Online courses directory (19947)

In today’s world, managerial decisions are increasingly based on data-driven models and analysis using statistical and optimization methods that have dramatically changed the way businesses operate in most domains including service operations, marketing, transportation, and finance.

The main objectives of this course are the following:

• Introduce fundamental techniques towards a principled approach for data-driven decision-making.
• Quantitative modeling of dynamic nature of decision problems using historical data, and
• Learn various approaches for decision-making in the face of uncertainty

Topics covered include probability, statistics, regression, stochastic modeling, and linear, nonlinear and discrete optimization.

Most of the topics will be presented in the context of practical business applications to illustrate its usefulness in practice. 

Flying drones or robot manipulators accomplish heavy-duty tasks that deal with considerable forces and torques not covered by a purely robot kinematics framework. Learn how to formulate dynamics problems and design appropriate control laws.

In this course, part of the Robotics MicroMasters program, you will learn how to develop dynamic models of robot manipulators, mobile robots, and drones (quadrotors), and how to design intelligent controls for robotic systems that can grasp and manipulate objects.

We will cover robot dynamics, trajectory generation, motion planning, and nonlinear control, and develop real-time planning and control software modules for robotic systems. This course will give you the basic theoretical tools and enable you to design control algorithms.

Using MATLAB, you will apply what you have learned through a series of projects involving real-world robotic systems.

How do robots “see”, respond to and learn from their interactions with the world around them? This is the fascinating field of visual intelligence and machine learning. Visual intelligence allows a robot to “sense” and “recognize” the surrounding environment. It also enables a robot to “learn” from the memory of past experiences by extracting patterns in visual signals.

You will understand how Machine Learning extracts statistically meaningful patterns in data that support classification, regression and clustering. Then by studying Computer Vision and Machine Learning together you will be able to build recognition algorithms that can learn from data and adapt to new environments.

By the end of this course, part of the Robotics MicroMasters program, you will be able to program vision capabilities for a robot such as robot localization as well as object recognition using machine learning.

Projects in this course will utilize MATLAB and OpenCV and will include real examples of video stabilization, recognition of 3D objects, coding a classifier for objects, building a perceptron, and designing a convolutional neural network (CNN) using one of the standard CNN frameworks.

The most comprehensive step by step guide taking the none coder(mom) and turning them(her) into an HTML master.

Want to be the “CEO” of the digital product, but unsure which types of skillsets you may need? This course is for you.

Product management drives the implementation of business models in startups and digital enterprises. Learn about the key decisions, underlying tradeoffs, and implementation decisions needed for each phase of the product life and master business and organizational logic to ensure product success in the marketplace.

In this course, part of the Digital Product Management MicroMasters program, you will be introduced to frameworks for decision-making based on both economic and organizational considerations. These frameworks inform a rising product manager on how to:

(i)  understand customer co-creation, needs and become “a champion” for user centric development in the digital technologies.

(ii)  set up and manage specific work flows (e.g. either lean, agile or stage gate development tasks) that result in timely launch and upgrades of products.

(iii)  take a data and metrics driven approach to make product life cycle decisions including pricing, versioning, maintenance, helpdesks and end of life.

(iv)  shape the direction of the product based on experimentation and system design  thinking by learning from product roadmaps, competitive considerations, and allied evolution of demand in digital markets.

Caveat: This is not a course on software development or architecture or on product marketing. The role of a product manager is to work with these functions effectively, such that the interests of a product (e.g. its profitability) and its customers are best served. Thus, the perspectives and skills covered in this course are integrative, and allied with decision-making, in their orientation.  

The exciting field of Cloud Computing is rapidly changing how businesses operate today. Cloud computing provides rapid access to shared pools of resources, such as: compute, storage, networks, applications, services, or libraries using an on-demand, utility-based model. The characteristics of a cloud computing model include: self-service, network access, resource pools, rapid elasticity, and metered resource usage. As with any large-scale shift, new skills and processes must be learned and implemented to overcome management challenges. Existing business processes, workflows, and policies must be adapted to account for this new technology.

