Master Big Data with UCSD and Coursera



E.g., 2016-07-27
E.g., 2016-07-27
E.g., 2016-07-27
Oct 3rd 2016

Learn how to apply selected mathematical modelling methods to analyse big data in this free online course. Have you ever wondered how mathematics can be used to solve big data problems? This course will show you how. Mathematics is everywhere, and with the rise of big data it becomes a useful tool when extracting information and analysing large datasets.

Average: 1.5 (2 votes)
Sep 5th 2016

Learn how to apply selected statistical and machine learning techniques and tools to analyse big data. Everyone has heard of big data. Many people have big data. But only some people know what to do with big data when they have it. So what’s the problem? Well, the big problem is that the data is big—the size, complexity and diversity of datasets increases every day. This means that we need new technological or methodological solutions for analysing data. There is a great demand for people with the skills and know-how to do big data analytics.

Average: 6 (1 vote)
Aug 22nd 2016

Le MOOC « Fondamentaux pour le big data » permet d'acquérir efficacement le niveau prérequis en informatique et en statistiques pour suivre des formations dans le domaine du big data. Le big data offre de nouvelles opportunités d’emplois au sein des entreprises et des administrations. De nombreuses formations préparant à ces opportunités de métiers existent. Le suivi de ces formations nécessite des connaissances de base en statistiques et en informatique que ce MOOC vous propose d’acquérir dans les domaines de l’analyse, algèbre, probabilités, statistiques, programmation Python et bases de données.

Average: 3.7 (3 votes)
Aug 8th 2016

Get a practical insight into big data analytics, and popular tools and frameworks for collecting, storing and managing data. Data is everywhere and can be obtained from many different sources. Digital data can be obtained from social media, images, audio recordings and sensors, and electronic data is quite often available as real-time data streams.

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Aug 2016

You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more obvious applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision or finding dense clusters in the advertiser-search query graphs at search engines. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements, call routing in telecommunications and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them approximately in a reasonable time. We finish with some applications to Big Data and Machine Learning which are heavy on algorithms right now.

Average: 5 (5 votes)
Jul 25th 2016

Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems.

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Jul 25th 2016

Accounting Analytics explores how financial statement data and non-financial metrics can be linked to financial performance. In this course, taught by Wharton’s acclaimed accounting professors, you’ll learn how data is used to assess what drives financial performance and to forecast future financial scenarios. While many accounting and financial organizations deliver data, accounting analytics deploys that data to deliver insight, and this course will explore the many areas in which accounting data provides insight into other business areas including consumer behavior predictions, corporate strategy, risk management, optimization, and more.

Average: 9.3 (4 votes)
Jul 25th 2016

You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R.

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Jul 25th 2016

This course is for novice programmers or business people who'd like to understand the core tools used to wrangle and analyze big data. With no prior experience, you'll have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry. You will be comfortable explaining the specific components and basic processes of the Hadoop architecture, software stack, and execution environment. In the assignments you will be guided in how data scientists apply the important concepts and techniques, such as Map-Reduce that are used to solve fundamental problems in big data. You'll feel empowered to have conversations about big data and the data analysis processes.

Average: 6.5 (6 votes)
Jul 25th 2016

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.

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Jul 18th 2016

In this second MOOC in the Social Marketing Specialization - "The Importance of Listening" - you will go deep into the Big Data of social and gain a more complete picture of what can be learned from interactions on social sites. You will be amazed at just how much information can be extracted from a single post, picture, or video. In this MOOC, guest speakers from Social Gist, BroadReader, Lexalytics, Semantria, Radian6, and IBM's Bluemix and Social Media Analytics Tools (SMA) will join Professor Hlavac to take you through the full range of analytics tools and options available to you and how to get the most from them. The best part, most of them will be available to you through the MOOC for free! Those purchasing the MOOC will receive special tools, templates, and videos to enhance your learning experience.

Average: 8.8 (4 votes)
Jul 18th 2016

Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools.

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Jul 18th 2016

Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.

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Jul 18th 2016

Want to learn the basics of large-scale data processing? Need to make predictive models but don’t know the right tools? This course will introduce you to open source tools you can use for parallel, distributed and scalable machine learning.

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Jul 18th 2016

This course is an introduction to how to use relational databases in business analysis. You will learn how relational databases work, and how to use entity-relationship diagrams to display the structure of the data held within them. This knowledge will help you understand how data needs to be collected in business contexts, and help you identify features you want to consider if you are involved in implementing new data collection efforts.

Average: 6.4 (5 votes)
Jul 4th 2016

This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Average: 2.9 (7 votes)
Jul 4th 2016

Learn how you can predict customer demand and preferences by using the data that is all around you. In a digital world, data has gone ‘big’ – ushering in the age of the zettabyte. This subject shows you how big data equals business opportunity. Find out what ‘big data’ means and where it comes from – including ordinary transactions and social interactions. See how smart businesses use data to target their offerings and get ahead of market trends. Consider how marketing data can be based on false assumptions such as the ‘last click myth’.

Average: 9 (3 votes)
Jun 27th 2016

Data visualisation is vital in bridging the gap between data and decisions. Discover the methods, tools and processes involved. Data visualisation is an important visual method for effective communication and analysing large datasets. Through data visualisations we are able to draw conclusions from data that sometimes are not immediately obvious, and interact with the data in an entirely different way.

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Jun 15th 2016

Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals. Spark is rapidly becoming the compute engine of choice for big data. Spark programs are more concise and often run 10-100 times faster than Hadoop MapReduce jobs. As companies realize this, Spark developers are becoming increasingly valued.

Average: 4.3 (4 votes)
May 17th 2016

Learn the latest technologies for managing big data and using it to improve life in smart cities. Cities run on a stream of data. In the smart city, the innovative use of data helps provide better and more inventive services to improve people’s lives and make the entire city run more smoothly. But the data our cities collect nowadays is more massive and varied, and is accessed at higher speeds than ever before. This is Big Data. New technologies are constantly being developed to better manage Big Data. This computer science course, from the IEEE Smart Cities initiative and the University of Trento, helps students understand and use these new technologies to help improve a city.

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