Course: Introduction to Data Analytics (techniques you can apply today)

Introduction to Data Analytics (techniques you can apply today)

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
About this Course

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!

Jef Jacobs

Basic knowledge
  • High school mathematics and some basic statistics
  • Some experience with Microsoft Excel
What you will learn
  • 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
Lectures quantity: 41
Common duration: 06:28:01
Beginner: Introduction to data and variables
  • Types of variables  
  • Types of data  
Introduction: The practice data we will use throughout this course
  • An overview of the data, variables and sample (including the downloadable file)  

    In this lecture we learn about the practice data we will be analyzing throughout this course. You can also download the attached file and extract the sample data. If you cannot extract the sample data then please send me a message and I will make sure you receive the data.

Beginner / Intermediate: Some Microsoft Excel tips and tricks to help you
  • How to select and navigate large tables of data in Excel  
  • How to quickly sort, filter and search for data in tables  
  • Using the =Average function in Excel  
  • Using the =AverageIF function in Excel  
  • Using the =AverageIFS function in Excel  
Beginner: Basic techniques for describing data
  • Using averages (means) to describe data  
  • Excel tip: how to calculate the average, median or mode  
  • How to describe categorical (non-numerical data) like gender, race or team  
  • Excel tip: creating bar graphs and pie chart to describe categorical data  
  • Data visualizations for averages and contributions  
  • Excel tip: comparing averages across different sub-categories (using =COUNTIFS)  
  • Using Scatter plots to illustrate trends or relationships between variables  
  • Excel tip: how to draw scatter plots in Excel  
  • Using box-plots to better illustrate and compare averages and data  
  • Excel tip: how to draw and edit box plots  
  • Describing the shape and frequency of data using a Histogram chart  
  • Excel tip: how to create and edit histograms in Excel  
  • How to calculate and use the correlation coefficient  

    The correlation coefficient measures the strength of how close two (continuous) variables follow or relate to each other. Very useful to use with scatter-plot graphs

  • Excel tip: how to calculate the correlation coefficient  
Beginner / Intermediate: Measuring the spread of data
  • Why we often square data in statistics  
  • How to calculate the sample or population variance  
  • Excel tip: how to calculate variance  
  • The what and how of standard Deviation (a super powerful measure)  
  • Excel tip: calculating standard deviation  
Beginner / Intermediate: Measuring accuracy and confidence
  • Why "normally distributed" data is so important  
  • What the Central Limit Theorem tells us - see it in action!  
  • Introduction to Confidence Intervals  
  • How to use Confidence Intervals in your graphs and work  
  • Excel tip: how to calculate confidence intervals in Excel  
  • Excel tip: how to add confidence intervals to graphs in Excel  
Intermediate: T-tests and Hypothesis testing (combining it all)
  • One sample t-test: calculating the t-value  
  • One sample t-test: finding the T-critical value  
  • One sample t-test: one-tail vs. two-tail tests  
  • One sample t-test: the p-values approach  
  • Two sample t-test: calculating the t-value  
  • Two sample t-test: finding the T-critical value  
  • Two sample t-test: using the p-value approach  
Conclusion: Final Analysis
  • Final analysis of results and insights  
Enrolled Students
$ 19.00
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