Courses tagged with "Business" (1739)

Top chefs and Harvard researchers explore how everyday cooking and haute cuisine can illuminate basic principles in physics and engineering, and vice versa.

Super-Earths And Life is a course about alien life, how we search for it, and what this teaches us about our place in the universe.

If you need intuitive, interactive and high-performance access to your data—and especially if you have large volumes of data—then this course is for you.

Whether you're new to Analysis Services or are experienced with earlier versions, this course will teach you all the information you need to develop a tabular data model. You will learn how to import tables of data from different sources and relate them to form the foundation of your model. You will then learn how to enhance your model with usability features, such as hierarchies.

Next, you will learn how to enhance your model with business logic. You will learn how to create calculated columns, tables and measures using DAX, the language of tabular models.

Additional topics include how to manage a tabular database -- including table storage, processing, permissions and deployment -- and  how to deploy your tabular model to the cloud with Azure Analysis Services.

Finally, this course will teach you how to compare the two types of Analysis Services models: tabular and multidimensional. This will help you to determine the appropriate data model for your project.

By the end of the course, you will have designed, developed and deployed a tabular model, and you will be ready to deliver high-performance business user experiences.

The course includes comprehensive hands-on exercises that enable you to directly apply the lessons with sample data.

Note: To complete the hands-on elements in this course, you will require an Azure subscription. You can sign up for a free Azure trial subscription (a valid credit card is required for verification, but you will not be charged for Azure services). Note that the free trial is not available in all regions. It is possible to complete the course and earn a certificate without completing the hands-on practices.

Everyone - non-scientists and scientists alike - has some form of expertise, but communicating across a gap in knowledge or experience is challenging. In this Teach-Out, we address this challenge by helping participants to develop core communication skills and more effectively communicate with one another.

Participants will learn why science communication is both essential and fundamentally challenging. Experts in three broad areas of public engagement with science - Policy & Advocacy, Education & Outreach, and Science in the Media - will weigh in to share their knowledge. Anyone with a STEM story to tell will have the opportunity to develop a better understanding of their audience, craft a clear message, weave a compelling story, and practice giving and receiving feedback on science communications. And all participants will be able to engage in a series of discussions addressing key issues in science communication.

Because the practice of science encourages dialogue, requires diverse perspectives, and has no political agenda, we hope this Teach-Out is a useful step in encouraging more science conversations between individuals and their local, national, and global communities.

This course discusses the lessons learned by Michael Stonebraker and Andy Palmer during their start-up endeavors over a 30-year period. The lessons are distilled into six steps that any entrepreneur can follow to get a company going. 

Topics include the generation and assessment of ideas, the challenges of building a prototype, the recruitment of a talented team, the closing of the first financing round, and pursuing growth with the right business leadership.

This is a self-paced course ending on August 31, 2016. Participants can move through the lectures and materials at their own pace and once completed, a certificate of completion will be generated automatically. Please note that we will be moderating the discussion board about 3 times a week on average. This discussion board is meant to serve as a platform between participants to discuss the content of the course, to offer additional ideas and useful perspectives, and to get answers to questions from the moderators or other participants. We hope you find the discussion board useful.

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.