Online Courses
Course
R
Programming
Date
Nov 3rdDec 1st
2014
+
Dec 1st –
Dec 28th
2014
+
6x2015
University
Johns
Hopkins
University
Link
https://w
ww.course
ra.org/cou
rse/rprog
Exploratory
Data Analysis
Nov 3rd –
Dec 1st
2014
+
Dec 1st –
Dec 28th
2014
+
6x2015
Johns
Hopkins
University
https://w
ww.course
ra.org/cou
rse/exdata
Reproducible
Research
Nov 3rdDec 1st
2014
+
Johns
Hopkins
University
https://w
ww.course
ra.org/cou
rse/repdat
Description
Compensation
In this course you will learn how to program in R and how to use
R for effective data analysis. You will learn how to install and
configure software necessary for a statistical programming
environment and describe generic programming language
concepts as they are implemented in a high-level statistical
language. The course covers practical issues in statistical
computing which includes programming in R, reading data into
R, accessing R packages, writing R functions, debugging, profiling
R code, and organizing and commenting R code. Topics in
statistical data analysis will provide working examples.
This course covers the essential exploratory techniques for
summarizing data. These techniques are typically applied before
formal modeling commences and can help inform the
development of more complex statistical models. Exploratory
techniques are also important for eliminating or sharpening
potential hypotheses about the world that can be addressed by
the data. We will cover in detail the plotting systems in R as well
as some of the basic principles of constructing data graphics. We
will also cover some of the common multivariate statistical
techniques used to visualize high-dimensional data.
This course focuses on the concepts and tools behind reporting
modern data analyses in a reproducible manner. Reproducible
research is the idea that data analyses, and more generally,
scientific claims, are published with their data and software
Examiner
Dec 1st –
Dec 28th
2014
+
6x2015
a
Statistical
Inference
Nov 3rdDec 1st
2014
+
Dec 1st –
Dec 28th
2014
+
6x2015
Johns
Hopkins
University
Regression
Models
Nov 3rdDec 1st
2014
+
Johns
Hopkins
University
code so that others may verify the findings and build upon
them. The need for reproducibility is increasing dramatically as
data analyses become more complex, involving larger datasets
and more sophisticated computations. Reproducibility allows for
people to focus on the actual content of a data analysis, rather
than on superficial details reported in a written summary. In
addition, reproducibility makes an analysis more useful to
others because the data and code that actually conducted the
analysis are available. This course will focus on literate statistical
analysis tools which allow one to publish data analyses in a
single document that allows others to easily execute the same
analysis to obtain the same results.
https://w Statistical inference is the process of drawing conclusions about
ww.course populations or scientific truths from data. There are many
ra.org/cou modes of performing inference including statistical modeling,
rse/statinf data oriented strategies and explicit use of designs and
erence
randomization in analyses. Furthermore, there are broad
theories (frequentists, Bayesian, likelihood, design based, …)
and numerous complexities (missing data, observed and
unobserved confounding, biases) for performing inference. A
practitioner can often be left in a debilitating maze of
techniques, philosophies and nuance. This course presents the
fundamentals of inference in a practical approach for getting
things done.
https://w Linear models, as their name implies, relates an outcome to a
ww.course set of predictors of interest using linear
ra.org/cou assumptions. Regression models, a subset of linear models, are
rse/regmo the most important statistical analysis tool in a data scientist’s
Dec 1st –
Dec 28th
2014
+
6x2015
Developing
Data Products
Practical
Machine
Learning
Cryptography
Nov 3rdDec 1st
2014
+
Dec 1st –
Dec 28th
2014
+
6x2015
Nov 3rdDec 1st
2014
+
Dec 1st –
Dec 28th
2014
+
6x2015
Nov 10th
– Dec
ds
Johns
Hopkins
University
Johns
Hopkins
University
toolkit. This course covers regression analysis, least squares and
inference using regression models. Special cases of the
regression model, ANOVA and ANCOVA will be covered as well.
Analysis of residuals and variability will be investigated. The
course will cover modern thinking on model selection and novel
uses of regression models including scatterplot smoothing.
https://w A data product is the production output from a statistical
ww.course analysis. Data products automate complex analysis tasks or use
ra.org/cou technology to expand the utility of a data informed model,
rse/devda algorithm or inference. This course covers the basics of creating
taprod
data products using Shiny, R packages, and interactive graphics.
The course will focus on the statistical fundamentals of creating
a data product that can be used to tell a story about data to a
mass audience.
https://w
ww.course
ra.org/cou
rse/predm
achlearn
One of the most common tasks performed by data scientists
and data analysts are prediction and machine learning. This
course will cover the basic components of building and applying
prediction functions with an emphasis on practical applications.
The course will provide basic grounding in concepts such as
training and tests sets, overfitting, and error rates. The course
will also introduce a range of model based and algorithmic
machine learning methods including regression, classification
trees, Naive Bayes, and random forests. The course will cover
the complete process of building prediction functions including
data collection, feature creation, algorithms, and evaluation.
University
https://w This course will introduce you to the foundations of modern
of Maryland ww.course cryptography, with an eye toward practical applications. We will
Getting and
Cleaning Data
Hardware
Security
20th
2014
+
Mar 9 th
– Apr 18
th 2015
Jan 5th –
Feb 2nd
2015
ra.org/cou
rse/crypto
graphy
Johns
Hopkins
University
https://w
ww.course
ra.org/cou
rse/getdat
a
learn the importance of carefully defining security; of relying on
a set of well-studied “hardness assumptions” (e.g., the hardness
of factoring large numbers); and of the possibility of proving
security of complicated constructions based on low-level
primitives.
Before you can work with data you have to get some. This
course will cover the basic ways that data can be obtained. The
course will cover obtaining data from the web, from APIs, from
databases and from colleagues in various formats. It will also
(5x 2015)
cover the basics of data cleaning and how to make data “tidy”.
Tidy data dramatically speed downstream data analysis tasks.
The course will also cover the components of a complete data
set including raw data, processing instructions, codebooks, and
processed data. The course will cover the basics needed for
collecting, cleaning, and sharing data.
th
Jan 5 – University
https://w Trust in digital system design
th
Feb 14
of Maryland ww.course - Vulnerability in combinational logic
2015
ra.org/cou - Vulnerability in sequential logic and finite state machine
rse/hardw - Hardware Trojan Horse
+
aresec
- Circuit obfuscation
Side-channel attacks
th
Apr 6 –
- Power analysis
May 16th
- Timing attacks
2015
- ElectroMagnetic analysis
Physical attacks
- Fault injection attack
- Smart card security
Programming
for Everybody
(Python)
Pattern
Discovery in
Data Mining
Cloud
Computing
Concepts
Feb 2nd –
Apr 13th
2015
(3 x
2015)
February
2015 –
Mar 31st
2015
February
– Apr
30th
University
of Michigan
University
of Illinois
(Coursera)
University
of Illinois
- Field Programmable Gate Array (FPGA) security
Emerging hardware security topics
- Trust Platform Modules (TPM)
- Physical Unclonable Functions (PUF)
- True Random Number Generators (TRNG)
- RFID tag
- Counterfeiting
- Intellectual property protection
https://w This course is specifically designed to be a first programming
ww.course course using the popular Python programming language. The
ra.org/cou pace of the course is designed to lead to mastery of each of the
rse/pytho topics in the class. We will use simple data analysis as the
nlearn
programming exercises through the course.
https://w
ww.course
ra.org/cou
rse/patter
ndiscovery
Learn the general concepts of data mining along with basic
methodologies and applications. Then dive into one subfield in
data mining: pattern discovery. Learn in-depth concepts,
methods and applications of pattern discovery in data mining.
We will also introduce methods for pattern-based classification
and some interesting applications of pattern discovery. This
course provides you the opportunity to learn skills and content
to practice and engage in scalable pattern discovery methods on
massive transactional data, discuss pattern evaluation
measures, and study methods for mining diverse kinds of
patterns, sequential patterns, and sub-graph patterns.
https://w Cloud computing systems today, whether open-source or used
ww.course inside companies, are built using a common set of core
ra.org/cou techniques, algorithms, and design philosophies—all centered
2015
Software
Security
Feb 23rd
– Apr 4th
2015
rse/cloudc
omputing
around distributed systems. Learn about such fundamental
distributed computing "concepts" for cloud computing.
