.
Design and implement your data pipeline using Storm or another technology.
Analyze your data and draw insights.
Present your findings and discuss your approach.

Career center

Learners who complete Big Data Emerging Technologies will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts use advanced analytics tools to collect, clean, and analyze data to identify patterns and trends. They then use this information to make recommendations and solve business problems. Big Data is a rapidly growing field, and Data Analysts with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Data Analyst.

See salaries and explore the career path for Data Analyst

Big Data Architect

Big Data Architects design and implement Big Data solutions. They work with businesses to understand their data needs and then design and build systems to meet those needs. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Big Data Architect.

See salaries and explore the career path for Big Data Architect

Data Scientist

Data Scientists use advanced analytics techniques to solve complex business problems. They work with large datasets to identify patterns and trends, and then develop models to predict future outcomes. Big Data is a rapidly growing field, and Data Scientists with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design and implement machine learning models. They work with businesses to understand their business needs and then develop models to solve those needs. Big Data is a rapidly growing field, and Machine Learning Engineers with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Database Administrator

Database Administrators manage and maintain databases. They ensure that databases are running smoothly and that data is safe and secure. Big Data is a rapidly growing field, and Database Administrators with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Database Administrator.

See salaries and explore the career path for Database Administrator

Software Engineer

Software Engineers design, develop, and maintain software applications. They work with businesses to understand their needs and then develop software solutions to meet those needs. Big Data is a rapidly growing field, and Software Engineers with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Software Engineer.

See salaries and explore the career path for Software Engineer

Business Analyst

Business Analysts work with businesses to understand their needs and then develop solutions to meet those needs. They often use data analysis techniques to identify patterns and trends, and then develop recommendations to improve business performance. Big Data is a rapidly growing field, and Business Analysts with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Business Analyst.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers design and build data pipelines. They work with businesses to understand their data needs and then design and build systems to collect, clean, and process data. Big Data is a rapidly growing field, and Data Engineers with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Data Engineer.

See salaries and explore the career path for Data Engineer

Statistician

Statisticians use statistical methods to analyze data and draw conclusions. They work with businesses to understand their data needs and then develop statistical models to answer questions and make predictions. Big Data is a rapidly growing field, and Statisticians with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Statistician.

See salaries and explore the career path for Statistician

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. They work with businesses to understand their needs and then develop models to optimize operations. Big Data is a rapidly growing field, and Operations Research Analysts with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use financial data to make investment recommendations. They work with businesses to understand their financial needs and then develop models to predict future financial performance. Big Data is a rapidly growing field, and Financial Analysts with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

Marketing Analysts use data to understand customer behavior and develop marketing campaigns. They work with businesses to understand their marketing needs and then develop models to predict customer behavior. Big Data is a rapidly growing field, and Marketing Analysts with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Marketing Analyst.

See salaries and explore the career path for Marketing Analyst

Risk Analyst

Risk Analysts use data to identify and assess risks. They work with businesses to understand their risk needs and then develop models to predict future risks. Big Data is a rapidly growing field, and Risk Analysts with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Risk Analyst.

See salaries and explore the career path for Risk Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze data and make investment recommendations. They work with businesses to understand their investment needs and then develop models to predict future investment performance. Big Data is a rapidly growing field, and Quantitative Analysts with skills in Big Data technologies are in high demand. This course provides a comprehensive overview of Big Data technologies and techniques, making it an ideal choice for those who want to become a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst