Online courses directory (10358)

Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology.

Learn about the most effective data analysis methods to solve problems and achieve insight.

The Coursera course, Data Analysis and Statistical Inference has been revised and is now offered as part of Coursera Specialization “Statistics with R”. This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.

Data and visual analytics is an emerging field concerned with analyzing, modeling, and visualizing complex high dimensional data. This course will introduce students to the field by covering state­-of-­the-art modeling, analysis and visualization techniques. It will emphasize practical challenges involving complex real world data and include several case studies and hands-on work with the R programming language.

Want to study for an MBA but unsure of the basic data analysis still required? This online course prepares you for studying in an MBA program.

Data analysis appears throughout any rigorous MBA program and in today’s business environment understanding the fundamentals of collecting, presenting, describing and making inferences from data sets is essential for success.

The goal of this course is to teach you fundamental data analysis skills so you are prepared for your MBA study and able to focus your efforts on core MBA curriculum, rather than continually playing catch-up with the underlying statistical knowledge needed.

We also hope that learning these data analysis skills will equip you with the ability to understand, to a greater degree, the data you encounter in your working lives and in the world around you - an essential life-skill in today’s data driven environment

This course assumes no prior knowledge of data analysis. Concepts are explained as clearly as possible and regular activities give you the opportunity to practice your skills and improve your confidence.

This course is part of the MITx MicroMasters program in Data, Economics, and Development Policy (DEDP). To audit this course, click “Enroll Now” in the green button at the top of this page.

To enroll in the MicroMasters track or to learn more about this program and how it integrates with MIT’s new blended Master’s degree, go to MITx’s MicroMasters portal.

This statistics and data analysis course will introduce you to the essential notions of probability and statistics. We will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.

This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively, but it is challenging. Students who are uncomfortable with basic calculus and algebra might struggle with the pace of the class.

Exploratory data analysis is an approach for summarizing and visualizing the important characteristics of a data set. Promoted by [John Tukey](http://en.wikipedia.org/wiki/John_Tukey), exploratory data analysis focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, to consider how that data set came into existence, and to decide how it can be investigated with more formal statistical methods. If you're interested in supplemental reading material for the course check out the Exploratory Data Analysis book. (Not Required) This course is also a part of our Data Analyst Nanodegree.

Are you ready to leave the sandbox and go for the real deal? Have you followed EX101x Data Analysis: Take It to the MAX() and EX102x Data Analysis: Visualization and Dashboard Design and are ready to carry out more robust data analysis?

In this project-based course you will engage in a real data analysis project that simulates the complexity and challenges of data analysts at work. Testing, data wrangling, Pivot Tables, sparklines? Now that you have mastered them you are ready to apply them all and carry out an independent data analysis.

For your project, you firstly get one raw dataset, which you will turn into a dashboard, step by step. You will begin with a business question, and then touch upon different business domains, such as revenue management, planning, scheduling, management, investment, etc.

The progress of the project will be gradual – in the first weeks you will be guided by quizzes to make sure that you are on track. You will engage with creating first drafts of your work and discuss them with your peers.

In the final week, you will be in charge. You will get a second dataset and a number of questions to answer, and it is up to you to design and build the perfect dashboard.

This course will allow you to practice the skills you’ve gained in previous Data Analysis XSeries courses and prove you can be an indispensable asset in data-driven organizations.

By completing this project and EX101x and EX102x, you can obtain the XSeries Data Analysis Verified Certificate. 

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The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.

EX101x is for all of those struggling with data analysis. That crazy data collection from your boss? Megabytes of sensor data to analyze? Looking for a smart way visualize your data in order to make sense out of it? We’ve got you covered!

Using video lectures and hands-on exercises, we will teach you cutting-edge techniques and best practices that will boost your data analysis and visualization skills.

We will take a deep dive into data analysis with spreadsheets: PivotTables, VLOOKUPS, Named ranges, what-if analyses, making great graphs - all those will be covered in the first weeks of the course. After that, we will investigate the quality of the spreadsheet model, and especially how to make sure your spreadsheet remains error-free and robust.