Some of these concepts include:
• clouds, MapReduce, key-value stores
• classical precursors
• widely-used algorithms
• classical algorithms
• scalability
• trending areas
and more!
University
https://w - Low-level, memory-based attacks, including stack smashing,
of Maryland ww.course format string attacks, stale memory access attacks, and returnra.org/cou oriented Programming (ROP)
rse/softwa - Defenses against memory-based attacks, including stack
resec
canaries, non-executable data (aka W+X or DEP), address space
layout randomization (ASLR), memory-safety enforcement (e.g.,
SoftBound), control-flow Integrity (CFI)
- Web security, covering attacks like SQL injection, Cross-site
scripting (XSS), Cross-site request forgery (CSRF), and Session
hijacking, and defenses that have in common the idea of input
validation
- Secure design, covering ideas like threat modeling and security
design principles, including organizing ideas like favor simplicity,
trust with reluctance, and defend in depth; we present realworld examples of good and bad designs
- Automated code review with static analysis and symbolic
execution, presenting foundations and tradeoffs and using static
taint analysis and whitebox fuzz testing as detailed examples
Fog Networks
and the
Internet of
Things
Mar 2nd
– Apr
13th
2015
Princeton
University
https://w
ww.course
ra.org/cou
rse/fog
Cluster
Analysis in
Data Mining
August
2015 –
Sep 27th
2015
University
of Illinois
(Coursera)
https://w
ww.course
ra.org/cou
rse/cluster
analysis
Cloud
Computing
Applications
August
2015 –
Sep 30th
2015
University
of Illinois
https://w
ww.course
ra.org/cou
rse/clouda
pplication
s
Text Mining
and Analytics
Sep 2015 University
– Oct
of Illinois
https://w
ww.course
- Penetration testing, presenting an overview of goals,
techniques, and tools of the trade
This course teaches the fundamentals of Fog Networking, the
network architecture that uses one or a collaborative multitude
of end-user clients or near-user edge devices to carry out
storage, communication, computation, and control in a
network. It also teaches the key results in the design of the
Internet of Things, including consumer and industrial
applications.
Discover the basic concepts of cluster analysis and then study a
set of typical clustering methodologies, algorithms, and
applications. This includes partitioning methods such as kmeans, hierarchical methods such as BIRCH, density-based
methods such as DBSCAN/OPTICS, probabilistic models and EM
algorithm. Learn clustering and methods for clustering high
dimensional data, streaming data, graph data, and networked
data.
Learn of "cloudonomics," the underlying economic reasons that
we are creating the cloud. Learn the basic concepts underlying
cloud services and be able to use services like AWS or
OpenStack Dashboard to construct cloud services or
applications. Demonstrate your ability to create web services,
massively parallel data intensive computations using
Map/Reduce, NoSQL databases, and real-time processing of
real-time data streams. Use machine learning tools to solve
simple problems.
This course will cover the major techniques for mining and
analyzing text data to discover interesting patterns, extract
25th
2015
ra.org/cou
rse/textan
alytics
Cloud
Networking
October
2015 –
Nov 30th
2015
University
of Illinois
https://w
ww.course
ra.org/cou
rse/cloudn
etworking
Data
Visualization
Novemb University
er 2015 – of Illinois
Dec 27th (Coursera)
2015
https://w
ww.course
ra.org/cou
rse/datavi
sualization
useful knowledge, and support decision making with an
emphasis on statistical approaches that can be generally applied
to arbitrary text data in any natural language with no or
minimum human effort.
This course will allow us to explore in-depth the challenges for
cloud networking—how do we build a network infrastructure
that provides the agility to deploy virtual networks on a shared
infrastructure, that enables both efficient transfer of big data
and low latency communication, and that enables applications
to be federated across countries and continents? Examining
how these objectives are met will set the stage for the rest of
the course.
Learn to present data to an observer in a way that yields insight
and understanding. The first week focuses on the infrastructure
for data visualization. It introduces elementary graphics
programming, focusing primarily on two dimensional vector
graphics, and the programming platforms for graphics. This
infrastructure will also include lessons on the human side of
visualization, studying human perception and cognition to gain a
better understanding of the target of the data visualization.
