Possible answers for the discussion: What is wrong with these data scenarios?
In this reading, you can find possible answers prepared by your tutor for the six data scenarios described in the discussion board at the end of Activity 1. 1. Car …
Course: Statistical Methods
Research activities before completing the discussion board
Your findings from this research activity feed into the post you will prepare for the following discussion forum. Each of the two research activities should take you about 30 minutes …
Ethics in statistics and data science
Data scientists need to apply ethical principles to their work. Here, you are introduced to key ideas you would need to consider in any data scientist role because decisions using …
Activity 3 overview
Data scientists must adhere to ethical principles in their work, and in Activity 3 you start by considering data privacy, security and data-driven decisions – including legislation for these issues. …
Be mindful about your data!
In this step we will examine some real-life cases that illustrate some of the caveats we should be wary of when handling data, including possible distortion, bias, misrepresentation or misconception …
Case studies: Inference from data
Previously, we have established the value of using statistics in data science. Here, you explore four case studies and reflect on what research questions the data may help to answer. …
Statistical inference and probability models
Previously, you learned that drawing your conclusions from data is called statistical inference. In this step, you learn how probability models for statistical inference help you to deal with data …
Activity 1 overview
Learning outcomes By completing the Week 1 course material, you will be better able to: Identify issues of bias and distortion in examples of datasets. Explain the role of statistical …
How to study on this course
In this course, there are videos, readings and quizzes. The videos are intended to introduce ideas and concepts before the material is covered in more detail in the readings. The …
Access to R and RStudio
Data science involves extensive data handling, data analysis and coding. The main programming environments that data scientists are expected to know are Python and R. Both are excellent but have …
About this course
This course introduces you to the fundamentals of probability and statistical methods as a tool used in statistical analysis and data science. It is for you if you are new …