Online courses directory (19947)
How to focus and get more done so you can unwind and disconnect.
Material science is the basis of all semiconductor technology, says former Intel Chairman of the Board Craig Barrett. An
Boost your personal and professional writing style by adding variety, complexity and sophistication to your sentences.
Classroom Management that Fosters Responsibility, Nurtures Intrinsic Motivation, and Brings Out the Best in Students
Imagine selling out of your entire eBay product inventory by simply sending ONE EMAIL?
Learn essentials of video screen recording so you can digitize your knowledge, flip the classroom and teach online now.
Explore how communities in transitioning economies around the world are working to enable the growth of entrepreneurship when the resources from the private sector alone are limited.
Any "ism" is an extreme and dangerous thing, says William McDonough, McDonough + Partners. And finding
The Mobile App Audit - best practices and proven techniques for acquiring more user downloads!
BGP is the toughest subject on the CCNP exams. Master it with Chris Bryant, CCIE #12933!
This course will start with the nuclear structure of atoms and discuss the creation of hydrogen in the big bang universe and the fusion of hydrogen to make heavier elements in stars. Three pillars of the big bang cosmology will be elaborated.
Ch. 1 “Atomic Nucleus” Rutherford’s 1908 Nobel Lecture will be used to discuss identification of the alpha particle as a possible building block of elements such as carbon and oxygen. The discovery of the proton as the ultimate building block of all nuclei will also be covered.
Ch. 2 “Origin of Elements” The modern view of the big bang synthesis of light elements and the stellar synthesis of heavy elements will be discussed. The 1978 Nobel Lecture by Penzias, titled “The Origin of Elements”, will be the primary source material.
Ch. 3 “Cosmic Background Radiation” How big bang cosmology was established by the discovery of the cosmic background radiation by Penzias and Wilson in 1965 will be discussed using Wilson’s 1978 Nobel Lecture.
Ch. 4 “Expansion of the Universe” How the foundation for big bang cosmology was laid out by the works of Leavitt, Slipher, and Hubble is the subject of this chapter. Hubble’s 1929 paper in PNAS about Hubble’s law will be the primary resource.
Specific, proven strategies Big Brand marketers use -- tailored to small brands with limited budgets.
Organizations use their data to support and influence decisions and build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. The collection of skills required by organizations to support these functions has been grouped under the term ‘data science’.
This statistics and data analysis course will attempt to articulate the expected output of data scientists and then teach students how to use PySpark (part of Spark) to deliver against these expectations. The course assignments include log mining, textual entity recognition, and collaborative filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.
This course covers advanced undergraduate-level material. It requires a programming background and experience with Python (or the ability to learn it quickly). All exercises will use PySpark (the Python API for Spark), and previous experience with Spark equivalent to Introduction to Apache Spark, is required.
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 Hadoop, R and MOA (Massive Online Analysis).
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;
- understanding of ethical and social concerns of data mining.
By the end of this course, you will be able to approach large-scale data science problems with creativity and initiative.
We introduce the characteristics and related analytic challenges on dealing with clinical data from electronic health records. Many of those insights come from medical informatics community and data mining/machine learning community. There are three thrusts in this course: Application, Algorithm and System
Data science plays an important role in many industries. In facing massive amount of heterogeneous data, scalable machine learning and data mining algorithms and systems 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. In this course, we study such algorithms and systems in the context of healthcare applications. In healthcare, large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). This data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment.
In data science, data is called “big” if it cannot fit into the memory of a single standard laptop or workstation.
The analysis of big datasets requires using a cluster of tens, hundreds or thousands of computers. Effectively using such clusters requires the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and corresponding computational models, such as Hadoop, MapReduce and Spark.
In this course, part of the Data Science MicroMasters program, you will learn what the bottlenecks are in massive parallel computation and how to use spark to minimize these bottlenecks.
You will learn how to perform supervised an unsupervised machine learning on massive datasets using the Machine Learning Library (MLlib).
In this course, as in the other ones in this MicroMasters program, you will gain hands-on experience using PySpark within the Jupyter notebooks environment.
What is Big Data,BIG Data Analytics,Traditional Analytics Vs. BIG Data Analytics and detail concept of Hadoop Ecosystem
Extremely useful basic concepts on Big Data and Apache Hadoop for beginners
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