Online courses directory (1728)

In this business and management course you will learn management techniques to operate in an international economy presented with tremendous opportunities as well as risks. 

Globalization has dramatically expanded opportunities for international trade, investment and economic development. At the same time, global managers are facing the prospect of trade wars, international financial crises and intensified competition over markets and resources. International organizations such as the International Monetary Fund, World Trade Organization and World Bank Group have a direct impact on international business operations.

This course develops analytical tools for understanding the rapidly changing and dynamic global economy. With these tools, you will be better prepared to anticipate the risks and take advantage of the opportunities you will encounter in the global business environment.

Improvements in modern biology have led to a rapid increase in sensitivity and measurability in experiments and have reached the point where it is often impossible for a scientist alone to sort through the large volume of data that is collected from just one experiment. 

For example, individual data points collected from one gene expression study can easily number in the hundreds of thousands. These types of data sets are often referred to as ‘biological big data’ and require bioinformaticians to use statistical tools to gain meaningful information from them.

In this course, part of the Bioinformatics MicroMasters program, you will learn about the R language and environment and how to use it to perform statistical analyses on biological big datasets.  

In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next generation sequencing and microarray data. Finally, we will discuss hierarchical models and empirical bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics

Regression Analysis is the most common statistical modeling approach used in data analysis and it is the basis for more advanced statistical and machine learning modeling.

In this course, you will be given fundamental grounding in the use of widely used tools in regression analysis. You will learn the basics of regression analysis such as linear regression, logistic regression, Poisson regression, generalized linear regression and model selection.

Throughout this course, you will be exposed to not only fundamental concepts of regression analysis but also many data examples using the R statistical software. Thus by the end of this course, you will also be familiar with the implementation of regression models using the R statistical software along with interpretation for the results derived from such implementations.

This course is more about the opportunity for individual discovery than it is about mastering a fixed set of techniques.

The job of a data scientist is to glean knowledge from complex and noisy datasets.

Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

In this course, part of the Data Science MicroMasters program, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Jupyter notebooks.

Concepts covered included: random variables, dependence, correlation, regression, PCA, entropy and MDL. 

We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics

Statistics is a versatile discipline that has revolutionized the fields of business, engineering, medicine and pure sciences. This course is Part 2 of a 4-part series on Business Statistics, and is ideal for learners who wish to enroll in business programs. The first two parts cover topics in Descriptive Statistics, whereas the next two focus on Inferential Statistics.

Spreadsheets containing real data from diverse areas such as economics, finance and HR drive much of our discussions.  

In Part 2, we use the language of probability to examine the underlying distributions of random variables. We model real-life phenomena using known variables such as Binomial, Poisson and Normal. We learn how to simulate data that are distributed according to these variables.

We shall take up datasets that have over a million rows, which makes it difficult to analyze using a spreadsheet. This is a natural setting for R, an advanced statistical programming platform. We incorporate helpful tutorials to get learners acquainted with the platform. 

Statistics is a versatile discipline that has revolutionized the fields of business, engineering, medicine and pure sciences. This course is Part 1 of a 4-part series on Business Statistics, and is ideal for learners who wish to enroll in business programs. The first two courses cover topics in Descriptive Statistics, whereas the next two courses focus on Inferential Statistics.

Spreadsheets containing real data from diverse areas such as economics, finance and HR drive much of our discussions.  

In Part 1, we shall be exploring multiple ways to describe these datasets, numerically as well as visually. Throughout, we shall embrace a problem-based approach to understanding the material: the primary reason to pick up a tool or a technique will be to solve a problem. Our course makes judicious use of tools.

In Part 2, we shall take up a few datasets that have over a million rows, which makes it impossible to analyze using a spreadsheet. This is a natural setting for R, an advanced statistical programming platform. The courses incorporate helpful tutorials to get learners acquainted with both the mechanisms. Parts 3 and 4 are dedicated to Inferential Statistics. In Part 3, we begin by exploring the benefits of random sampling, and apply the Central Limit Theorem to arrive at confidence intervals for important population parameters. We also learn how to formulate hypotheses for business data, and resolve them with the testing framework that we establish. Along the way, we shall compare two or more populations and draw inferences with a set of statistical tests.

You will learn all these concepts with the help of various demonstrations, which show real-life application of the concepts related to business situations.

Data is everywhere, from the media to the health sciences, and from financial forecasting to engineering design. It drives our decisions, and shapes our views and beliefs. But how can we make sense of it?

This course introduces some of the key ideas and concepts of statistics, the discipline that allows us to analyse and interpret the data that underpins modern society.

In this course, you will explore the key principles of statistics for yourself, using interactive applets, and you will learn to interpret and evaluate the data you encounter in everyday life.

No previous knowledge of statistics is required, although familiarity with secondary school mathematics is advisable.

Logo image: © The University of Edinburgh 2016 CC BY, derived from Waverley Bridge, by Manuel Farnlack on Flickr, 2010 CC BY

The motion of falling leaves or small particles diffusing in a fluid is highly stochastic in nature. Therefore, such motions must be modeled as stochastic processes, for which exact predictions are no longer possible. This is in stark contrast to the deterministic motion of planets and stars, which can be perfectly predicted using celestial mechanics.

