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.
Courses under this program: Course 1: Programming for Data Science
Learn how to apply fundamental programming concepts, computational thinking and data analysis techniques to solve real-world data science problems.
Course 2: Computational Thinking and Big Data
Learn the core concepts of computational thinking and how to collect, clean and consolidate large-scale datasets.
Course 3: Big Data Fundamentals
Learn how big data is driving organisational change and essential analytical tools and techniques, including data mining and PageRank algorithms.
Course 4: Big Data Analytics
Learn key technologies and techniques, including R and Apache Spark, to analyse large-scale data sets to uncover valuable business information.
Course 5: 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.
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 organisational change and the key challenges organizations face when trying to analyse 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.
By the end of this course, you will have a better understanding of the various applications of big data methods in industry and research.
Gain essential skills in today’s digital age to store, process and analyse 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.
Topics covered in this course include:
cloud-based big data analysis;
predictive analytics, including probabilistic and statistical models;
application of large-scale data analysis;
analysis of problem space and data needs.
By the end of this course, you will be able to approach large-scale data science problems with creativity and initiative.
The Big Data Capstone Project will allow you to apply the techniques and theory you have gained from the four courses in this Big Data MicroMasters program to a medium-scale data science project.
Working with organisations and stakeholders of your choice on a real-world dataset, you will further develop your data science skills and knowledge.
This project will give you the opportunity to deepen your learning by giving you valuable experience in evaluating, selecting and applying relevant data science techniques, principles and theory to a data science problem.
This project will see you plan and execute a reasonably substantial project and demonstrate autonomy, initiative and accountability.
You’ll deepen your learning of social and ethical concerns in relation to data science, including an analysis of ethical concerns and ethical frameworks in relation to data selection and data management.
By communicating the knowledge, skills and ideas you have gained to other learners through online collaborative technologies, you will learn valuable communication skills, important for any career. You’ll also deliver a written presentation of your project design, plan, methodologies, and outcomes.
Computational thinking is an invaluable skill that can be used across every industry, as it allows you to formulate a problem and express a solution in such a way that a computer can effectively carry it out.
In this course, part of the Big Data MicroMasters program, you will learn how to apply computational thinking in data science. You will learn core computational thinking concepts including decomposition, pattern recognition, abstraction, and algorithmic thinking.
You will also learn about data representation and analysis and the processes of cleaning, presenting, and visualizing data. You will develop skills in data-driven problem design and algorithms for big data.
The course will also explain mathematical representations, probabilistic and statistical models, dimension reduction and Bayesian models.
You will use tools such as R and Java data processing libraries in associated language environments.
There is a rising demand for people with the skills to work with Big Data sets and this course can start you on your journey through our Big Data MicroMasters program towards a recognised credential in this highly competitive area.
Using practical activities you will learn how digital technologies work and will develop your coding skills through engaging and collaborative assignments.
You will learn algorithm design as well as fundamental programming concepts such as data selection, iteration and functional decomposition, data abstraction and organisation. In addition to this you will learn how to perform simple data visualisations using Processing and embed your learning using problem-based assignments.
This course will test your knowledge and skills in solving small-scale data science problems working with real-world datasets and develop your understanding of big data in the world around you.
Aneta Neumann, Katrina Falkner, Claudia Szabo, Nick Falkner, David Suter, Lewis Mitchell, Simon Tuke, Frank Neumann, Gary Glonek, Lingqiao Liu, Gavin Meredith, Ian Knight, Markus Wagner, Wanru (Kelly) Gao and Vahid Roostapour