Learn By Example: Hadoop MapReduce for Big Data problems

Course Overview

This course, Learn By Example: Hadoop MapReduce for Big Data problems, is designed to help students develop advanced MapReduce applications to process BigData. It will teach them the art of "thinking parallel" - how to break up a task into Map/Reduce transformations, and how to self-sufficiently set up their own mini-Hadoop cluster whether it's a single node, a physical cluster or in the cloud. Students will also learn how to use Hadoop + MapReduce to solve a wide variety of problems, from NLP to Inverted Indices to Recommendations. They will understand HDFS, MapReduce and YARN and how they interact with each other, as well as the basics of performance tuning and managing their own cluster.

The course is taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.

This course is a zoom-in, zoom-out, hands-on workout involving Hadoop, MapReduce and the art of thinking parallel. It covers the individual components of Hadoop in great detail, and also gives students a higher level picture of how they interact with each other. Students will learn how to set up their own cluster using both VMs and the Cloud, and all the major features of MapReduce are covered - including advanced topics like Total Sort and Secondary Sort. Examples in this course will train students to "think parallel" and use MapReduce to recommend friends in a Social Networking site, generate Top 10 friend recommendations using a Collaborative filtering algorithm, build an Inverted Index for Search Engines, generate Bigrams from text, chain multiple MR jobs together, write their own Customized Partitioner, and more. They will also learn how to install Hadoop in Standalone, Pseudo-Distributed and Fully Distributed modes, setup a hadoop cluster using Linux VMs, set up a cloud Hadoop cluster on AWS with Cloudera Manager, understand HDFS, MapReduce and YARN and their interaction, customize their MapReduce Jobs, and more.

[Applications]
Upon completion of this course, learners will be able to apply their knowledge of Hadoop MapReduce to solve Big Data problems. They will be able to set up their own mini-Hadoop cluster, understand HDFS, MapReduce and YARN, and customize their MapReduce jobs. Learners will also be able to use Hadoop + MapReduce to solve a wide variety of problems, such as recommending friends in a social networking site, generating bigrams from text, and building an inverted index for search engines.

[Career Paths]
Recommended career paths for learners of this course include:

1. Big Data Engineer: Big Data Engineers are responsible for designing, developing, and maintaining large-scale data processing systems. They must be able to work with a variety of data sources and technologies, such as Hadoop, MapReduce, and Spark. They must also be able to develop algorithms and models to analyze and interpret data. This role is expected to grow in demand as more organizations look to leverage the power of Big Data.

2. Data Scientist: Data Scientists are responsible for analyzing large amounts of data and developing insights from it. They must be able to use a variety of tools and techniques to uncover patterns and trends in data. They must also be able to communicate their findings to stakeholders in a clear and concise manner. This role is expected to grow in demand as organizations look to gain a better understanding of their data.

3. Data Analyst: Data Analysts are responsible for collecting, organizing, and analyzing data. They must be able to use a variety of tools and techniques to uncover patterns and trends in data. They must also be able to communicate their findings to stakeholders in a clear and concise manner. This role is expected to grow in demand as organizations look to gain a better understanding of their data.

4. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They must be able to use a variety of tools and techniques to develop and optimize models. They must also be able to communicate their findings to stakeholders in a clear and concise manner. This role is expected to grow in demand as organizations look to leverage the power of machine learning.

[Education Paths]
Recommended Degree Paths:

1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, algorithms, data structures, operating systems, and software engineering. It also covers topics such as artificial intelligence, machine learning, and big data analytics. This degree path is ideal for those interested in developing and deploying applications that leverage Hadoop MapReduce for Big Data problems.

2. Master of Science in Data Science: This degree path provides students with a deep understanding of data science principles and techniques, including data mining, machine learning, and predictive analytics. It also covers topics such as data visualization, data engineering, and distributed computing. This degree path is ideal for those interested in leveraging Hadoop MapReduce for Big Data problems.

3. Master of Science in Artificial Intelligence: This degree path provides students with a comprehensive understanding of artificial intelligence principles and techniques, including natural language processing, computer vision, and robotics. It also covers topics such as machine learning, deep learning, and distributed computing. This degree path is ideal for those interested in developing and deploying applications that leverage Hadoop MapReduce for Big Data problems.

4. Master of Science in Business Analytics: This degree path provides students with a comprehensive understanding of business analytics principles and techniques, including data mining, predictive analytics, and optimization. It also covers topics such as data visualization, data engineering, and distributed computing. This degree path is ideal for those interested in leveraging Hadoop MapReduce for Big Data problems.

Developing Trends:

1. Bachelor of Science in Computer Science: The demand for computer science professionals is increasing as businesses and organizations become more reliant on technology. As such, there is a growing need for professionals with expertise in Hadoop MapReduce for Big Data problems.

2. Master of Science in Data Science: Data science is becoming increasingly important as businesses and organizations look to leverage data to gain insights and make better decisions. As such, there is a growing need for professionals with expertise in Hadoop MapReduce for Big Data problems.

3. Master of Science in Artificial Intelligence: Artificial intelligence is becoming increasingly important as businesses and organizations look to leverage AI to automate processes and gain insights. As such, there is a growing need for professionals with expertise in Hadoop MapReduce for Big Data problems.

4. Master of Science in Business Analytics: Business analytics is becoming increasingly important as businesses and organizations look to leverage data to gain insights and make better decisions. As such, there is a growing need for professionals with expertise in Hadoop MapReduce for Big Data problems.