This course has a singular and clear aim, to empower you to do statistical tests, ready for incorporation into your dissertations, research papers, and presentations. The ability to summarize data, create plots and charts, and to do the tests that you commonly see in the literature is a powerful skill indeed. Not only will it further your career, but it will put you in the position to contribute to the advancement of humanity through scientific research.
Doing Clinical Research: Biostatistics with the Wolfram Language
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Doing Clinical Research: Biostatistics with the Wolfram Language
Кейптаунский университет
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Learn to use the Wolfram Language to summarize data and create plotsLearn to use the Wolfram Language to do common statistical tests
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Начальный уровень
Прибл. 10 часов на выполнение
Предполагаемая нагрузка: Four weeks of study, at approximately five hours per week.
Английский
Субтитры: Английский
Программа курса: что вы изучите
2 ч. на завершение
Week 1
This first week establishes the aims of the course and motivation for using the Wolfram Language. We aim to support you in gaining a remarkable new set of skills for doing statistical analysis that you can continue to use long after you complete the course. We will also describe the process of procuring the software that you will use in the course. The first is the absolutely free version, which is software as a service, meaning it runs in any web browser. The second is the desktop version. If you work or study at an institution with a site licence, you will be able to get the software for free. There is also the option to purchase your own licence.
2 ч. на завершение
14 видео ((всего 54 мин.)), 8 материалов для самостоятельного изучения
14 видео
Welcome6мин
The Klopper Research Group1мин
Assumptions1мин
Learning a computer language1мин
Why the Wolfram language?7мин
Getting Mathematica5мин
The new Wolfram Cloud1мин
The Wolfram Cloud1мин
The Wolfram Programming Lab8мин
Free-form input and Wolfram Alpha in the Cloud3мин
Mathematica27
Free-form input and Wolfram Alpha in the desktop7мин
Help and documentation3мин
Assignment notebooks4мин
8 материалов для самостоятельного изучения
How this Course Works10мин
Welcome to Module 110мин
Meet the Course Instructor10мин
Module 1 Notebook10мин
Welcome to Wolfram Cloud10мин
Welcome to Module 310мин
Module 3 Notebook10мин
Module 3 Exercise10мин
3 ч. на завершение
Week 2
In week 2, we start with some actual coding, now that you know about the Wolfram Language and its different coding environments. We start off with a demonstration of a completed project. It is just a little teaser, showcasing what you will be able to do at the end. Next, we are going to learn to code by doing simple arithmetic. That is simple addition, subtraction, multiplication, and so on. Once you have realized just how simple these tasks are, you will be introduced to the way in which data is stored in a computer language. These are the stepping stone required to bringing in your own data, ready for the analyses in the following weeks.
3 ч. на завершение
20 видео ((всего 100 мин.)), 8 материалов для самостоятельного изучения, 1 тест
20 видео
Simulated data demonstration - part 11мин
Simulated data demonstration - part 27мин
Simple arithmetic30
Addition and subtraction5мин
Multiplication and division9мин
Powers5мин
Arithmetical order2мин
Calculating a mean5мин
Working with data40
Lists part 17мин
Lists part 23мин
Tables7мин
Index10мин
Datasets9мин
Selecting6мин
Dataset functions3мин
Creating lists from datasets2мин
Spreadsheets6мин
Spreadsheets in the cloud4мин
8 материалов для самостоятельного изучения
Welcome to Module 410мин
Module 4 Notebook10мин
Welcome to Module 510мин
Module 5 Exercise10мин
Welcome to Module 610мин
Module 6 Notebook10мин
Module 6 Exercise10мин
Coronavirus data analysis10мин
1 практическое упражнение
Modules 1 to 5
5 ч. на завершение
Week 3
In week 3, its time to start analyzing data, now that you can write some code and import your data. The two most important steps to understand the message hidden in data, are to summarize and visualize it. Descriptive statistics turn rows and columns of data into something that we as humans can understand. By summarizing values and replacing them with single values, we start to get an idea of what our analyses might show. Visualizing the data is an even better way of getting to grips with data. Box-and-whisker plots, scatter plots, bar charts, and the like are wonderful ways to augment your understanding of the data. The Wolfram Language makes summary statistics easy but it really shines when creating plots. There are almost no limits to customizing plots. No matter what your project requirements, you will learn to create plots that work for you. Starting this week is an optional Honors lessons that introduce machine learning using the Wolfram Language.
