Module 1- Introduction to focus on integrity

Module 2- Data integrity is important

Module 3- Balancing objectives with data integrity

Module 4- Dealing with insufficient data

Module 5- The importance of sample size

Module 6- Using statistical power

Module 7- Determine the best sample size 

Module 8- Evaluate the reliability of your data

Module 9- Data cleaning is important

Module 10- Recognize and remedy dirty data

Module 11- Data-cleaning tools and techniques

Module 12- Cleaning data from multiple sources

Module 13- Data-cleaning features in spreadsheets

Module 14- Optimise the data-cleaning process

Module 15- Different data perspectives

Module 16- Even more data-cleaning techniques

Module 17-Using SQL to clean data

Module 18-Understanding SQL capabilities

Module 19-Spreadsheets versus SQL

Module 20-Widely used SQL queries

Module 21-Cleaning string variables using SQL

Module 22-Advanced data cleaning functions, part 

Module 23-Advanced data-cleaning functions, part 

Module 24-Verifying and reporting results

Module 25-Cleaning and your data expectations

Module 26-The final step in data cleaning

Module 27-Capturing cleaning changes

Module 28- Documentation is important

Module 29- Feedback and cleaning

Module 30- About the data-analyst hiring process 

Module 31- The data analyst job-application process

Module 32- Creating a resume 

Module 33- Making your resume unique

Module 34-Translating past work experience