Statistical Methods
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What you'll learn
The role of statistics in data analysis
R software using RStudio for creating numerical and graphical summaries
How to perform simple probability experiments and computer simulations
Skills you'll gain
- Exploratory Data Analysis
- Statistics
- Data Analysis
- Statistical Methods
- Statistical Programming
- Data Collection
- Data Visualization Software
- Probability & Statistics
- Statistical Visualization
- Probability Distribution
- Statistical Inference
- Simulations
- Statistical Analysis
- Statistical Reporting
- Probability
- Data Literacy
- Statistical Modeling
Tools you'll learn
Details to know
3 assignments
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There are 3 modules in this course
Build your statistics and probability expertise with this short course from the University of Leeds.
The first week introduces you to statistics as the art and science of learning from data. Through multiple real-life examples, you will explore the differences between data and information, discovering the necessity of statistical models for obtaining objective and reliable inferences. You will consider the meaning of "unbiased" data collection, reflecting on the role of randomization. Exploring various examples of data misrepresentation, misconception, or incompleteness will help develop your statistical intuition and good practice skills, including peer review. In the second week, you will learn and practice R software skills in RStudio for exploratory data analysis, creating graphical and numerical summaries. The final week will involve completing probability experiments and computer simulations of binomial trials, such as tossing a coin or rolling a die. This will help you develop an intuitive concept of probability, encompassing both frequentist and subjective perspectives. Throughout the course, you will acquire vital statistical skills by practicing techniques and software commands and engaging in discussions with fellow students. By the end of the course, you will be able to: - Understand and explain the role of statistical models in making inferences from data. - Implement appropriate tools for numerical and graphical summaries using RStudio, and interpret the results. - Evaluate the stability of frequencies in computer simulations through experimental justification and "measurement" of probability. No matter your current level of mathematical skill, you will find practical and real-life examples of statistics in action within this course. This course is a taster of the Online MSc in Data Science (Statistics) but it can be completed by learners who want an introduction to programming and explore the basics of Python.
This first week introduces you to statistics as the art and science of learning from data. You will learn how to recognise the difference between data and information and realise the need for statistical models to gain objective and reliable inferences. You will consider examples of datasets and reflect on suitable research questions that can be posed and answered using these data. You will see the importance of 'unbiased' data collection and learn about randomisation as a tool to achieve this. Various examples of data misrepresentation, misconception or incompleteness will help you develop statistical intuition and good practice skills. In the activities section, you are introduced to the peer review tool, which is a useful way for you to improve your statistical data analysis skills.
What's included
1 video14 readings1 assignment2 discussion prompts
1 videoβ’Total 6 minutes
- Overview of the short course with Dr Leonid Bogachevβ’6 minutes
14 readingsβ’Total 169 minutes
- About this courseβ’2 minutes
- Access to R and RStudioβ’2 minutes
- How to study on this courseβ’4 minutes
- Our sustainability commitment for your courseβ’10 minutes
- Lesson 1 overviewβ’2 minutes
- Statistics and Data Analysisβ’15 minutes
- Statistical inference and probability modelsβ’20 minutes
- Case studies: Inference from dataβ’15 minutes
- Be mindful about your data!β’20 minutes
- Lesson 2 overviewβ’2 minutes
- Ethics in statistics and data scienceβ’10 minutes
- Research activities before completing the discussion boardβ’60 minutes
- Possible answers for the discussion 'What is wrong with these data scenarios?'β’5 minutes
- Next week on the courseβ’2 minutes
1 assignmentβ’Total 30 minutes
- Week 1 Graded Quiz: Basic concepts of statistical data analysisβ’30 minutes
2 discussion promptsβ’Total 90 minutes
- What's wrong with these data scenarios?β’60 minutes
- The data scenarios you exploredβ’30 minutes
Week 2 gives you the opportunity to learn and practise your R skills in exploratory data analysis by producing numerical and graphical summaries of a variety of datasets. You learn to distinguish between different types of data (categorical vs numerical) and to use appropriate numerical and graphical summaries. You also gain experience in distinguishing between 'normal' and skewed data using box plots and histograms. This week offers a substantive task in RStudio to complete.
What's included
1 video10 readings1 assignment1 discussion prompt1 ungraded lab
1 videoβ’Total 10 minutes
- Demonstration: Basics of RStudio for data summariesβ’10 minutes
10 readingsβ’Total 131 minutes
- Lesson 3 overviewβ’2 minutes
- Types of dataβ’15 minutes
- Graphical summaries of dataβ’20 minutes
- Numerical summaries of dataβ’20 minutes
- Shape of data distributionβ’15 minutes
- Useful tips for RStudio and some R commands β’10 minutes
- R code for numerical and graphical summaries β’25 minutes
- Lesson 4 overviewβ’2 minutes
- Instructions for your first RStudio Lab tasksβ’20 minutes
- Next week on the courseβ’2 minutes
1 assignmentβ’Total 30 minutes
- Week 2 Graded Quiz: Numerical and graphical summaries of dataβ’30 minutes
1 discussion promptβ’Total 20 minutes
- Sharing your outputs from RStudio Lab 1β’20 minutes
1 ungraded labβ’Total 60 minutes
- RStudio Lab 1: Hands-on Data Summariesβ’60 minutes
This final week gives you the opportunity to explore a remarkable stability of frequencies as an experimental support of the concept of probability. You apply your R skills and conduct computer simulations of repeated random trials (e.g. tossing a coin or rolling a dice). Based on these observations, you develop an intuitive concept of probability (frequentist and subjective). You share your findings on a discussion board or 'forum' to discuss long-term experiments as a way to 'measure' probability of various events of interest (e.g. long runs of 6 in dice rolls, or tied birthdays in a class of students).
What's included
1 video6 readings1 assignment1 discussion prompt1 ungraded lab
1 videoβ’Total 5 minutes
- Demonstration: Stability of frequenciesβ’5 minutes
6 readingsβ’Total 41 minutes
- Lesson 5 overviewβ’2 minutes
- Computer simulations of random experimentsβ’20 minutes
- Lesson 6 overviewβ’2 minutes
- Instructions for your second RStudio lab tasksβ’10 minutes
- Short course summaryβ’5 minutes
- Course complete β Do you want to take your studies further?β’2 minutes
1 assignmentβ’Total 30 minutes
- Week 3 Graded Quiz: Data Summaries, statistical inference and margins of errorβ’30 minutes
1 discussion promptβ’Total 30 minutes
- Sharing your outputs from Lab 2β’30 minutes
1 ungraded labβ’Total 60 minutes
- RStudio Lab 2: Let's Measure Probabilityβ’60 minutes
Prepare for a degree
Taking this course by University of Leeds may provide you with a preview of the topics, materials and instructors in a related degree program which can help you decide if the topic or university is right for you.
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Reviewed on Aug 19, 2024
Few videos - but that's fine - and plenty of hands-on R, also fine. Less talking head and more participation is great. A short course, I would have liked longer.
Frequently asked questions
This course is a taster to MSc Data Science (Statics) on Coursera. Completion of the course will not give you credit towards this programme. The course can be completed independently by any learners interested in programming and learning the basics of Python.
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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Financial aid available,