The second week will utilize the knowledge of graphics
programming and human perception in the design and
construction of visualizations, starting with simple charts and
graphs, and incorporating animation and user interactivity. The
third week expands the data visualization vocabulary with more
sophisticated methods, including hierarchical layouts and
networks. The final week focuses on visualization of database
and data mining processes, with methods specifically focused on
Algorithms,
Part II
Oct 31st
– Dec
19th
2014
Princeton
University
https://w
ww.course
ra.org/cou
rse/algs4p
artII
Cryptography I Jan 5th –
Mar 9th
2015
+ Future
sessions
(?)
Stanford
https://w
ww.course
ra.org/cou
rse/crypto
Cryptography
Stanford
https://w
Jan 5th –
visualization of unstructured information, such as text, and
systems for visual analytics that provide decision support.
Part II covers graph-processing algorithms, including minimum
spanning tree and shortest paths algorithms, and string
processing algorithms, including string sorts, tries, substring
search, regular expressions, and data compression, and
concludes with an overview placing the contents of the course
in a larger context.
Cryptography is an indispensable tool for protecting information
in computer systems. This course explains the inner workings of
cryptographic primitives and how to correctly use them.
Students will learn how to reason about the security of
cryptographic constructions and how to apply this knowledge to
real-world applications. The course begins with a detailed
discussion of how two parties who have a shared secret key can
communicate securely when a powerful adversary eavesdrops
and tampers with traffic. We will examine many deployed
protocols and analyze mistakes in existing systems. The second
half of the course discusses public-key techniques that let two
or more parties generate a shared secret key. We will cover the
relevant number theory and discuss public-key encryption and
basic key-exchange. Throughout the course students will be
exposed to many exciting open problems in the field.
The course will include written homeworks and programming
labs. The course is self-contained, however it will be helpful to
have a basic understanding of discrete probability theory.
Cryptography is an indispensable tool for protecting information
II
Feb 20th
2015
+ Future
sessions
(?)
Computationa
l Methods for
Data Analysis
Dec 9th
2014 –
Feb 17th
2015
+ Future
sessions
(?)
Dec 9th
2014 –
Feb 17th
Computationa
l Methods for
Data Analysis
ww.course
ra.org/cou
rse/crypto
2
University
of
Washington
University
of
Washington
in computer systems. This course is a continuation of Crypto
I and explains the inner workings of public-key systems and
cryptographic protocols. Students will learn how to reason
about the security of cryptographic constructions and how to
apply this knowledge to real-world applications. The course
begins with constructions for digital signatures and their
applications. We will then discuss protocols for user
authentication and zero-knowledge protocols. Next we will
turn to privacy applications of cryptography supporting
anonymous credentials and private database lookup. We will
conclude with more advanced topics including multi-party
computation and elliptic curve cryptography. Throughout the
course students will be exposed to many exciting open
problems in the field. The course will include written
homeworks and optional programming labs. The material is
self-contained, but the course assumes knowledge of the topics
covered in Crypto I as well as a basic understanding of discrete
probability theory.
https://w Exploratory and objective data analysis methods applied to the
ww.course physical, engineering, and biological sciences.
ra.org/cou
rse/comp
methods
https://w Exploratory and objective data analysis methods applied to the
ww.course physical, engineering, and biological sciences.
ra.org/cou
2015
Dynamical
Modeling
Methods for
Systems
Biology
Mar 2nd
– Apr
20th
2015
Digital Signal
Processing
Jan 19th
– Mar
30th
2015
rse/comp
methods
Icahn
https://w
School of
ww.course
Medicine at ra.org/cou
Mount Sinai rse/dynam
icalmodeli
ng
École
Polytechniq
ue Fédérale
de
Lausanne
We take a case-based approach to teach contemporary
mathematical modeling techniques. The course is appropriate
for advanced undergraduates and beginning graduate
students. Lectures provide biological background and describe
the development of both classical mathematical models and
more recent representations of biological processes. The course
will be useful for students who plan to use experimental
techniques as their approach in the laboratory and employ
computational modeling as a tool to draw deeper understanding
of experiments. The course should also be valuable as an
introductory overview for students planning to conduct original
research in modeling biological systems.
