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‎06_StatisticalInference/announcements.md‎

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## First Announcement
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Welcome to Week 1 of the Statistical Inference, part of the Coursera
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Data Science specialization from Johns Hopkins Biostatistics! This
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course presents the fundamentals of statistical inference that you
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will need throughout the rest of the Data Science track.
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We believe that the key word in Data Science is "science". Our course
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track is focused on providing you with three things: (1) an
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introduction to the key ideas behind working with data in a scientific
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way that will produce new and reproducible insight, (2) an
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introduction to the tools that will allow you to execute on a data
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analytic strategy, from raw data in a database to a completed report
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with interactive graphics, and (3) on giving you plenty of hands on
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practice so you can learn the techniques for yourself.
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This course represents the most fundamental and foundational
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component of the series. Using only a bare minimum of mathematics,
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we attempt to give students the fundamentals of using statistics
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to draw inferences about populations.
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We are excited about the opportunity to attempt to scale Data Science
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education. We intend for the courses to be self contained, fast paced,
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and interactive. We intend to run them frequently to give people with
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busy schedules the opportunity to work on material at their own pace.
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Please see the course syllabus for information about the quizes, the
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project, due dates, and grading. Don't forget to say hi on the message
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boards. The community developed around these courses is one of the
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best places to learn and the best things about taking a MOOC!
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Brian Caffo and the Data Science Track Team
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---
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## Week 1 Announcement
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Hi all, welcome to Week 1 of the Statistical Inference class.
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Please get the materials off of github. Also, make sure that you're
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keeping up with the videos and plan on taking the week 1 quiz.
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Get those forums going; we're looking forward to seeing some
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really active posting!
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Good luck and have a great week!
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Brian Caffo and the Data Science Track Team
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---
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## Week 2 Announcement
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Welcome to Week 2 of Statistical Inference!
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Make sure that you're keeping up with the videos and planning
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on taking the second quiz.
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Keep those forums rocking.
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Good luck and have a great week!
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Brian Caffo and the Data Science Track Team
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---
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## Week 3 Announcement
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Welcome to Week 3 of Statistical Inference!
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Make sure that you're keeping up with the videos and planning
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on taking the third quiz.
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Keep up with the forums and if you get a chance, send
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us pull requests with changes for the notes.
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Good luck and have a great week!
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Brian Caffo and the Data Science Track Team
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---
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## Week 4 Announcement
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Welcome to Week 4 of Obtaining Data!
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Make sure that you're keeping up with the videos and planning
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on taking the fourth quiz.
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Keep up with the forums and if you get a chance, send
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us pull requests with changes for the notes.
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Good luck and have a great week!
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Brian Caffo and the Data Science Track Team
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---
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## Course wrap-up
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Congratulations on finishing the Statistical Inference!
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We have set the grading and released the Statements of Accomplishment
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for the Course. It might take a few hours/days for the statements to
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be disbursed to accounts.
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A couple of other notes:
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* The course will begin again immediately starting in a couple of
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days. If you are still interested in keeping in touch with your
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fellow learners, please enroll in the new course and keep the conversation going. You may also be an invaluable resource for
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new course takers!
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* Keep your eye on Hopkins offerings from Coursera. All announcements about future offerings will be posted at: https://twitter.com/jhubiostat and http://simplystatistics.org/, http://twitter.com/simplystats.
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* If you liked this course, please consider taking some of the other course offerings through the Data Science Track. If you have completed all the course work in this track you now have the tools you will need to take on the challenges in the rest of our courses or in other Statistics, Data Science, or Machine Learning courses you may encounter.
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Thanks again for all of your efforts during the course of the class and best of luck in your career!
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Brian Caffo and the Data Science Track Team

‎06_StatisticalInference/grading.md‎

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## Grading and logistics
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The grading in this class is very easy.
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There are four quizzes, each containing in the neighborhood of 10 questions.
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Each question is equally weighted.
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Your total points is the sum of the questions across all quizzes that
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you answered correctly (using all of your quiz attempts).
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70% or more of the total points is a pass for the class.
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80% or more of the total points is a pass with distinction.
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‎06_StatisticalInference/syllabus.md‎

