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 🙂
Concept Even though I already have some knowledge and experience in Data Analytics using Python, Pandas, Seaborn etc I wanted to take this certification out of curiosity and to learn something new. I am not going to go into the debate of Python vs R and which one is better. Rather, I will only focus on this course and its…
In this part of the course, we will learn about the data life cycle and data analysis process. They are both relevant to our work in this program and on the job. We will also be introduced to applications that help guide data through the data analysis process. Module 2 Learning Objectives Variations of the data life cycle You have…
Data Analytics Tools Spreadsheets, query languages, and data visualization tools are all a big part of a data analyst’s job. In this part of the course, we will learn the basic concepts to use them for data analysis. Learning Objectives Let’s learn Spreadsheet .. In the spirit of lifelong learning, it is good to have resources to turn to when…
In this module, we will examine different types of businesses and the jobs and tasks that analysts do for them. You’ll also learn how a Google Data Analytics Certificate will help you meet many of the requirements for an analyst position with these organizations. Learning Objectives Ethical and fair decision-making process of a Data analyst Fairness means ensuring your analysis…
Why You Should Take This Course This course is the second in the Google Data Analytics Certificate program. It builds on the foundation you learned in the first course and dives deeper into the world of data analysis. Here are some reasons why you should take this course: In addition to the above, this course will also help you to:…
In analytics, data drives decision-making, and this is your opportunity to explore data of all kinds and its impact on all sorts of business decisions. We will also learn how to effectively share your data through reports and dashboards. Learning Objectives Data trials and triumphs Introduction A data analytics professional’s job is to provide the data necessary to inform key…
Spreadsheets are a key data analytics tool. Here, we will learn both why and how data analysts use spreadsheets in their work. We will also investigate how structured thinking helps analysts understand problems and come up with solutions. Learning Objectives Spreadsheets and the data life cycle To better understand the benefits of using spreadsheets in data analytics, let’s explore how…
Working with stakeholders Your data analysis project should answer the business task and create opportunities for data-driven decision-making. That’s why it is so important to focus on project stakeholders. As a data analyst, it is your responsibility to understand and manage your stakeholders’ expectations while keeping the project goals front and centre. You might remember that stakeholders are people who…
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…