The Google Data Analytics Professional Certificate is an all-inclusive online program aimed at equipping individuals with the necessary skills to excel as data analysts. Created by Google, this program imparts essential abilities and tools crucial for a successful data analysis career, such as data cleaning, problem-solving, and data visualization.
Benefits of the Program:
- Developed by Google's industry experts, ensuring top-notch quality.
- Flexibility to learn at one's own pace through online courses.
- Gain practical, hands-on experience with real-world projects relevant to the industry.
- Connect with a global community of fellow learners.
- Acquire job-ready skills upon completion of the program.
The program is broken up into 8 different courses:
1. Foundations: Data, Data, Everywhere
2. Ask Questions to Make Data-Drive Decisions
3. Prepare Data for Exploration
4. Process Data from Dirty to Clean
5. Analyze Data to Answer Questions
6. Share Data Through the Art of Visualization
7. Data Analysis with R Programming
8. Google Data Analytics Capstone: Complete a Case Study
The course provides a diverse range of learning materials, including video lectures with available transcripts, readings, discussion prompts, interactive practice tools, and numerous quizzes to assess your understanding.
The lessons in each course are also divided in week. After finishing each week, you will be required to take the Week Challenge and at the end of each course, there is also Course Challenge for you to complete to move on to the next stage or course.
While it is completely self-paced, Google suggests it should take you 6 months to complete while working on it 10 hours per week. Though most people should be able to complete it within a month or two, especially those who are with experience in data analytics. However, for beginners who want to kick off their career in data analytics, this course might take approximately 6 months by the suggested timeline.
After completing the entire course, you will gain the following skillsets:
- Job Portfolio
- Data Cleansing
- Data Analysis Process (Ask, Prepare, Process, Analyze, Share, Act)
- Data Lifecycle (Plan, Capture, Manage, Analyze, Archive, Destroy)
- Data Visualization (DataViz)
- Spreadsheet
- Metadata
- BigQuery
- Data Ethics
- SQL
- Data Calculations
- Data Aggregation
- R Programming
- R Markdown
- Tableau
- Structured Thinking
- Data Integrity
What you'll learn:
Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystems.
Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking.
Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics.
Describe the role of a data analyst with specific reference to jobs.
What you'll learn:
Explain how the problem-solving road map applies to typical analysis scenarios.
Discuss the use of data in the decision-making process.
Demonstrate the use of spreadsheets to complete basic tasks of the data analyst including entering and organizing data.
Describe the key ideas associated with structured thinking.
What you'll learn:
Explain what factors to consider when making decisions about data collection.
Discuss the difference between biased and unbiased data.
Describe databases with references to their functions and components.
Describe best practices for organizing data.
What you'll learn:
Define different types of data integrity and identify risks to data integrity.
Apply basic SQL functions to clean string variables in a database.
Develop basic SQL queries for use on databases.
Describe the process of verifying data cleaning results.
What you'll learn:
Discuss the importance of organizing your data before analysis by using sorts and filters.
Convert and format data.
Apply the use of functions and syntax to create SQL queries to combine data from multiple database tables.
Describe the use of functions to conduct basic calculations on data in spreadsheets.
What you'll learn:
Describe the use of data visualizations to talk about data and the results of data analysis.
Identify Tableau as a data visualization tool and understand its uses.
Explain what data driven stories are including reference to their importance and their attributes.
Explain principles and practices associated with effective presentations.
What you'll learn:
Describe the R programming language and its programming environment.
Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors.
Describe the options for generating visualizations in R.
Demonstrate an understanding of the basic formatting in R Markdown to create structure and emphasize content.
What you'll learn:
Differentiate between a capstone project, case study, and a portfolio.
Identify the key features and attributes of a completed case study.
Apply the practices and procedures associated with the data analysis process to a given set of data.
Discuss the use of case studies/portfolios when communicating with recruiters and potential employers.