UCT GSB - Doing Clinical Research: Biostatistics with the Wolfram Language
- Offered byCoursera
Doing Clinical Research: Biostatistics with the Wolfram Language at Coursera Overview
Duration | 15 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Doing Clinical Research: Biostatistics with the Wolfram Language at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level
- Approx. 15 hours to complete
- English Subtitles: English
Doing Clinical Research: Biostatistics with the Wolfram Language at Coursera Course details
- 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.
- We live in a wonderful age with great tools at our disposal, ready to achieve this goal. None are quite as easy to learn, yet as powerful to use, as the Wolfram Language. Knowledge is literally built into the language. With its well-structured and consistent approach to creating code, you will become an expert in no time.
- This course follows the modern trend of learning statistical analysis through the use of a computer language. It requires no prior knowledge of coding. An exciting journey awaits. If you wanting even more, there are optional Honors lessons on machine learning that cover the support in the Wolfram Language for deep learning.
Doing Clinical Research: Biostatistics with the Wolfram Language at Coursera Curriculum
Week 1
Welcome
The Klopper Research Group
Assumptions
Learning a computer language
Why the Wolfram language?
Getting Mathematica
The new Wolfram Cloud
The Wolfram Cloud
The Wolfram Programming Lab
Free-form input and Wolfram Alpha in the Cloud
Mathematica
Free-form input and Wolfram Alpha in the desktop
Help and documentation
Assignment notebooks
How this Course Works
Welcome to Module 1
Meet the Course Instructor
Module 1 Notebook
Welcome to Wolfram Cloud
Welcome to Module 3
Module 3 Notebook
Module 3 Exercise
Week 2
Create Your Own Computational Essay
Simulated data demonstration - part 1
Simulated data demonstration - part 2
Simple arithmetic
Addition and subtraction
Multiplication and division
Powers
Arithmetical order
Calculating a mean
Working with data
Lists part 1
Lists part 2
Tables
Index
Datasets
Selecting
Dataset functions
Creating lists from datasets
Spreadsheets
Spreadsheets in the cloud
Welcome to Module 4
Module 4 Notebook
Welcome to Module 5
Module 5 Exercise
Welcome to Module 6
Module 6 Notebook
Module 6 Exercise
Coronavirus data analysis
Modules 1 to 5
Week 3
Summary Statistics
Descriptive statistics
Data import for descriptive statistics
Creating lists for descriptive statistics
Point estimates
Measures of dispersion
Data Visualization
Data import for visualization
Scatter plots
Box plots
Histograms
Bar and pie charts
Distributions
Probability
PDF and CDF
Discrete distributions
Continuous distributions
Sampling distributions
Simulated data
01: Introduction to neural networks
02: Introduction to machine learning
03: The fundamentals
04: Basic framework of a neural network
05: Layers in a neural network
06: Reviewing a neural network
07: From inputs to predictions
08: Finding a solution
Welcome to Module 7
Module 7 Notebook
Module 7 Exercise
Welcome to Module 8
Module 8 Notebook
Module 8 Exercise
Welcome to Module 9
Module 9 Notebook
Module 9 Exercise
Neural networks in the Wolfram language
Modules 6 to 9
Honors: Deep learning basics
Week 4
Inferential Statistics
Linear regression
Importing data
Descriptive statistics and visualization
Linear model
Comparing means
Data import
Comparing two means
Comparing more than two means
Comparing categorical variables
Contingency tables
Chi-squared test
Creating a Computational Essay
Data import
Main research question
Secondary research questions
Congratulations on reaching the end
09: Introduction to Wolfram Language machine learning
10: Automated Machine Learning
11: Running an automated algorithm
12: Testing the automated algorithm
13: Setting the method to neural network
14: Normalizing the data
15: Manually created neural networks
16: Regression - part 1
17: Regression - part 2
18: Regression - part 3
Welcome to Module 10
Module 10 Notebook
Module 10 Exercise
Welcome to Module 11
Module 11 Notebook
Module 11 Exercise
Welcome to Module 12
Module 12 Notebook
Module 12 Exercise
Welcome to Module 13
Module 13 Notebook
Final Exam Instructions
Continuing your journey with deep neural networks
Modules 10 to 13
Final Exam
Honors: Deep learning functions
Doing Clinical Research: Biostatistics with the Wolfram Language at Coursera Admission Process
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