Revenues for big data and business analytics is expected to be more than $203 billion by 2020 according to IDC – and this means more jobs in these areas. Large corporations such as Google, Amazon, Microsoft, Facebook and more, are investing heavily in acquiring talent that can turn data sets into valuable technological advancements.
If you have an interest in broadening your skill set as a data scientist or want to enhance your knowledge of big data then The Big Data Bundle educational courses can help you achieve these goals. This affordable bundle will give you 64.5 hours of training in Hadoop, MapReduce, Sparkm and more, to help prepare you for the jobs of the future.
These courses will provide a stronger foundation for your big data processing skills, machine learning, data science, workflow, and more. Here’s a basic description of each course you’ll receive in The Big Data Bundle.
Hive for Big Data Processing
This course will help you connect the dots between SQL and Hive to enhance your big data processing skills, and understand what goes on under the hood of Hive with HDFS and MapReduce, and more.
Learn By Example: Hadoop & MapReduce for Big Data Problems
This course will help you discover mass data processing methods by using the leading data framework. Using Hadoop and MapReduce, you'll learn how to process and manage enormous amounts of data efficiently.
From 0 to 1: Spark for Data Science in Python
This course will make your data fly using Spark for analytics, machine learning and data science. Spark provides you a single engine to explore and work with large amounts of data, run machine learning algorithms, and perform many other functions in a single interactive environment. This course will focus on implementing complex algorithms like PageRank & Music Recommendations, work with a variety of datasets from airline delays to Twitter, web graphs, & product ratings, and more.
Scalable Programming with Scala & Spark
The functional programming nature and the availability of a REPL environment make Scala particularly well suited for a distributed computing framework like Spark. Using these two technologies in tandem can allow you to effectively analyze and explore data in an interactive environment with extremely fast feedback. This course will help you implement complex algorithms, use the different features & libraries of Spark, like RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming, and GraphX, and more.
Learn by Example: HBase – The Hadoop Database
This course will teach you how to create more flexible databases by mastering HBase. By using HBase it allows for an increased level of flexibility, providing column oriented storage, no fixed schema and low latency to accommodate the dynamically changing needs of applications.
Pig for Wrangling Big Data
Think about the last time you saw a completely unorganized spreadsheet. Now imagine that spreadsheet was 100,000 times larger. Mind-boggling, right? That's why there's Pig. Pig works with unstructured data to wrestle it into a more palatable form that can be stored in a data warehouse for reporting and analysis. With the massive sets of disorganized data many companies are working with today, people who can work with Pig are in major demand. By the end of this course, you could qualify as one of those people.
From 0 to 1: The Cassandra Distributed Database
Data sets can outgrow traditional databases, much like children outgrow clothes. Unlike, children's growth patterns, however, massive amounts of data can be extremely unpredictable and unstructured. For big data, the Cassandra distributed database is the solution, using partitioning and replication to ensure that your data is structured and available even when nodes in a cluster go down. Children, you're on your own.
Oozie: Workflow Scheduling for Big Data Systems
Working with big data, obviously, can be a very complex task. That's why it's important to master Oozie. Oozie makes managing a multitude of jobs at different time schedules, and managing entire data pipelines significantly easier if you know the right configurations parameters. This course will teach you how to best determine those parameters, so your workflow will be significantly streamlined.
Flume & Sqoop for Ingesting Big Data
Flume and Sqoop are important elements of the Hadoop ecosystem, transporting data from sources like local file systems to data stores. This is an essential component to organizing and effectively managing big data, making Flume and Sqoop great skills to set you apart from other data analysts. This course will help you efficiently import data to HDFS, HBase and Hive from a variety of sources.
Through The Big Data Bundle you’ll be well on your way to enhancing that resume and it’s available for $45 at the TechCo Shop.
For a more detailed description of each course click here.
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