Finally, once we have mastered spreadsheets, we will demonstrate other ways to store and analyze data. We will also look into how Python, a programming language, can help us with analyzing and manipulating data in spreadsheets.

EX101x is created using Excel 2013 and Windows. Most assignments can be made using another spreadsheet program and operating system as well, but we cannot offer full support for all configurations.

The goal of this course is to help you to overcome data analysis challenges in your work, research or studies. Therefore we encourage you to participate actively and to raise real data analysis problems that you face in our discussion forums. 

This course is part of the Data Analysis XSeries.

LICENSE
The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.

Struggling with data at work? Wasting valuable time working in multiple spreadsheets to gain an overview of your business? Find it hard to gain sharp insights from piles of data on your desktop?

If you are looking to enhance your efficiency in the office and improve your performance by making sense of data faster and smarter, then this advanced data analysis course is for you.

If you have already sharpened your spreadsheet skills in EX101x Data Analysis: Take It to the MAX(), this course will help you dig deeper. You will learn advanced techniques for robust data analysis in a business environment. This course covers the main tasks required from data analysts today, including importing, summarizing, interpreting, analyzing and visualizing data. It aims to equip you with the tools that will enable you to be an independent data analyst. Most techniques will be taught in Excel with add-ons and free tools available online. We encourage you to use your own data in this course but if not available, the course team can provide.

This course is part of the Data Analysis XSeries. 

LICENSE

The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.

In this free course Data Analytics – Introduction to Machine Learning you will learn about machine learning methods that help automate the analysis of data. These computing methods help find hidden insights and information within the data without being explicitly programmed where or what to search for within the data.<br /><br />This course begins by introducing you to supervised and unsupervised learning. You will learn how to distinguish between each type of learning and how to use them to analyse data. You will also learn about linear regression and how it can be used. The course introduces concepts about regularization and how to avoid over-fitting by using regularization.<br /><br />Next, you will learn about using Excel and Matlab to perform simple and multiple regression. You will learn about confidence levels and subset selections. You will learn how to distinguish between R² and adjustment R² and what they both measure. The course will finish by introducing what the K-NN approach is in data analytics and when this approach should be used. <br /><br />This course will be of great interest to professionals who work in the areas of data analytics and data science and who would like to learn more about methods used in machine learning. It will also be of interest to learners who are interested in computer science and would like to learn more about how machine learning gives computers the ability to learn without being explicitly programmed.

In this free data analysis course Data Analytics - Mining and Analysis of Big Data you will be introduced to the concept of big data and to a number of techniques that are used to analyse and interpet big data. <br /><br />The course begins by introducing you to big data and lists the four V’s of big data. You will learn about associative rule mining, and about when association can be applied and the patterns that arise in mining. <br /><br />Next, you will learn about clustering analysis. You will examine the difference between clustering and classification and the different types of clustering. You will also learn about K-means clustering and K-meloids.<br /><br />Finally, you will learn about online and active learning. You will learn about experimentation and the difference between an online and offline context of creating data. You will be introduced to the n-arm bandit problem and how to find solutions for the multi-arm bandit problem. <br /><br />This free online course will be of great interest to professionals involved in data science and data analysis and any learner who wants to learn more about analysing big data using mining and clustering techniques.

Please note that the verified certificate option is not currently open for this course. Please enroll in the audit track and you will be emailed when the verified certificate option is open for enrollment.

Businesses, consumers, and societies leave behind massive amounts of data as a by-product of their activities. Leading-edge companies in every industry are using analytics to replace intuition and guesswork in their decision-making. As a result, managers are collecting and analyzing enormous data sets to discover new patterns and insights and running controlled experiments to test hypotheses.

This course, part of the Analytics: Essential Tools and Methods MicroMasters program, prepares you to understand data and business analytics and become a leader in these areas in business organizations.

It covers the methodologies, algorithms, issues, and challenges related to analyzing business data. It will illustrate the processes of analytics by allowing you to apply business analytics algorithms and methodologies to real-world business datasets from finance, marketing, and operations. The use of real-world examples and cases places business analytics techniques in context and teaches you how to avoid the common pitfalls, emphasizing theimportance of applying proper business analytics techniques.