This course focuses on dynamical modeling techniques used in
Systems Biology research. These techniques are based on
biological mechanisms, and simulations with these models
generate predictions that can subsequently be tested
experimentally. These testable predictions frequently provide
novel insight into biological processes. The approaches taught
here can be grouped into the following categories: 1) ordinary
differential equation-based models, 2) partial differential
equation-based models, and 3) stochastic models.
https://w The goal of the course is to develop a complete working set of
TIES324 Signaa Timo
ww.course digital signal processing notions from the ground up. DSP is
linkäsittely, 4 Hämäläine
ra.org/cou arguably at the heart of the “digital revolution” that, in the
op
n
rse/dsp
space of just a few decades, has enabled unprecedented levels
of interpersonal communication and of information availability.
Linear and
Integer
Programming
Oct 20th
– Dec
15th
2014
University
of Colorado
Boulder
In the class, starting from the basic definitions of a discrete-time
signal, we will work our way through Fourier analysis, filter
design, sampling, interpolation and quantization to build a DSP
toolset complete enough to analyze a practical communication
system in detail. Hands-on examples and demonstration will be
routinely used to close the gap between theory and practice.
https://w Linear Programming (LP) is arguably one of the most important
ww.course optimization problems in applied mathematics and engineering.
ra.org/cou The Simplex algorithm to solve linear programs is widely
rse/linear regarded as one among the "top ten" algorithms of the 20th
programm century. Linear Programs arise in almost all fields of engineering
ing
including operations research, statistics, machine learning,
control system design, scheduling, formal verification and
computer vision. It forms the basis for numerous approaches to
solving hard combinatorial optimization problems through
randomization and approximation.
The primary goals of this course will be to:
1. Understand the basic theory behind LP, algorithms to solve
LPs, and the basics of (mixed) integer programs.
2. Understand important and emerging applications of LP to
economic problems (optimal resource allocation, scheduling
problems), machine learning (SVM), control design (finite
horizon optimal control, dynamic programming), and formal
verification (ranking functions, symbolic execution, SMT
solvers).
At the end of the course, the successful student will be able to
cast various problems that may arise in her research as
optimization problems, understand the cases where the
Heterogeneou
s Parallel
Programming
Jan 12th
– Mar
8th 2015
University
of Illinois at
UrbanaChampaign
Gamification
Jan 26th
University
optimization problem will be linear, choose appropriate solution
methods and interpret results appropriately. This is generally
considered a useful ability in many research areas.
https://w All computing systems, from mobile to supercomputers, are
ww.course becoming heterogeneous, massively parallel computers for
ra.org/cou higher power efficiency and computation throughput. While the
rse/hetero computing community is racing to build tools and libraries
to ease the use of these systems, effective and confident use of
these systems will always require knowledge about low-level
programming in these systems. This course is designed for
students to learn the essence of low-level programming
interfaces and how to use these interfaces to achieve
application goals. CUDA C, with its good balance between user
control and verboseness, will serve as the teaching vehicle for
the first half of the course. Students will then extend their
learning into closely related programming interfaces such as
OpenCL, OpenACC, and C++AMP.
The course is unique in that it is application oriented and only
introduces the necessary underlying computer science and
computer engineering knowledge for understanding. It covers
the concept of data parallel execution models, memory models
for managing locality, tiling techniques for reducing bandwidth
consumption, parallel algorithm patterns, overlapping
computation with communication, and a variety of
heterogeneous parallel programming interfaces. The concepts
learned in this course form a strong foundation for learning
other types of parallel programming systems.
https://w Gamification is the application of digital game design techniques
Grow to
Greatness:
Smart Growth
for Private
Businesses,
Part I
– Apr
10th
2015
of
Pennsylvani
a
Oct 20th
– Dec
8th 2014
University
of Virginia
ww.course
ra.org/cou
rse/gamifi
cation
to non-game problems, such as business and social impact
challenges. Video games are the dominant entertainment form
of our time because they are powerful tools for motivating
behavior. Effective games leverage both psychology and
technology, in ways that can be applied outside the immersive
environments of games themselves. Gamification as a business
practice has exploded over the past two years. Organizations
are applying it in areas such as marketing, human resources,
productivity enhancement, sustainability, training, health and
wellness, innovation, and customer engagement. Game thinking
means more than just dropping in badges and leaderboards; it
requires a thoughtful understanding of motivation and design
techniques. This course examines the mechanisms of
gamification and provides an understanding of its effective use.
