In this course we use a real data-set to practice what is taught in each of the lecture videos. By using real data you will get "hands on" practice so that you are able to apply what you have learnt to your work or life needs.
Although gender wage trends may not be a relevant topic for you, we use wage data to demonstrate how to execute and apply data analysis techniques. By the end of this course you will be able to explain why some females earn less than males and be able to identify (and measure) where gender wage discrimination is taking place and where not. This ability can be applied to any industry or research topic.
The techniques taught in this course are all executable in Microsoft Excel and will help you improve your current analysis skills. The topics cover:
- Using averages and means
- Using counts and medians
- Data visualizations (graphs and plots)
- Correlations and scatter-plots
- Histograms to describe the shape of data
- How to easily understand and use variance and standard deviation
- How to construct and use "confidence intervals"
- Hypothesis testing
- Using t-tests to prove or disprove an assumption
For all of the above we replicate the technique in Excel and practice drawing insights about what we are seeing.
The course begins by introducing you to some basic theory about data and variables. You will then be introduced to some Excel tips and tricks (if you want) and the data-set that will be used. The data is a sample of over 500 survey responses that includes information about income, employment, education, gender, race, age and industry.
We then dive into the different techniques until we can statistically prove, with a high level of confidence, where wage discrimination is taking place (or not).
I hope you find this course rewarding, interesting and challenging!
- High school mathematics and some basic statistics
- Some experience with Microsoft Excel
- Apply the right techniques for the type of data
- Compare data by using averages, medians and modes
- Further analyse data by its shape using histograms and other data visualizations
- Measure and describe the spread of data around the average
- Measure the strength of a relationship between two variables
- Construct confidence intervals to describe how trustworthy and an average really is
- Complete a t-test to compare how similar or different two sets of data are
- Learn how to analyse wage data and identify gender discrimination