This course is an introduction to stochastic processes through numerical simulations, with a focus on the proper data analysis needed to interpret the results. We will use the Jupyter (iPython) notebook as our programming environment. It is freely available for Windows, Mac, and Linux through the Anaconda Python Distribution.

The students will first learn the basic theories of stochastic processes. Then, they will use these theories to develop their own python codes to perform numerical simulations of small particles diffusing in a fluid. Finally, they will analyze the simulation data according to the theories presented at the beginning of course.

At the end of the course, we will analyze the dynamical data of more complicated systems, such as financial markets or meteorological data, using the basic theory of stochastic processes.

This course has a focus on learning the most commonly used project management methodologies in the IT field, and why they are effective. This course introduces you to project management standards and frameworks that increase efficiency and deliver tangible business benefits to IT projects.

Topics include:

• Relationships among projects, programs and portfolios
• Organizational culture and project management roles
• Project management methods and lifecycles and their applications

In this course, part of the Marketing in a Digital World MicroMasters program, you will learn how to apply the concepts of advertising and marketing to branding. We will examine topics and various strategies related to building, measuring, and managing a brand, including direct and indirect measures of brand equity, structures of desired brand knowledge, choice of brand elements, development of supporting marketing programs, and management of brands over time.

You will learn how to:

• Develop a framework to identify and establish brand positioning and values to build customer based brand equity
• Plan and implement brand marketing programs through integrated marketing communications and brand associations to build brand equity
• Evaluate how a brand equity management system can capture customer mindset and enhance market performance through sources and outcomes of brand equity
• Design specific strategies that focus around extending, revitalising and reinforcing existing brands to retain brand resonance (i.e. loyalty, attachment, community and engagement).

The branding landscape is fast-paced and continually changing, yet is a stimulating and exciting environment in which to work. This course is relevant to anyone working in marketing, communications, public relations, social media and advertising.

This course is part of the CurtinX MicroMasters Credential in Marketing in a Digital World that is specifically designed to teach the critical skills needed to be successful in this exciting field. In order to qualify for the MicroMasters Credential you will need to earn a Verified Certificate in each of the five courses.

In this business and management course, you will develop a strong understanding of how firms compete by learning about important analytical approaches that underlie  business strategy.

This course will show you how a business views itself in its "totality" and in the context of its environment. The course will be taught from the perspective of a manager or chief executive officer (CEO). 

This course aims to equip you with a good understanding of:

• What managing a firm strategically implies?
  • How one analyzes the industry in which a firm competes?
  • How does a firm create competitive advantage?
  • How does a firm sustain its competitive advantage?

Help your organization survive, drive, and thrive on fundamental technological changes in your industry. This advanced strategic management course helps you translate strategic insights into smart strategic decisions on positioning, partnering and being socially responsible.

1. Positioning the Firm:

• How can strategy portfolios guide corporate strategy and help decision-makers scope the activities of their organization?
• How can general strategies help organizations gain competitive advantages in their industries?
• How can organizations make competition irrelevant and create uncontested market space?

2. Orchestrating Strategic Alliances:

• What is the case for cooperative strategy? When should we move from a competitive to a cooperative mindset?
• What are strategic alliances and what is their payoff?
• How can firms build and orchestrate collaborative ecosystems to enhance their (innovative) performance?

3. Embracing Social Responsibility:

• What is the role of business in society? How do organizations contribute and cause harm to the public good?
• What exactly are the social responsibilities of organizations?
• To whom are they responsible and for what? Why should they care?
• How can ethics inform strategic decisions? How can we encourage ethical behavior and reconcile responsibility and profitability? 

All key ideas are illustrated through a case study on Tesla and its role in the technological transformation of the global automotive industry.

This course is part of the "Managing Technology & Innovation: How to deal with disruptive change" MicroMasters program designed to teach the critical skills needed to be successful in this exciting field. In order to qualify for the MicroMasters Credential, you will need to earn a Verified Certificate in each of the six courses of the RWTHx MicroMasters program.

Engage in rigorous strategic analysis to complement your intuition with the deep insights needed to help your organization prosper in times of transition. This foundational course enables you to design effective strategy processes, spot fundamental market and technology changes in your industry and renew your organization’s internal resource base.

1. Unpacking the Process of Strategy:

• What precisely is strategy and why does it matter? What are the origins of the concept and how can we define it?
• What are the building blocks of strategy and how can they be arranged as a process?
• What distinct conceptions of the strategy process co-exist and what are their respective advantages and disadvantages?

2. Analyzing Industry Structures:

• What is the role of the industry and its structure for firm performance? How can we analyze industry structures?
• How can we unpack industries and identify the most attractive strategic positions to occupy therein?
• How do we identify what it takes to outcompete others in our strategic group?

3. Developing Internal Resources:

• Can firms succeed in generally unattractive industries? What is the role of firms’ internal resource base in this regard?
• How can we find out which resources and capabilities are likely to have the greatest strategic value?
• What are dynamic capabilities and how do they matter for firm survival and performance?