Subtitles for all video lectures available in: English, Russian
(provided by Digital October), Turkish (Koc University)
https://w Most entrepreneurship courses focus on how to start a
ww.course business. Few focus on the next big entrepreneurial inflection
ra.org/cou point: how do you successfully grow an existing private
rse/growt business? This is the focus of this Course. It is based on the
ogreatnes instructor's research and thirty years of real-world experience
s
advising private growth companies.
This Course will challenge how you think about growth; give you
tools to help you plan for growth, assess the preconditions to
grow, and manage the risks of growth. You will study stories of
how five different private businesses faced their growth
challenges.
Growth, if not properly managed, can overwhelm a business,
Grow to
Greatness:
Smart Growth
for Private
Businesses,
Part II
Jan 12th
– Feb
13th
2015
University
of Virginia
destroying value and in many cases even causing the business to
fail. However, the research shows that every growth business
faces common challenges. You can learn from others'
experience—you do not have to "reinvent the wheel".
The Course format is case based. Each case tells a compelling
story. You will learn from Julie Allinson, Susan Fellers, Dave
Lindsey, Parik Laxinarayan and Eric Barger. In addition, each
week, we will discuss a different content theme. In Weeks and 2
and 5, you will engage in Workshops where you will be asked to
use and apply the Course tools and concepts to create growth
strategies for two different real-life businesses. You will have
the opportunity to create a Course Community of fellowstudents to learn from each other as the Course progresses.
You will learn about the: "3 Myths of Growth"; the "Truth About
Growth"; why growth is like "Mother Nature"; the "Gas Pedal"
approach to growth; the all important "4 Ps" of how to grow;
and how to scale a business strategically.
https://w Most entrepreneurship courses focus on how to start a
ww.course business. Few focus on the next big entrepreneurial inflection
ra.org/cou point: how do you successfully grow an existing private
rse/GTG
business? This is the focus of this Course. It is based on the
instructor's research and thirty years of real-world experience
advising private growth companies.
This Course will focus on the common “people” challenges
private growth companies face as they grow. You will study
stories of how six different private businesses faced their
growth challenges.
While strategic focus and operational excellence are necessary
Developing
Innovative
Ideas for New
Companies
Nov 10th
2014 –
Jan 5th,
2015
+
Dec 1st
2014 –
University
of
Maryland,
College
Park
to build a great growth company, they are not sufficient.
Growth requires the right kind of leadership, culture, and
people. My research clearly showed that many entrepreneurs
struggle with personal challenges presented to them by growth,
as well as the challenge of hiring the right people and building
the right management team that can play well together. The
research shows that every growth business faces common
challenges. You can learn from others' experience—you do not
have to "reinvent the wheel".
The Course format is story based. Each case tells a compelling
story. You will learn from Barbara Lynch, Ryan Dienst, Steve
Ritter, Randy Bufford, John Gabbert, and Mike Cote. In addition,
each week, we will discuss a different content theme. In Week
3, you will engage in a Workshop where you will be asked to
apply the Growth System Assessment Tool. You will have the
opportunity to create a Course Community of fellow students to
learn from each other as the Course progresses.
You will learn how entrepreneurs must grow, too; the “secret”
of high performance; people-centric leadership; how to create
high employee engagement; how to create an internal Growth
System; and how to build a senior management team.
https://w This course assists aspiring entrepreneurs in developing great
ww.course ideas into great companies. With strong economies presenting
ra.org/cou rich opportunities for new venture creation, and challenging
rse/innova economic times presenting the necessity for many to make their
tiveideas
own job, the need to develop the skills to develop and act on
innovative business opportunities is ever present.
Using proven content, methods, and models for new venture
Jan 26th
2015
Content
Strategy for
Professionals:
Engaging
Audiences for
Your
Organization
Jan 19th
–
Mar 1st
2015
Northweste
rn
University
opportunity assessment and analysis, students will learn how to
enhance their entrepreneurial mindset and develop their
functional skill sets to see and act entrepreneurially. The initial
steps to creating a business plan, and raising financial capital to
launch the firm, are examined as well. Our goal is to demystify
the startup process, and to help you build the skills to identify
and act on innovative opportunities now, and in the future.
https://w Why Content Strategy is essential for professionals in any
ww.course organization – business, non-profit, or government.
ra.org/cou Content Strategy is a conversation that provides thoughtrse/conte leadership. It starts a “conversation” with users and
ntstrategy stakeholders inside and outside an organization. Conversations
are the natural way people think about complex issues.
