On successful Completion of the Career Enabler: Big Data Analysis with R & Apache Spark course you will be able to:
Apache Spark a popular cluster computing framework used for performing large scale data analysis
Learn about R a popular statistical programming language with a number of extensions that support data processing and machine learning tasks
Learn about SparkR an R package that provides a light-weight front end to use Apache Spark from R
Learn how SparkR provides a distributed data frame implementation that supports operations like selection, filtering, aggregation etc. (similar to R data frames, dplyr) but on large datasets
Learn how SparkR also supports distributed machine learning using MLlib
Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.
On successful Completion of the Career Enabler: Apache Spark Fundamentals course you will be able to:
the purpose of Spark and understand why and when you would use Spark
how to list and describe the components of the Spark unified stack
the basics of the Resilient Distributed Dataset, Spark's primary data abstraction
Learn how Spark performs at speeds up to 100 times faster than Map Reduce for iterative algorithms or interactive data mining.
Learn how Spark provides in-memory cluster computing for lightning fast speed and supports Java, Python, R, and Scala APIs for ease of development
Learn how Spark can handle a wide range of data processing scenarios by combining SQL, streaming and complex analytics together seamlessly in the same application
Learn how Spark runs on top of Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources such as HDFS, Cassandra, HBase, or S3
how to download and install Spark standalone
an overview of Scala and Python
Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.
Career Enabler: Introduction to Data Science, AI & Machine Learning eLearning
Data Science
£299.99
On successful Completion of the Career Enabler: Introduction to Data Science, AI & Machine Learning course you will be able to:
Translate business questions into Machine Learning problems to understand what your data is telling you
Explore and analyze data from the Web, Word Documents, Email, Twitter feeds, NoSQL stores, Relational Databases and more, for patterns and trends relevant to your business
Build Decision Tree, Logistic Regression and Naïve Bayes classifiers to make predictions about your customers’ future behaviors as well as other business critical events
Use K-Means and Hierarchical Clustering algorithms to more effectively segment your customer market or to discover outliers in your data
Discover hidden customer behaviors from Association Rules and Build Recommendation Engines based on behavioral patterns
Use biologically-inspired Neural Networks to learn from observational data as humans do
Investigate relationships and flows between people, computers and other connected entities using Social Network Analysis
Certification: This course will assist in preparation for the Microsoft, IBM and Oracle Data Science and Machine Learning Certification exams.
This course introduces students to the fundamentals of SQL using Oracle Database 11g database technology. In this course students learn the concepts of relational databases and the powerful SQL programming language. This course provides the essential SQL skills that allow developers to write queries against single and multiple tables, manipulate data in tables, and create database objects.
On successful Completion of the course you will be able to accomplish the following:
Understand Relational Database Modelling
Identify Oracle Database Development Tools
Install your Oracle Software
Understand the Oracle Database 11g Enterprise Edition Options
Understand the Oracle Database Architecture
Identify the major structural components of the Oracle Database 11g
Manage objects with data dictionary views
Manage schema objects
Run data definition language (DDL) statements to create and manage schema objects
Retrieve row and column data from tables with the SELECT statement
Create reports of sorted and restricted data
Display data from multiple tables using the ANSI SQL 99 JOIN syntax
Create reports of aggregated data
Use the SET operators to create subsets of data
Run data manipulation statements (DML) to update data
Employ SQL & PL/SQL functions to generate and retrieve customized data
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