This course is part of the Statistics with R Specialization. This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization.
You will be guided through installing and using R and RStudio free statistical softwareand will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.
Duke University has about 13, undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world. This course introduces you to sampling and exploring data, as well as basic probability theory. You will examine various types of sampling methods and discuss how such methods can impact the utility of a data analysis.
The concepts in this module will serve as building blocks for our later courses. Each lesson comes with a set of learning objectives that will be covered in a series of short videos. There will be weekly quizzes designed to assess your learning and mastery of the material covered that week in the videos. In addition, each week will also feature a lab assignment, in which you will use R to apply what you are learning to real data.
There will also be a data analysis project designed to enable you to answer research questions of your own choosing. One of the most rewarding aspects of a Coursera course is participation in forum discussions about the course materials. Please take advantage of other students' feedback and insight and contribute your own perspective where you see fit to do so. Thank you for joining the Introduction to Probability and Data community!
Say hello in the Discussion Forums. We are looking forward to your participation in the course. Welcome to Introduction to Probability and Data! I hope you are just as excited about this course as I am! In the next five weeks, we will learn about designing studies, explore data via numerical summaries and visualizations, and learn about rules of probability and commonly used probability distributions. Welcome to Week 2 of Introduction to Probability and Data! Hope you enjoyed materials from Week 1.
This week we will delve into numerical and categorical data in more depth, and introduce inference. Welcome to Week 3 of Introduction to Probability and Data! Last week we explored numerical and categorical data. Thank you for your enthusiasm and participation, and have a great week!
Great work so far! Welcome to Week 4 -- the last content week of Introduction to Probability and Data! This week we will introduce two probability distributions: the normal and the binomial distributions in particular. As usual, you can evaluate your knowledge in this week's quiz.
There will be no labs for this week.This course gives you easy access to the invaluable learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life.
This course can be taken independent of, concurrent with, or prior to, its companion course, Mindshift. Learning How to Learn is more learning focused, and Mindshift is more career focused. Founded inMcMaster University is committed to creativity, innovation, and excellence by inspiring critical thinking, personal growth, and a passion for learning. UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.
Introduction to Probability and Data
News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory. Although living brains are very complex, this module uses metaphor and analogy to help simplify matters.
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You will discover several fundamentally different modes of thinking, and how you can use these modes to improve your learning. You will also be introduced to a tool for tackling procrastination, be given some practical information about memory, and discover surprisingly useful insights about learning and sleep. Chunks are compact packages of information that your mind can easily access. In this module, we talk about two intimately connected ideas—procrastination and memory.
Building solid chunks in long term memory--chunks that are easily accessible by your short term memory—takes time. This is why learning to handle procrastination is so important. Ultimately, you will learn more about the joys of living a life filled with learning! I enjoyed this course so much. I learned a lot about how I can become a better learner. This course was very interesting and useful. I have some tools that I can use to optimize my learning potential. This is a course which I enjoyed.
It gave a good insight of the learning methodologies which we have often heard of but not given due importance.In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.
The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world. This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R.
The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.
Welcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions.
We also introduce the first programming assignment for the course, which is due at the end of the week. We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice. This week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed.
The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R. Excellent course! Very challenging, but good course. I've been programming in R for over a year, but there were still some things for me to pick up in this class. Assignments were a challenge, but satisfying to tackle. Highly recommended! Excelente opportunity to learn a lot. The course is very well prepared introduce you to R programing.Gain the in-demand skills you need to break into a new career field like information technology or data science.
No prior experience is required to get started. Overview Your launchpad to a career in IT. This program is designed to take beginner learners to job readiness in about six months. This program is for Anyone that wants to start a new career in IT support.
No experience is necessary. There are 27 interactive, hands-on labs that allow you to practice using the skills you've learned throughout the program. Overview Gain the latest job-ready skills and techniques covering a wide array of data science topics.
Complete hands-on projects in the IBM Cloud using real data science tools and real-world data sets. This program is for Anyone interested in developing skills and experience to pursue a career in Data Science. Prerequisites No prior computer science, programming, or statistics knowledge required to get started. Be ready to get a job teaching English!
This program is for This course is designed for anyone who wants to teach English anywhere in the world. If you want to become a teacher, polish your teaching skills, or add a credential to your resume, this is the course for you! Get certified by a nationally-ranked university. Related Job Roles Teacher in a school or language institute, online teacher, literacy mentor for a non-native English speaker, English tutor, English specialist for refugees.How Somalia’s Pirates Make Money
If you are looking for a position in an established school, they often require a bachelor's degree in addition to a TESOL Certificate. The professional portfolio includes a teaching philosophy, 10 lesson plans, 10 micro videos, and 2 teacher tips.
