Course 4 – Process Data from Dirty to Clean
- Ensuring data integrity. Data integrity is necessary to ensure a successful analysis. In this part of the course, you will explore methods and steps that analysts take to check data for integrity. This includes knowing what to do when you have an insufficient amount of data. You will also learn about sample size, avoiding sample bias, and using random samples. All of these measures also help to ensure a successful data analysis.
- Understanding clean data. Every data analyst wants clean data to work with when performing an analysis. In this part of the course, you will learn the difference between clean and dirty data. You will practice data cleaning techniques in spreadsheets and other tools.
- Cleaning data using SQL. Knowing a variety of ways to clean data can make an analyst’s job much easier. In this part of the course, you will use SQL to clean data from databases. You will explore how SQL queries and functions can be used to clean and transform your data before analysis.
- Verifying and reporting cleaning results. Cleaning data is an important step in the data analysis process. In this part of the course, you will verify that data is clean and report data cleaning results. With verified clean data, you will be ready for the next step in the data analysis process.
- (Optional) Add data to your resume. Creating an effective resume will help you in your data analytics career. In this part of the course, you will learn all about the job application process. Your focus will be on building a resume that highlights your strengths and relevant experience.
- Completing the Course Challenge. At the end of this course, you will be able to apply what you have learned in the Course Challenge. The Course Challenge will ask you questions about the key concepts and then will give you an opportunity to put them into practice as you go through prepared scenarios.
The importance of integrity
Sparkling-clean data
Verify and report on your cleaning results