Conversations also enable people to develop “stories,” which
lead to understanding and helpful mental pictures. Content
Strategy practitioners are at all levels of the best enterprises – in
all departments and sectors from the top leader to the
newcomer in the ranks.
In this complex information age, forward-thinking employees
know that if they and their organizations are to thrive, they
need to go beyond their job descriptions. They must master the
most demanding communications frontier – creating engaging,
strategic, honest stories and information that is valued by their
most important audiences. In turn that will make their
enterprise stand out.
Regardless of their department, area of work, or expertise,
Content Strategy practitioners know how to use words, pictures,
video, and social and mobile media to interact with their most
International
Organizations
Management
Nov 10th University
–
of Geneva
Dec 15th
2014
important constituents with trustable, actionable information
that the audience values and will use. The strategic content they
produce enhances the audience’s lives and deepens their
understanding and engagement with the organization.
Content Strategy is similar to the best examples of journalism,
but it is done by non-journalism organizations. Content Strategy
is always honest, trustable, and transparent. It tells all sides of
every story it reports. Often it is also deeper and directed at
topics and audiences that traditional journalism under-serves or
does not reach at all. Content Strategy is not advertising,
marketing, or public relations. It is different because it never
pursues the persuasive goals that are appropriate in those
disciplines.
https://w International and not-for-profit organizations present an
ww.course increasingly complex environment to work in and therefore
ra.org/cou require for their successful management an unprecedented
rse/intero level of managerial skills on top of a deep understanding of the
rg
socioeconomic and political context they operate in. This course
is designed to provide students with (1) basic notions of the
practice of international relations (2) a general overview of the
management challenges international and not-for-profit
organizations are faced with as well as key theoretical
frameworks and practical tools for managers to excel in this
environment. Key areas of management will be reviewed, from
strategy setting to implementation through marketing & fund
raising, and assessment. (3) Given the growing interaction
between public and private sectors, this course also touches
upon the management of public/private partnerships.
Inspiring
Leadership
through
Emotional
Intelligence
Nov 3rd
2014 –
Jan 5th
2015
Case
Western
Reserve
University
https://w
ww.course
ra.org/cou
rse/lead-ei
Introduction
Feb 2nd
University
https://w
Great leaders move us through our emotions. They establish a
deep emotional connection with others called resonance. Their
own levels of emotional intelligence allow them to create and
nurture these resonant relationships. They use their EI as a path
to resonant leadership through mindfulness, hope, compassion,
and playfulness. Unfortunately, most people in leadership and
helping positions (i.e., doctors, teachers, coaches, etc.) lose their
effectiveness over time because of the cumulative damage from
chronic stress. But humans can renew themselves,
neurologically, hormonally, and emotionally.
Based on decades of research into emotional
intelligence competencies and longitudinal studies of their
development, the course will examine resonance and
developing "resonant leadership" capability, emotional
intelligence, and the experiences of mindfulness, hope, and
compassion. Using the latest in neuroscience, behavioral,
organizational and psychological research, participants
will understand the theory, research, and experience of the
Positive Emotional Attractor that is an essential beginning to
sustained, desired change for individuals, teams, organizations
and communities.
The course will consist of nine classes, with three or so modules
per class, to be taken over 8 weeks. Each module will consist of
a video, assigned and recommended readings, reflective
exercises, writing in your Personal Journal, and on-line,
asynchronous discussions. Each class will have personal learning
assignments to use and tests of comprehension.
This course is primarily devoted to the fundamental principles of
to Finance
– May
18th
2015
+
Jun 1st –
Sep 14th
2015
+
Oct 5th
2015 –
Jan 18th
2016
of Michigan
ww.course
ra.org/cou
rse/introfi
nance
valuation. We will learn and apply the concepts of time value of
money and risk to understand the major determinants of value
creation. We will use both theory and real world examples to
demonstrate how to value any asset.