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## Course Title
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### Statistical Inference
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---
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## Course Instructor(s)
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The primary instructor of this class is
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[Brian Caffo](http://www.bcaffo.com)
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Brian is a professor at Johns Hopkins Biostatistics and
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co-directs the [SMART working group](http://www.smart-stats.og)
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This class is co-taught by Roger Peng and Jeff Leek. In addition,
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Sean Kross and Nick Carchedi have been helping greatly.
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---
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## Course Description
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In this class students will learn the fundamentals of statistical
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inference. Students will receive a broad overview of the goals,
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assumptions and modes of performing statistical inference. Students
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will be able to perform inferential tasks in highly targeted settings
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and will be able to use the skills developed as a roadmap for more
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complex inferential challenges.
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---
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## Course Content
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This class is taught in three modules
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1. Probability and probability distributions
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2. Basics of inference
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3. More advanced inference techniques
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Each module has sub modules, labeled such as 01_03. Videos within submodules are
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broken up so that 01_03_a is the first video in sub-module 3 in module 1
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while 01_03_b is the second video.
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For convenience we post the broken up videos, and then also the full videos
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for each sub-module on the site.
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The full list of topics are as follows
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Module 1, probability and probability distributions
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* 01_01 Introduction
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* 01_02 Probability
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* 01_03 Expectations
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* 01_04 Independence
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* 01_05 Conditional probability
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Module 2, basics of inference
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* 02_01 Common Distributions
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* 02_02 Asymptopia
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* 02_03 t confidence intervals
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* 02_04 Likelihood
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* 02_05 Beginning Bayes Inference
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Module 3, more advanced inference
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* 03_01 Independent group intervals
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* 03_02 Hypothesis testing
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* 03_03 P-values
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* 03_04 Power
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* 03_05 Multiple Testing
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* 03_06 resampled inference
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---
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Github repository
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The most up to date information on the course lecture notes will always be in the Github repository
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[https://github.com/DataScienceSpecialization/courses](https://github.com/DataScienceSpecialization/courses)
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Please issue pull requests so that we may improve the materials.
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---
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## Lecture Materials
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Lecture videos will be released weekly and will be available for the
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week and thereafter. You are welcome to view them at your
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convenience. Accompanying each video lecture will be a PDF copy of the
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slides and a link to an HTML5 version of the slides.
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The lecture videos are released in a weekly fashion. They do not
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correspond to the modules (as there's three modules and four weeks).
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---
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## Weekly quizzes
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The weekly quizzes will cover the material from that week.
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### Quiz 1
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Assigned: Class open (1st of Month)
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Due: 7th of the Month 12:00 AM UTC
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### Quiz 2
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Assigned: 8th of the Month 12:01 AM UTC
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Due: 14th of the Month 12:00 AM UTC
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### Quiz 3
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Assigned: 15th of the Month 12:01 AM UTC
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Due: 21st of the Month 12:00 AM UTC
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### Quiz 4
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Assigned: 22nd of the Month 12:01 AM UTC
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Due: 28th of the Month 12:00 AM UTC
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---
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## Quiz Scoring
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You may attempt each quiz up to 2 times. Only the score from your final attempt will count toward your grade.
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---
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## Hard deadlines and soft deadlines
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The reported due date is the soft deadline for each quiz. You may turn
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in quizzes up to two days after the soft deadline. The hard deadline
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is the Tuesday after the Quiz is due at 23:30 UTC-5:00. Each day late
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will incur a 10% penalty, but if you use a late day, the penalty will
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not be applied to that day.
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---
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## Late Days for Quizzes
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You are permitted 5 late days for quizzes in the course. If you use a late day, your quiz grade will not be affected.
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---
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## Dates for the project
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This class has no project unlike the other classes in the Data Science Series. (The content doesn't lend itself well to a project.)
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So be warned that there are more quiz questions here than in the other classes in the Data Science series.
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---
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## Typos
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* We are prone to a typo or two - please report them and we will try
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* to update the notes accordingly. In some cases, the videos may
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* still contain typos that have been fixed in the lecture notes. The
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* lecture notes represent the most up-to-date version of the course
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* material.
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---
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## Differences of opinion
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Keep in mind that currently data analysis is as much art as it is
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science - so we may have a difference of opinion - and that is ok!
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Please refrain from angry, sarcastic, or abusive comments on the
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message boards. Our goal is to create a supportive community that
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helps the learning of all students, from the most advanced to those
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who are just seeing this material for the first time.
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---
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## Technical Information
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Regardless of your platform (Windows or Mac) you will need a
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high-speed Internet connection in order to watch the videos on the
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Coursera web site. It is possible to download the video files and
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watch them on your computer rather than stream them from Coursera and
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this may be preferable for some of you.
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### Here is some platform-specific information:
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_Windows_
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The Coursera web site seems to work best with either the Chrome or the
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Firefox web browsers. In particular, you may run into trouble if you
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use Internet Explorer. The Chrome and Firefox browsers can be
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downloaded from: _Chrome:
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[http://www.google.com/chrome](http://www.google.com/chrome) _
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Firefox: [http://www.mozilla.org](http://www.mozilla.org)
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_Mac_
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The Coursera site appears to work well with Safari, Chrome, or Firefox, so any of these browsers should be fine.

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