The University of Adelaide is one of Australia’s leading research-intensive universities and is consistently ranked among the top 1% of universities in the world. Established in 1874, it is Australia’s third oldest university and has a strong reputation for excellence in research and teaching. The University is known for its dedication to the discovery of new knowledge and preparing the educated leaders of tomorrow. It has over 100 Rhodes Scholars, including Australia’s first Indigenous winner, and five Nobel Laureates among its alumni community. Currently, there are more than 25,000 students from over 90 countries.
5-Course Series: Big Data
Self-Paced
About this series:
Big data is changing the way businesses operate. Driven by a new scale of data collection that provides massive levels of information, businesses are now able to analyse and gather data insights to make better-informed decisions.
Data scientists and business analysts are in high-demand as companies look to use data to improve their business operations.
In this Big Data MicroMasters ® Program, you will learn tools and analytical methods to use data for decision-making, collect and organise data at scale, and gain an understanding of how data analysis can help to inform change within organisations.
You’ll develop both the technical and computational skills that are in high demand across a range of industries. You’ll develop critical skills in programming for data science, computational thinking, algorithm design, big data fundamentals, and data-driven analysis, with plenty of opportunities to apply and explore your new learnings through a range of case studies.
Other information:
- Average Length: 6-10 weeks per course
- Effort: 8-10 hours per week, per course
- Number Of Courses: 5 Courses in Program
- Subject: Computer Science, Data Analysis & Statistics
- Institution: University of Adelaide
- Language: English
- Video Transcripts: English
What you'll 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.
Courses in detail:
- Programming for Data Science: Learn how to apply fundamental programming concepts, computational thinking and data analysis techniques to solve real-world data science problems.
- Computational Thinking and Big Data: Learn the core concepts of computational thinking and how to collect, clean and consolidate large-scale datasets.
- Big Data Fundamentals: Learn how big data is driving organisational change and essential analytical tools and techniques, including data mining and PageRank algorithms.
- Big Data Analytics: Learn key technologies and techniques, including R and Apache Spark, to analyse large-scale data sets to uncover valuable business information.
- Big Data Capstone Project: Further develop your knowledge of big data by applying the skills you have learned to a real-world data science project.
Please go to this MicroMasters Program's link on edX for further detailed information: Big Data.
