About the content
In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.
- Week 1 - Hypothesis Testing and ANOVA
This session starts where the Data Management and Visualization course left off. Now that you have selected a data set and research question, managed your variables of interest and visualized their relationship graphically, we are ready to test those relations...
- Week 2 - Chi Square Test of Independence
This session shows you how to test hypotheses in the context of a Chi-Square Test of Independence (when you have two categorical variables). Your task will be to write a program that manages any additional variables you may need and runs and interprets a Chi-S...
- Week 3 - Pearson Correlation
This session shows you how to test hypotheses in the context of a Pearson Correlation (when you have two quantitative variables). Your task will be to write a program that manages any additional variables you may need and runs and interprets a correlation coef...
- Week 4 - Exploring Statistical Interactions
In this session, we will discuss the basic concept of statistical interaction (also known as moderation). In statistics, moderation occurs when the relationship between two variables depends on a third variable. The effect of a moderating variable is often cha...
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This is based on their previous course (Data Management and Visualization). This course is better in terms of explaining content clearly, and I enjoyed the real-life example used when explaining about the Chi Square test. However, the python coding could be more optimized; for example, it suggests doing the Chi Square post-hoc test for each variable one by one... which can be 15 batches of dictionary recodes! Thankfully someone in the forum provided a solution for doing an automated batch testing. Maybe the course lecturers felt that a batch recode would be too complicated, but it doesn't feel like you could use their method effectively for a work environment, either. In any case, it's still a good course to explain the various data tests for quantitative and categorical data if you're new to statistics.
Just love this whole specialisation! videos are great, lessons are great... it's just a great course! highly recommended to anyone looking to dive into the field of data analysis!
inactive forums with too many unanswered questions to get assistance by searching the forums. This course and program have potential, but it needs active moderators and mentors... otherwise it's definitely not worth the cost. Ended up switching to a Python-specific data science specialization, with a much more active forum.