In addition to cases, this course features hands-on experiences with data collection, analysis, and visualization using Python programs and analytics software such as SAS.

This course includes a significant analytics project.

Many people talk about the promise of “big data” to health care. But how can the application of data analytics to big data actually improve health and health care? We will show that novel data analytics based solutions can result in better diagnosis, better care and better curing. This provides fertile ground for entrepreneurship and the development of new businesses.

In our course we’ll start from the very basics of data analytics, look at different real world approaches and help you to see entrepreneurial opportunities and develop a business plan.

We will cover three important fields:

• Health care expertise: We will present medical approaches to data and give an overview of challenges where big data based solutions have been developed to improve the efficiency and effectiveness in medicine.
• Data analytics: We’ll explain the basics of data mining within the context of a wide variety of health care settings, and the types of data and data analysis challenges that you will likely encounter in each. We’ll start with gathering the data, move on to classifying, analyzing and finally visualizing it.
• Entrepreneurship: You will learn how to assess when data sciences based improvements in health care represent entrepreneurial opportunities. The development of a rigorous business plan is used to help you make that assessment.

Participants with prior experience in the medical field will learn how novel data science applications can improve healthcare, create societal value and how to spot entrepreneurial opportunities.

Participants with experience in data science or mathematics will learn about medical approaches to data and why healthcare is an exciting area to apply and develop data analytics.

Participants interested in launching their startup will learn how big data solutions in health care can provide a solid basis to build great ventures.

Whatever your motivation to enrol in this course, we care about your project and your success - that’s why we will guide you through all parts of this learning journey step by step!

Enter now to see how you can engage in data driven innovation and make an impact on improving care, outcomes and the quality of life.

Is your data messy? Do you need to learn how to clean it up? In this computer science course, we will discuss the discipline of Data Quality Assurance and Data Quality Services (DQS). You will learn why your data needs cleansing, the capabilities and features of DQS, what a DQS solution looks likes and how data cleansing integrates with an Integration Services (SSIS) data flow. We will demonstrate a variety of critical data quality activities such as knowledge discovery, domain management, matching policies for the de-duplication of data, and administration topics covering installation, configuration and security.

6.263J / 16.37J focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.

The free online course Data Journalism and Media Standards introduces you to the methods used by journalists in gathering and utilizing online data, as well as the use of freedom of information requests for government and public records. The use of blogs and the rise of the citizen journalist are also explored.<br /><br />The course first introduces the ever-growing set of tools, techniques, and approaches to storytelling known as data journalism. You will learn how to find data on the web, how to request it using freedom of information laws, how to use screen-scraping to gather data from unstructured sources, and use crowd-sourcing to collect datasets from readers. You will learn how to present data using techniques such as data visualization and news applications. You will also learn about mining data and learn to recognize the four main obstacles to the use of data journalism. <br /><br />Next, you will learn about the role of the free press and the standards that are in place which describe the privileges and responsibilities of a free press in a free society. The laws, statutes, and codes that determine the conduct required of news organizations are explained. The framework of a free press is also explained, such as, what rights are essential, in order for journalists to do their jobs. You will also learn about censorship and regulation of the media. You will review how government licensing in some countries both determines who may be a journalist and circumscribes the parameters of acceptable reporting and commentary leading to self-censorship and stifling of dissent and debate.<br /><br />Finally, you are introduced to the tools and resources used to hold bloggers and citizen journalists to account. You will learn how access to electronic files presents special challenges such as being over-connected which can fragment and compartmentalize public opinion. You will also learn how information that is in the public domain cannot be the basis of an invasion of privacy suit. You will also learn about how discipline, order and planning are essential for successful investigative journalism, and how your work must be solidly researched, well written and verifiable.<br /><br />This course will be of great interest to journalists and students who would like to learn how gathering and presenting data can be used as key part of the journalistic process. The course will also be of interest to learners who want an insight into the ethics and laws governing journalism, and how they apply to both professional and citizen journalists.<br />

Learn critical concepts and practical methods to support research data planning, collection, storage and dissemination.

Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.

This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

Apply the learned algorithms and techniques for data mining from the previous courses in the Data Mining Specialization to solve interesting real-world data mining challenges.