- Foundation Course Module 1: Introduction of data analytics and analytical thinking
- Foundation Course Module 2 : The wonderful world of data
- Foundation Course Module 3 : Setup of data analytics toolbox
- Foundation Course Module 4: Becoming a fair and impactful data professional
- Foundation Course: Glossary
- Course 2: Ask questions to make data driven decisions, Module 1: Ask effective questions
- Course 2: Ask questions to make data driven decisions, Module 2: Make data-driven decisions
- Course 2: Ask questions to make data driven decisions, Module 3: Spreadsheet magic
- Course 2: Ask questions to make data driven decisions, Module 4: Always remember the stakeholder
- Course 3: Prepare Data For Exploration: Learning objectives and overviews
- Course 3: Prepare Data For Exploration, Module 1: Data types and structures
- Course 3: Prepare Data For Exploration, Module 2: Data responsibility
- Course 3: Prepare Data For Exploration, Module 3: Database Essentials
- Course 3: Prepare Data For Exploration, Module 4: Organise and Secure Data
- Course 4: Process Data from Dirty to Clean: Overview
- Course 4: Process Data from Dirty to Clean, Module 1: The importance of integrity
- Course 4: Process Data from Dirty to Clean, Module 2: Clean it up
- Course 4: Process Data from Dirty to Clean, Module 3: SQL
- Course 4: Process Data from Dirty to Clean, Module 4: Verify and Report Results
- Course 5: Analyse Data to Answer Questions, Module 1: Organise data for more effective analysis
- Course 5: Analyse Data to Answer Questions, Module 2: Format and adjust data
- Course 5: Analyse Data to Answer Questions, Module 3: Aggregate data for analysis
- Course 5: Analyse Data to Answer Questions, Module 4: Perform Data Calculations
- Course 6: Share Data Through the Art of Visualisation, Course Overview plus Module 1: Visualise Data
- Course 6: Share Data Through the Art of Visualisation, Course Overview plus Module 2: Create Data Visualisation with Tableau
A massive amount of data is generated every single day. In this part of the course, you will discover how this data is generated and how analysts decide which data to use for analysis. You’ll also learn about structured and unstructured data, data types, and data formats as you start thinking about how to prepare your data for analysis.
Learning Objectives
- Explain how Kaggle can benefit a data analyst.
- Explain how data is generated as a part of our daily activities, with reference to the types of data generated.
- Explain factors that should be considered when making decisions about data collection.
- Explain the difference between structured and unstructured data.
- Discuss the difference between data and data types.
- Explain the relationship between data types, fields, and values.
- Discuss wide and long data formats with references to organization and purpose.
Course 3 overview
Welcome to the third course in the Google Data Analytics Certificate program! So far, you’ve been introduced to the field of data analytics and discovered how data analysts use their skills to answer business questions.
As you work through this course, you’ll identify and explore different types of data and data structures that can be used to understand and respond to a business problem. Then, you’ll learn to identify any bias in data and to verify its credibility. You’ll keep adding to your data analyst tool box by further exploring data within spreadsheets and databases. Finally, you’ll learn more about engaging with the data community and manage your online presence. All of these skills will come in handy, no matter where your career as a data analyst takes you.
Course content
Each course in this program is broken into modules. Each one is designed to familiarize you with different data structures and show you how to collect, verify, and organize data. You will work on a wide range of activities that are similar to on-the-job tasks that data analysts come across on a daily basis.
Here’s an overview of the skills you’ll learn in each module.
Module 1: Data types and structures
A massive amount of data is generated every single day. In this part of the course, you will discover how this data is generated and how analysts decide which data to use for analysis. You’ll also learn about structured and unstructured data, data types, and data formats as you start thinking about how to prepare your data for analysis.
Module 2: Data responsibility
Before you work with data, you must confirm that it is unbiased and credible. After all, if you start your analysis with unreliable data, you won’t be able to trust your results. In this part of the course, you will learn to identify bias in data and to ensure your data is credible. You’ll also explore open data and the importance of data ethics and data privacy.
Module 3: Database essentials
When you analyze large datasets, you’ll access much of the data from a database. In this part of the course, you will learn about databases, including how to access them and extract, filter, and sort the data they contain. You’ll also explore metadata to discover its many facets and how analysts use it to better understand their data.
Module 4: Organize and protect data
Good organizational skills are a big part of most types of work, especially data analytics. In this part of the course, you will learn best practices for organizing data and keeping it secure. You’ll also understand how analysts use file naming conventions to help them keep their work organized.
Module 5: Engage in the data community
Having a strong online presence can be a big help for job seekers of all kinds. In this part of the course, you will explore how to manage your online presence. You’ll also discover the benefits of networking with other data analytics professionals.
