Online courses directory (10358)

An on-hand help desk, administrative assistance, and a valuable tap into global resources are just a few examples of the

Katie Rodan, co-developer of Proactiv Solution, talks about the benefits of her partnership. She says that it is great f

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Replicating Best Marketing Strategies used by Apple, Google, Starbucks, Walmart & Geiko etc.

Project management plays a key role in supporting a business’s success. In this project management course, you will learn what’s required from an organization for projects to excel.

You will learn how to turn project management principles and theory into practice. The course will cover:

• project management methods and best practices
• project portfolio management
• the project management office
• Six Sigma
• corporate culture and organizational behavior
• project management leadership

The course will utilize case studies and examples from companies to help students sharpen their project management skills to recognize and implement an environment that supports success.

First, we will cover the basic organizational and leadership elements required to provide a successful environment for all projects to succeed.

Second, we will cover the necessary organizational support structures and methods that enable project management and project managers to deliver results to the business and to the customers they serve.

Finally, we will explore the challenges of specific types of projects such as projects in crisis, global projects and managing a portfolio.

This course is part of the RIT Project Management MicroMasters Program that is designed to teach the importance of the organizational and leadership characteristics for the success of projects. In order to qualify for the MicroMasters Credential, you will need to earn a verified certificate in each of the three courses as well as pass a capstone exam.

What is a "life" when it's written down? How does memory inform the present? Why are memoirs so popular? This course will address these questions and others, considering the relationship between biography, autobiography, and memoir and between personal and social themes. We will closely examine some recent memoirs: Tobias Wolff's This Boy's Life, Barack Obama's Dreams From My Father, Edwidge Danticat's Brother, I'm Dying, Ayaan Hirsi Ali's Infidel, and Alison Bechdel's Fun Home. Students will write two brief papers: a critical essay and an experiment in memoir.

As a "Sampling," this class offers 6 units, with a strong emphasis on close reading, group discussion, focused writing, and research and presentation skills.

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Charles Pinkerton, Partner at The Media Kitchen, gives useful tips and information about how to get the most out of an i

The Total Leadership approach will help you become a better leader by having a richer life and have a richer life by becoming a better leader. Learn a practical, proven method for how to articulate your core values and vision; build trust with your most important people; and achieve "four-way wins" -- improved performance at work or in school, at home with your family, in your community, and for your self (mind, body, spirit). It's not about "work/life balance"; it's about creating harmony among the different parts of your life as a leader in all of them.

Material science is the basis of all semiconductor technology, says former Intel Chairman of the Board Craig Barrett. An

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

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.

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.

Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research on learning.

In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will examine the methods being developed by researchers in the educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You’ll also gain experience with standard data mining methods frequently applied to educational data. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications.

The course will discuss how to use each method to answer education research questions, and to drive intervention and improvement in educational software and systems. Methods will be covered at a theoretical level, and in terms of learning how to apply them using software tools like RapidMiner. We will also discuss validity and generalizability; establishing how trustworthy and applicable the analysis results.

Essential Knowledge for everyone associated with Big Data & Hadoop