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Learn Data Science & AI from the comfort of your browser, at your own pace with DataCamp’s video tutorials & coding challenges on R, Python, Statistics & more.

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Online Learning Analytics Courses through EDX

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Online Learning Analytics Courses through EDX

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What Is Analytics?

Analytics is simply the study of data, but in a world making use of machine learning and marketing algorithms, it has the potential to be a critical part of your organization, regardless of field. Humans are producing more data than they ever have before, but it's only now that we're able to glean anything meaningful from that data. It used to take weeks to parse through big data (sometimes months or years), but machines are delivering almost real time results. Our job is to know what to ask and how to evaluate the results.

What Can Analytics Do For My Organization?

Businesses are using analytics tools to map customer journeys. Organizations build google ads campaigns to drum up momentum for an initiative. Digital marketing experts are using analytics solutions to drive business decisions. Enterprise solutions are within reach of everyone now and not just a lucky few. It goes beyond watching the numbers in google analytics. Businesses and organizations can parse large amounts of unstructured data to make not only decisions about what's happening now but decisions about the future using real-time data. Predictive analytics opens doors for better, faster decision making with fewer risks.

Learn Analytics

If you want to make a career in analytics, you need more than Google analytics tools. You need far-reaching data science skills that can extrapolate unstructured data and help interpret the results. You can use search engines to your advantage, evaluate KPIs critical to your organization, and build a marketing platform with a bigger ROI and less expense. Learning how to leverage customer insights into a complete picture of how certain marketing decisions affects the bottom line can help businesses and organizations find the right customers and puts you in the most-valuable-employee seat.

Analytics Courses and Certifications

Consider your target field and your expertise niche. If you need to get started with the basics, Business Analytics Fundamentals from Babson College can get you started with analytics foundations explicitly applied to business decisions. You can also get an introduction to Big Data from the University of Adelaide. Targeted courses help build your toolbox for a variety of positions within the data science field. You can take courses specific to business, such as Demand and Supply analytics from Columbia, or specialize in the health industry with Doane University's Health Informatics Technology course. You can also learn how machine learning and deep learning are transforming analytics through courses that give you thorough introductions to data mining, IoT, and deep learning algorithms. There's even a full master's degree in analytics from highly ranked Georgia Tech.

Explore a Career in Analytics

Regardless of your field, analytics has broad-reaching applications. Analytics intelligence builds business automation, information architecture for organizations (including government) and has the potential to transform your industry. Move beyond Google analytics and learn the skills required for the fourth industrial revolution.

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Analytics is simply the study of data, but in a world making use of machine learning and marketing algorithms, it has the potential to be a critical part of your organization, regardless of field. Humans are producing more data than they ever have before, but it's only now that we're able to glean anything meaningful from that data.

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International Conference on Learning Analytics & Knowledge (LAK)

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International Conference on Learning Analytics & Knowledge (LAK)

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Society for Learning Analytics Research (SoLAR)

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International Conference on Learning Analytics & Knowledge (LAK) - Society for Learning Analytics Research (SoLAR)

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The International Conference on Learning Analytics & Knowledge is the premier research forum in the field, providing common ground for all stakeholders in the design of analytics systems to debate the state of the art at the intersection of Learning and Analytics.

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Hello! I have a question regarding registration for spring semester, someone who has taken HUDK 4011 please comment

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Please share any suggestions to improve this padlet!

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Please share any suggestions to improve this padlet!

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📊 Path to a free self-taught education in Data Science!

This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World.

In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind.

OSSU Data Science uses the report Curriculum Guidelines for Undergraduate Programs in Data Science as our guide for course recommendation.
What is Data Science Students who already know basic programming in any language can skip this first course Python for Everybody Introduction to Computer Science and Programming Using Python Introduction to Computational Thinking and Data Science The Algorithms courses are taught in Java. If students need to learn Java, they should take this course first Java Programming Algorithms, Part I Algorithms, Part II Database Management Essentials Data Warehouse Concepts, Design, and Data Integration Relational Database Support for Data Warehouses Business Intelligence Concepts, Tools, and Applications Design and Build a Data Warehouse for Business Intelligence Implementation MongoDB for Developers Learning Path Calculus 1A: Differentiation Calculus 1B: Integration Calculus 1C: Coordinate Systems & Infinite Series Essence of Linear Algebra Linear Algebra Multivariable Calculus Introduction to Probability Intro to Descriptive Statistics Intro to Inferential Statistics Tools for Data Science Data Science Methodology Data Science: Wrangling Machine Learning Intro to Machine Learning Mining Massive Datasets Process Mining
Some courses can be taken in parallel, while others must be taken sequentially. All of the courses within a topic should be taken in the order listed in the curriculum. The graph below demonstrates how topics should be ordered.

