Google Data Analysis Professional Certification
Why this course and ultimate Goal
At first glance, I liked this course because it is a combination of 8 courses and gives a solid foundation on the fundamental understanding of Data Analytics, what is it and why. And then this course dives deep into learning spreadsheets, R programming Language, visualisation with Tableu etc. I think these are very powerful concepts to understand moving forward.
So the 8 subjects in this course are:
Course 1: Foundations: Data, Data, Everywhere
What you will learn:
Real-life roles and responsibilities of a junior data analyst
How businesses transform data into actionable insights
Spreadsheet basics
Database and query basics
Data visualisation basics
Skill sets you will build:
Using data in everyday life
Thinking analytically
Applying tools from the data analytics toolkit
Showing trends and patterns with data visualisations
Ensuring your data analysis is fair
Course 2: Ask Questions to Make Data-Driven Decisions
What you will learn:
How data analysts solve problems with data
The use of analytics for making data-driven decisions
Spreadsheet formulas and functions
Dashboard basics, including an introduction to Tableau
Data reporting basics
Skill sets you will build:
Asking SMART and effective questions
Structuring how you think
Summarizing data
Putting things into context
Managing team and stakeholder expectations
Problem-solving and conflict-resolution
Course 3: Prepare Data for Exploration
What you will learn:
How data is generated
Features of different data types, fields, and values
Database structures
The function of metadata in data analytics
Structured Query Language (SQL) functions
Skill sets you will build:
Ensuring ethical data analysis practices
Addressing issues of bias and credibility
Accessing databases and importing data
Writing simple queries
Organizing and protecting data
Connecting with the data community (optional)
Course 4: Process Data from Dirt to Clean
What you will learn:
Data integrity and the importance of clean data
The tools and processes used by data analysts to clean data
Data-cleaning verification and reports
Statistics, hypothesis testing, and margin of error
Resume building and interpretation of job postings (optional)
Skill sets you will build:
Connecting business objectives to data analysis
Identifying clean and dirty data
Cleaning small datasets using spreadsheet tools
Cleaning large datasets by writing SQL queries
Documenting data-cleaning processes
Course 5: Analyse Data to Answer Questions
What you will learn:
Steps data analysts take to organize data
How to combine data from multiple sources
Spreadsheet calculations and pivot tables
SQL calculations
Temporary tables
Data validation
Skill sets you will build:
Sorting data in spreadsheets and by writing SQL queries
Filtering data in spreadsheets and by writing SQL queries
Converting data
Formatting data
Substantiating data analysis processes
Seeking feedback and support from others during data analysis
Course 6: Share Data Through the Art of Visualisation
What you will learn:
Design thinking
How data analysts use visualisations to communicate about data
The benefits of Tableau for presenting data analysis findings
Data-driven storytelling
Dashboards and dashboard filters
Strategies for creating an effective data presentation
Skill sets you will build:
Creating visualisations and dashboards in Tableau
Addressing accessibility issues when communicating about data
Understanding the purpose of different business communication tools
Telling a data-driven story
Presenting to others about data
Answering questions about data
Course 7: Data Analysis with R Programming
What you will learn:
Programming languages and environments
R packages
R functions, variables, data types, pipes, and vectors
R data frames
Bias and credibility in R
R visualisation tools
R Markdown for documentation, creating structure, and emphasis
Skill sets you will build:
Coding in R
Writing functions in R
Accessing data in R
Cleaning data in R
Generating data visualisations in R
Reporting on data analysis to stakeholders
Course 8: Data Analytics Capstone Project: Complete a Case Study
What you will learn:
How a data analytics portfolio distinguishes you from other candidates
Practical, real-world problem-solving
Strategies for extracting insights from data
Clear presentation of data findings
Motivation and ability to take the initiative
Skill sets you will build:
Building a portfolio
Increasing your employability
Showcasing your data analytics knowledge, skill, and technical expertise
Sharing your work during an interview
Communicating your unique value proposition to a potential employer
My ultimate goal from this course is to learn theoretical and practical knowledge and get better at data analysis. At the end I will post my Capstone Project Case Study here and in GitHub with all the documentation.
I will mainly take notes and details from the course directly and paste them here. I also decided to write them as a series to make it easy to jump in and out in different posts and course modules. The series will grow and evolve as I move forward with the course. Might seem a bit of unclear time to time, but I will try my outmost to build a comprehensive preparation guide.
I will go module by module notes for now..
Welcome to the journey and Happy Learning 🙂