Go Big with these Big Data Courses

Organizations now have access to massive amounts of data and it's influencing the way they operate. They are realizing in order to be successful they must leverage their data to make effective business decisions.

In this course, part of the Big Data MicroMasters program, you will learn how big data is driving organizational change and the key challenges organizations face when trying to analyze massive data sets.

You will learn fundamental techniques, such as data mining and stream processing. You will also learn how to design and implement PageRank algorithms using MapReduce, a programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. You will learn how big data has improved web search and how online advertising systems work.

What you'll learn

Knowledge and application of MapReduce
Understanding the rate of occurrences of events in big data
How to design algorithms for stream processing and counting of frequent elements in Big Data
Understand and design PageRank algorithms
Understand underlying random walk algorithms

Big Data Analytics

Learn key technologies and techniques, including R and Apache Spark, to analyze large-scale data sets to uncover valuable business information.

University of Adelaide

About this course

Gain essential skills in today's digital age to store, process and analyze data to inform business decisions.

In this course, part of the Big Data MicroMasters program, you will develop your knowledge of big data analytics and enhance your programming and mathematical skills. You will learn to use essential analytic tools such as Apache Spark and R.

What you'll learn

How to develop algorithms for the statistical analysis of big data;
Knowledge of big data applications;
How to use fundamental principles used in predictive analytics;
Evaluate and apply appropriate principles, techniques, and theories to large-scale data science problems.

IoT Programming and Big Data

Learn how to apply software solutions for different systems and Big Data needs to your IoT designs.

Curtin University

About this course

The Internet of Things is creating massive quantities of data, and managing and analyzing it requires a unique approach to programming and statistics for distributed data sources.

This course will teach introductory programming concepts that allow connection to, and implementation of some functionality on, IoT devices, using the Python programming language. In addition, students will learn how to use Python to process text log files, such as those generated automatically by IoT sensors and other network-connected systems.

Learners do not need prior programming experience to undertake this course, and will not learn a specific programming language - however, Python will be used for demonstrations. This course will focus on learning by working through realistic examples.

What you'll learn

Appreciate the software needs of an IoT project
Understand how data is managed in an IoT network
Apply software solutions for different systems and Big Data to your IoT concept designs
Create Python scripts to manage large data files collected from sensor data and interact with the real world via actuators and other output devices.

MicroMasters Program in Big Data

University of Adelaide

What you will learn

How to design algorithms
Understand fundamental programming concepts including data abstraction, storage, and structures
Understand computational thinking which includes decomposition, pattern recognition, and abstraction
Data-driven problem and algorithm design for big data
Interpretation of data representation and analysis
Understand key mathematical concepts, including dimension reduction and Bayesian models
How to use analytical tools such as R and Java

Big Data Analytics in Healthcare

Gain hands-on experience with scalable machine learning algorithms, big data systems, and healthcare data analytics applications.

The Georgia Institute of Technology

About this course

Data science plays an important role in many industries. In facing massive amounts of heterogeneous data, scalable machine learning and data mining algorithms and systems have become extremely important for data scientists. The growth of volume, complexity, and speed in data drives the need for scalable data analytic algorithms and systems.

What you'll learn

Understand health data and big data analytic technology;
Health data standards;
Scalable machine learning algorithms such as online learning and fast similarity search;
Big data analytics systems such as Hadoop family (Hive, Pig, HBase), Spark and Graph DB;
Deep learning models and packages such as TensorFlow.

Source: HOB

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