5 ч. на завершение
27 видео ((всего 153 мин.)), 10 материалов для самостоятельного изучения, 2 тестов
27 видео
Descriptive statistics49
Data import for descriptive statistics5мин
Creating lists for descriptive statistics8мин
Point estimates10мин
Measures of dispersion6мин
Data Visualization39
Data import for visualization2мин
Scatter plots10мин
Box plots3мин
Histograms5мин
Bar and pie charts6мин
Distributions1мин
Probability8мин
PDF and CDF3мин
Discrete distributions7мин
Continuous distributions6мин
Sampling distributions6мин
Simulated data6мин
01: Introduction to neural networks46
02: Introduction to machine learning6мин
03: The fundamentals25
04: Basic framework of a neural network10мин
05: Layers in a neural network11мин
06: Reviewing a neural network4мин
07: From inputs to predictions6мин
08: Finding a solution10мин
10 материалов для самостоятельного изучения
Welcome to Module 710мин
Module 7 Notebook10мин
Module 7 Exercise10мин
Welcome to Module 810мин
Module 8 Notebook10мин
Module 8 Exercise10мин
Welcome to Module 910мин
Module 9 Notebook10мин
Module 9 Exercise10мин
Neural networks in the Wolfram language10мин
2 практических упражнения
Modules 6 to 9
Honors: Deep learning basics1ч
5 ч. на завершение
Week 4
This final week covers all the common statistical tests - going from Student's t-test to analysis of variance to chi-squared tests. We conclude the course with a run-through of the demonstration research project that you saw at the beginning of week two. This brings together all the skills that you have acquired during the course and prepares you for the final exam. You will also have the opportunity to create your own computational essay, if you are not content with just working through the demonstration project. For those following the optional Honors lessons there is an introduction to deep learning using the Wolfram Language.
5 ч. на завершение
27 видео ((всего 121 мин.)), 13 материалов для самостоятельного изучения, 3 тестов
27 видео
Linear regression36
Importing data4мин
Descriptive statistics and visualization3мин
Linear model5мин
Comparing means17
Data import3мин
Comparing two means8мин
Comparing more than two means7мин
Comparing categorical variables21
Contingency tables7мин
Chi-squared test3мин
Creating a Computational Essay47
Data import7мин
Main research question6мин
Secondary research questions7мин
Congratulations on reaching the end20
09: Introduction to Wolfram Language machine learning25
10: Automated Machine Learning10мин
11: Running an automated algorithm4мин
12: Testing the automated algorithm3мин
13: Setting the method to neural network3мин
14: Normalizing the data7мин
15: Manually created neural networks8мин
16: Regression - part 14мин
17: Regression - part 22мин
18: Regression - part 37мин
13 материалов для самостоятельного изучения
Welcome to Module 105мин
Module 10 Notebook5мин
Module 10 Exercise5мин
Welcome to Module 115мин
Module 11 Notebook5мин
Module 11 Exercise5мин
Welcome to Module 125мин
Module 12 Notebook5мин
Module 12 Exercise5мин
Welcome to Module 1310мин
Module 13 Notebook10мин
Final Exam Instructions10мин
Continuing your journey with deep neural networks10мин
3 практических упражнения
Modules 10 to 13
Final Exam
Honors: Deep learning functions1ч
О Кейптаунский университет
The University of Cape Town is the oldest university in South Africa and is one of the leading research universities on the African continent. UCT has over 25 000 students, of whom 30% are postgraduate students. We offer degrees in six faculties: Commerce, Engineering & the Built Environment, Health Sciences, Humanities, Law, and Science. We pride ourself on our diverse student body, which reflects the many cultures and backgrounds of the region. We welcome international students and are currently home to thousands of international students from over 100 countries. UCT has a tradition of academic excellence that is respected world-wide and is privileged to have more than 30 A-rated researchers on our staff, all of whom are recognised as world leaders in their field. Our aim is to ensure that our research contributes to the public good through sharing knowledge for the benefit of society. Past students include five Nobel Laureates – Max Theiler, Alan Cormack, Sir Aaron Klug, Ralph Bunche and, most recently, J M Coetzee.
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Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
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Оплатив сертификацию, вы получите доступ ко всем материалам курса, включая оцениваемые задания. После успешного прохождения курса на странице ваших достижений появится электронный сертификат. Оттуда его можно распечатать или прикрепить к профилю LinkedIn. Просто ознакомиться с содержанием курса можно бесплатно.
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