All key ideas are illustrated through a real-life case study on Tesla and its role in the technological transformation of the global automotive industry. 

This course is part of the "Managing Technology & Innovation: How to deal with disruptive change" MicroMasters program designed to teach the critical skills needed to be successful in this exciting field. In order to qualify for the MicroMasters Credential, you will need to earn a Verified Certificate in each of the six courses of the RWTHx MicroMasters program.

Increasing concerns about the state of our planet have catapulted sustainability issues from the corporate backburner to the boardroom. Consequently, corporate sustainability initiatives have flourished, driven by a desire to minimize costs and risks, maximize opportunities and enhance reputation.

What does sustainability mean in the business context? Why has it captured so much attention from academics as well as corporate leaders? How can businesses manage emerging sustainability challenges without compromising their core advantages, and transform themselves into 'sustainable enterprises'?

This course explores the emerging relationships between sustainability issues and competitive advantage. Building on the basic concepts of strategic management, this course will explore how managers may effectively deal with the sustainability challenges that they now encounter.

By encouraging you to reflect on these issues using multiple case studies, discussions and interviews, we will aim to enhance your understanding of alternative models of strategy development in the context of sustainable development.

Too much mathematical rigor teaches rigor mortis: the fear of making an unjustified leap even when it lands on a correct result. Instead of paralysis, have courage: Shoot first and ask questions later. Although unwise as public policy, it is a valuable problem-solving philosophy and the theme of this course: how to guess answers without a proof or an exact calculation, in order to develop insight.

You will learn this skill by mastering six reasoning tools---dimensional analysis, easy cases, lumping, pictorial reasoning, taking out the big part, and analogy. The applications will include mental calculation, estimating population growth rates, understanding drag without differential equations, singing musical intervals to estimate logarithms, approximating integrals, summing infinite series, and turning differential equations into algebra.

Your learning will be supported by regular readings that you discuss with other students, by short tablet videos, by quick problems to help you check your understanding, by weekly homework problems, review and and a final exam. You will work hard, and, by the end of the course, have learned a rough-and-ready approach to using mathematics to understand the world.

All required readings are available within the courseware, courtesy of The MIT Press. A print version of the course textbook, Street-Fighting Math, is also available for purchase. The MIT Press is offering enrolled students a special 30% discount on books ordered directly through the publisher’s website. To take advantage of this offer, please use promotion code SFM30 at The MIT Press. 

FAQ

• Do I need to buy a textbook?
  • Back in 2010, MIT Press agreed to publish the textbook, *Street-Fighting Mathematics*, under a free license (in print and online).
  • Thus, the book is legally available all over the internet, including on this course platform.
  • As a registered student in this course, you can also purchase a printed book from MIT Press at a discount.
• Do you often get into street fights?
  • The last time was in high school, when I was attacked for being “different” and suspended for fighting back.
  • However, in my problem-solving fights (and now that I’m older!), I regularly use reasoning tools and we’ll do the same in this course.

This economics and finance course surveys developments in the area of prudential risk regulation with particular emphasis on stress testing as the primary tool of regulation. This course will explain the motivations for changes to the BIS capital accords from Basel I to Basel III.

Upon completion of this course, participants will receive a certificate bearing the New York Institute of Finance (NYIF) name. A NYIF certificate is a valuable addition to your credentials, proving that you have acquired the work-ready skills that employer’s value.

For those who wish to go further, students can enroll in the other four modules to earn the complete Risk Management Professional Certificate, backed by the New York Institute of Finance’s 93-year history. As a final option, students may also opt to sit for the NYIF Certificate of Mastery Exam, resulting in the Risk Management Certificate of Mastery upon successful completion.

This economics and finance course will cover the practical aspects of conducting CCAR/DFAST tests in the US. We will also review the impact of the financial (subprime) crisis of 2007 and 2008 on regulatory reform and the consequences for financial intermediaries.

Upon completion of this course, participants will receive a certificate bearing the New York Institute of Finance (NYIF) name. A NYIF certificate is a valuable addition to your credentials, proving that you have acquired the work-ready skills that employer’s value.

For those who wish to go further, students can enroll in the other four modules to earn the complete Risk Management Professional Certificate, backed by the New York Institute of Finance’s 93-year history. As a final option, students may also opt to sit for the NYIF Certificate of Mastery Exam, resulting in the Risk Management Certificate of Mastery upon successful completion.

Have you ever needed to lease a car, start a small business, or simply rent an apartment?

Deals are a part of everyday life. Understanding how to construct an effective agreement is critical, and yet, many people are unfamiliar with how an effective agreement is structured and what they can do to support a successful deal. This course features numerous examples of situations in which you may need to make a deal and provides practical information and resources to help you construct successful agreements. 

In this course, we have brought three Cornell Law professors and several practicing attorneys together to provide you with a select range of topics that will serve as a foundation for structuring successful contracts. Together, they possess a wealth of knowledge and experience as legal scholars and distinguished law practitioners.

You will begin with a brief overview of the framework within which laws and contracts are enforced.. You will then focus on various kinds of contract provisions that enable and support successful agreements. You will gain greater familiarity with legal terms and concepts involved in deals, making collaboration with legal counsel more effective.