Python and R are heavily used in Data Science community and our courses teach you both. Remember, the important thing for each course is to internalize the core concepts and to be able to use them with whatever tool (programming language) that you wish.

You must share only files that you are allowed. Do NOT disrespect the code of conduct that you sign in the beginning of your courses.

You can open an issue and give us your suggestions as to how we can improve this guide, or what we can do to improve the learning experience.

You can also fork this project and send a pull request to fix any mistakes that you have found.

If you want to suggest a new resource, send a pull request adding such resource to the extras section. The extras section is a place where all of us will be able to submit interesting additional articles, books, courses and specializations.

OSSU's code of conduct.

We have a discord server! This should be your first stop to talk with other OSSU students. Why don't you introduce yourself right now?

Subscribe to our newsletter.

You can also interact through GitHub issues.

Add Open Source Society University to your Linkedin and Facebook profile!

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📊 Path to a free self-taught education in Data Science! This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World.

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The report offers a vital outline of the Education and Learning Analytics Software and Services Market, including everything from applications, classifications, definitions, to industry chain frameworks, and more. It highlights on the latest market trends and the essential market dynamics. The report entails a complete overview of the various sectors which propel the growth of the Education and Learning Analytics Software and Services Market like drivers, restraints, trends, and opportunities which transform the market in a negative or in a positive way. It also sheds light on the different segments and applications that in the future, may impact the market. All information mentioned in the report is based on historic milestones & the latest trends. Each segment has thoroughly been studied, covering the essential factors like the valuation of the market, the CAGR, market share, size of the market, growth potential, and various market dynamics.

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COVID-19 Impact On Education and Learning Analytics Software and Services Market Projection By Latest Technology, Global Analysis, Industry Growth, Current Trends And Forecast Till 2026

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The report offers a vital outline of the Education and Learning Analytics Software and Services Market, including everything from applications, classifications, definitions, to industry chain frameworks, and more. It highlights on the latest market trends and the essential market dynamics.

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How's the feeling when you study in Learning Analytics program? Excited? Difficullt? Please share it!

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How's the feeling when you study in Learning Analytics program? Excited? Difficullt? Please share it!

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Digital Learning | Educational Innovation Across Northwestern

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Perhaps you've heard the term "learning analytics." You may have heard it used in the same phrase with "big data" and "predictive analytics." But is this just one of those terms, like "synergy," that will someday define a decade? Or a fad, like Second Life, that will slowly whimper and die?

Learning analytics is a term for how the data generated by students as they learn are measured and analyzed. The newly developing field of learning analytics is centered on using that information to understand and develop educational processes. Researchers, educators, and policy makers are involved; the central actors are students. By utilizing learning analytics, faculty, staff, and students can identify risks, improve learning and course outcomes, and develop more effective teaching and learning techniques.

Learning analytics tools go beyond what is possible for an individual instructor to accomplish; they can track learners and learning across multiple sources and, at their best, can reveal information about student learning and course improvement opportunities.

The data used by learning analytics researchers can come from a wide variety of places, including the learning management system (Canvas at Northwestern), student information systems, course evaluation systems, even the recreation facilities. Once the data has been gathered and run through analysis programs, the researchers create reports that can enable action on the part of students, instructors, or administrators.

The significance of learning analytics is that they add another arrow to the quiver of tools for improving teaching and learning. Students can understand better how they learn and apply that knowledge to their career of study. Instructors can understand their students' learning process and act to improve course outcomes.

For many colleges, the first foray into learning analytics manifested itself as a way to identify at-risk students. Focusing on retention and graduation rates, they looked to the data for indicators that students were on a path that might lead to course failure and subsequent departure from higher education. Taking action - including personal outreach from faculty or reminders and study tips from the course management system - was a direct result of the data analyses and reports.

Beyond identifying at-risk students, learning analytics can be employed to help instructors understand whether their students are comprehending material in their courses. If the course management system shows that students are returning again and again to a topic or consistently missing a concept in multiple assessments, the instructor learns that these concepts need to be explained more clearly. Insights like these can help instructors as they redesign courses. If combined data show that students successfully pursue a major only if they have had extensive exposure to the topic outside of class, the department can adjust its program. If learning analytics suggest that students who did well in class X often do well in major Y, they've just helped to create a degree pathway.

We'll be looking in depth at learning analytics in a series of subsequent articles, which will focus on examples of its use at Northwestern in the classroom and beyond.

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Perhaps you've heard the term "learning analytics." You may have heard it used in the same phrase with "big data" and "predictive analytics." But is this just one of those terms, like "synergy," that will someday define a decade? Or a fad, like Second Life, that will slowly whimper and die?