This course, part of the Cloud Computing MicroMasters program, focuses on commonly encountered management issues with the adoption and implementation of cloud computing. Examples include: Cloud migration, Cloud Requests for Proposals, Cloud Service Level Agreements, and other business process compliance issues.

You will examine these issues in depth, then review industry best practices and other case studies to develop the techniques that address and mitigate them. Business processes such as procurement, vendor management, and end-user management are also covered.

In today’s interconnected world, online education has exploded with engaging learning experiences infused with interactive digital tools, digital media, and collaborative projects designed to engage dispersed learners. These highly engaging and effective courses are not created by chance - they are created by instructional designers using a careful and systematic design process.

In this education and teacher training course, part of the Instructional Design and Technology MicroMasters Program we will look at the history and evolution of online learning. You will explore traditional instructional design models and the progression of the learning design approach to creating online learning experiences. During the instructional design process, it’s important to collaborate and work with the many stakeholders involved in the planning and design, especially subject matter experts.

You will explore curriculum design, collaboration and questioning techniques to create shared understandings as you develop your outline of an online course. 

Proteins play a very important role in all organisms. In fact, most of the work that happens inside every cell happens because a specific protein is employed for a specific task. Often the three-dimensional shape of a protein plays a major role in its function. As such, it is important to know the structure of all proteins to have an idea of what function they perform.

One of the jobs of a bioinformatician is to predict the three-dimensional structure of a protein using only the DNA sequence that encodes it as well as determining the effects of any mutations in the DNA on the three-dimensional structure/function.

In this course, part of the Bioinformatics MicroMasters program, you will learn about protein structure and its impact on function, practice aligning protein sequences to discover differences, and generate model structures of proteins using web and software-based approaches.

There is much more to software testing than just finding defects. Successful software and quality assurance engineers need to also manage the testing of software. 

In this course, part of the Software Testing and Verification MicroMasters program, you will learn about the management aspects of software testing. You will learn how to successfully plan, schedule, estimate and document a software testing plan.

You will learn how to analyze metrics to improve software quality and software tests.

We will also discuss software quality initiatives developed by industry experts.

No previous programming knowledge needed.

This is the third course in the Software Development MicroMasters program. You will learn how to build larger and more complex software systems using the Java programming language.

The course begins with the topic of data abstraction - from specification to implementation. Particular attention is given to how to write robust tests using JUnit. Then the course expands on these ideas to explore how type hierarchies and polymorphism can be used to decrease redundancy in your code. The course wraps up with a discussion of how to design robust classes.

By the end of the course, you will have a solid foundation in designing software in Java, and be ready to move onto Software Construction: Object-Oriented Design, where you will learn more complex design patterns and principles designing object-oriented programs.

Learners who enroll in the Verified track will receive staff grading for the course project and increased interaction with the instructors and staff.

The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data.

The goal of this course, part of the Analytics: Essential Tools and Methods MicroMasters program, is for you to learn how to build these components and connect them using modern tools and techniques.

In the course, you’ll see how computing and mathematics come together. For instance, “under the hood” of modern data analysis lies  numerical linear algebra, numerical optimization, and elementary data processing algorithms and data structures. Together, they form the foundations of numerical and data-intensive computing.

The hands-on component of this course will develop your proficiency with modern analytical tools. You will learn how to mash up Python, R, and SQL through Jupyter notebooks, among other tools. Furthermore, you will apply these tools to a variety of real-world datasets, thereby strengthening your ability to translate principles into practice.

This course presents an example of how to apply a database application development methodology to a major real-world project.

All the database concepts, techniques and tools that are needed to develop a database application from scratch will be introduced along the way as you apply them to your own major class team project.

In addition to the development methodology, techniques and tools learned in this course will include the Extended Entity Relationship Model, the Relational Model, Relational algebra, calculus and SQL, database normalization, efficiency and indexing. Finally, techniques and tools for metadata management and archival will be presented.

For marketers, an understanding of how a consumer selects, purchases, uses and disposes of products and services is pertinent to successfully managing the marketing function.

In this course, you will learn about the role of consumer behaviour within marketing. We will discuss how this behaviour is shaped by the social and cultural environment, as well as a number of psychological factors.

