Go Big with these Big Data Courses

By ridhigrg |Email | Dec 24, 2019 | 4068 Views

Big Data Fundamentals
Learn how big data is driving organizational change and essential analytical tools and techniques, including data mining and PageRank algorithms.
University of Adelaide

About this course
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

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