Overview Customer Engagement is the perfect entry point to start your career in IT, with a multitude of job openings ranging from onsite or remote help desk work to customer care or client support.
This program is for Anyone that wants to launch a new career in customer support. Build your skills and advance your career in high-demand fields like cloud architecture, applied AI, and more.
Many programs also provide a pathway to industry-recognized certification. Overview You'll learn how to program with Python with no previous knowledge of coding required, how to use Python to automate common system administration tasks, and learn how to use Git and GitHub to troubleshoot and debug complex problems. Command line Linux recommended. Overview Launch or advance your career in cloud architecture.
Learn to design, develop, and manage cloud solutions. Prepare for the Professional Cloud Architect certification. This program is for Engineers looking to advance their career in cloud architecture and learners interested in preparing for the Google Cloud Professional Cloud Architect certification exam. Overview By the end of this comprehensive 6 course Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career in AI.
This program is for This Professional Certificate is suitable for learners from a variety of backgrounds, including students looking to enter the workforce and existing professionals looking to future proof themselves with in-demand AI skills.Edtech firms see money in live streaming classes. Teachers learn a fresh lesson from Edtech firms. All rights reserved. For reprint rights: Times Syndication Service. Tech and Gadgets. Market Watch. Pinterest Reddit. By Sanghamitra Kar.
Edtech companies, including Coursera and Udacity, are caught in a situation where developers and other professionals are sharing paid content from these sites among their peer networks and groups where colleagues or friends avail courses for free. These links are shared in a password-protected manner. This appears to have become a common mode of sharing, especially among coders.
With reskilling becoming a significant career booster, edtech companies have seen a dramatic increase in uptake of relevant courses from IT companies as well as startups. Of course, piracy of content hampers some part of the business.
He said there are many sites which allow users access to such content for free. However, these sites are also infected at times, and therefore risky, said experts. Coursera and Udemy did not respond to queries emailed by ET. Read more on Abhay Sharma. Follow us on. Download et app. Become a member. To see your saved stories, click on link hightlighted in bold. Fill in your details: Will be displayed Will not be displayed Will be displayed.
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University of Michigan. Specialization 5 Courses. Data Science: Foundations using R. Johns Hopkins University. Google IT Support. Professional Certificate. IBM Data Science. Deep Learning. Data Science. Specialization 10 Courses.
University of Illinois. University of Pennsylvania. Most Popular Courses. The Science of Well-Being. Yale University. Programming for Everybody Getting Started with Python. Career Success. University of California, Irvine. Successful Negotiation: Essential Strategies and Skills. Machine Learning. Stanford University. Learning How to Learn: Powerful mental tools to help you master tough subjects.Or look back at a pile of unfinished projects that all got too messy and frustrating to work on?
I definitely have a stack of them. Long lost ideas that seemed simple to at the beginning. Or the more common one. Or you get your new shop feature in and realize right before you release that it broke your login system. And at some points in my career it felt that way. Wondering, was I doing it right?
Is this even the right pattern for what I want to do? Is there a better way? Dramatically over-complicating them. All the while, adding to the pain. Making my projects harder to ship, harder to work on, and definitely harder to love. Projects built almost completely in 1 file. When we first start out, we learn to write code and the computer just listens. I had no idea how wrong they were, or what the hidden costs of these bad practices were.
When I learned to code I was mostly on my own. Self taught from books and practice, with a couple little courses here and there. But mostly, I learned by doing.
So when I started my first professional game dev job, I went in with close to no idea what I was doing. And on day one got the shock of my life. An assignment to build some new crazy tool to handle content for a live AAA mmo.
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But I was thrown into the fire, still mostly unattended and with almost no architectural guidance. As a result, I built up some bad habits. And I made some huge messes in an already confusing code base. Instead of leaving things cleaner than I found them boyscout ruleI was adding shortcuts, breaking encapsulation, adding complexity.
And worst of all creating strange random bugs. Early on, Sony bought our project from Microsoft. But it took a few years before I got to jump into another code base. Think back to when you first discovered static or singletons. The same when I discovered events… I had no idea about the hidden costs of all the things I was doing. It usually stays pretty fast paced too. Then it hits.