You will learn about decision-making processes in consumption behaviour in different buying situations. This will develop your ability to integrate marketing processes at a higher level.

Concepts drawn from various disciplines such as psychology, economics and anthropology will be examined.

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.

The technologies used to produce solar cells and photovoltaic modules are advancing to deliver highly efficient and flexible solar panels. In this course you will explore the main PV technologies in the current market. You will gain in-depth knowledge about crystalline silicon based solar cells (90% market share) as well as other up and coming technologies like CdTe, CIGS and Perovskites. This course provides answers to the questions: How are solar cells made from raw materials? Which technologies have the potential to be the major players for different applications in the future?

This course is part of the Solar Energy Engineering MicroMasters Program designed to cover all physics and engineering aspects of photovoltaics: photovoltaic energy conversion, technologies and systems.

This course is about modeling and how computer models can support managerial decision making. A model is a simplified representation of a real situation and modeling is the process of developing, analyzing and interpreting a model in order to help make better decisions. Models can be invaluable tools in managing and understanding the complexity and risk inherent in many business problems. As a result, models have become an increasingly important part of business at all levels from daily operations to strategic decision making.

This course will help learners become intelligent users and consumers of these models. To this end, we will cover the basic elements of modeling – how to formulate a model and how to use and interpret the information a model produces. The course emphasizes “learning by doing” so that students will be expected to formulate, solve, and interpret a number of different optimization and simulation models using Excel spreadsheets. An important theme in the course is to understand the appropriate use of models in business and the potential pitfalls from using models incorrectly or inappropriately.

The course has two distinct parts:

• The first half of the course we will cover supervised learning techniques for regression and classification. In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization. Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory.
• In the second half, we shift to unsupervised learning techniques. In these problems the end goal less clear-cut than predicting an output based on a corresponding input. We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.

Marketers want to understand and forecast how customers purchase products and services and how they respond to marketing initiatives.

Learn how analytics help businesses drive marketing to maximize its effectiveness and optimize return on investment (ROI).

In this course, part of the Business Analytics MicroMasters program, discover how to develop quantitative models that leverage business data, statistical computation, and machine learning to forecast sales and marketing impact for:

• customer relationship management;
• market segmentation;
• value creation;
• communication;
• monetization.

You will learn how to use probabilistic models and optimization tools to model customer demand forecasts, pricing sensitivity, Lifetime Value and how to leverage such data to make optimal decisions on designing new products, marketing segmentation and strategy.

Firms such as Apple, Alibaba, Facebook, SalesForce, Uber and Yelp operate as platform ecosystems that match buyers and sellers, gain value and market share from network effects, and harness their users to innovate.

This course teaches you how to convert products to platforms and how to generate platform innovation. You will learn how to negotiate platform startup, convert existing businesses, and make vital decisions on issues of openness, cannibalization, and competition.

You will discover how to apply concepts from two sided networks, information asymmetry, pricing, intellectual property, and game theory to real problems.

This course is taught by the instructor who literally wrote the book on this topic, “Platform Revolution: How Networked Markets Are Transforming the Economy—and How to Make Them Work for You.”

This course is part of both the Digital Leadership and Product Management MicroMasters programs.

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. 

How do robots climb stairs, traverse shifting sand and navigate through hilly and rocky terrain?

This course, part of the Robotics MicroMasters program, will teach you how to think about complex mobility challenges that arise when robots are deployed in unstructured human and natural environments.

You will learn  how to design and program the sequence of energetic interactions that must occur between sensors and mechanical actuators in order to ensure stable mobility. We will expose you to underlying and still actively developing concepts, while providing you with practical examples and projects. 

As Cloud Computing shapes businesses of all sizes, it is vital to understand the technologies behind cloud infrastructure, both public and private.

In this course, part of the Cloud Computing MicroMasters program, you will learn to evaluate and compare cloud systems, technologies and providers. In doing so, you will build an understanding of the concepts of elasticity and availability through cloud orchestration.

Some industry leading cloud platforms will be covered in this class, including: Amazon Web Services, VMware vSphere, Microsoft Azure, Google Cloud, and OpenStack. You will use the built-in tools and management consoles within those platforms to configure and manage the infrastructure.