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 🙂
Select the right data Following are some data-collection considerations to keep in mind for your analysis: How the data will be collected Decide if you will collect the data using your own resources or receive (and possibly purchase it) from another party. Data that you collect yourself is called first-party data. Data sources If you don’t collect the data using…
Data Responsibility Rundown Key Learnings: Specific Topics Covered: Data anonymization What is data anonymization? We have been learning about the importance of privacy in data analytics. Now, it is time to talk about data anonymization and what types of data should be anonymized. Personally identifiable information, or PII, is information that can be used by itself or with other data to…
Maximise databases in data analytics Databases enable analysts to manipulate, store, and process data. This helps them search through data a lot more efficiently to get the best insights. Relational databases A relational database is a database that contains a series of tables that can be connected to form relationships. Basically, they allow data analysts to organise and link data…
File organisation guidelines Every data analyst’s goal is to conduct efficient data analysis. One way to increase the efficiency of your analyses is to streamline processes that help save time and energy in the long run. Meaningful, logical, and consistent file names help data analysts organise their data and automate their analysis process. When you use consistent guidelines to describe…
This course is the fourth in the Google Data Analytics Certificate program. It will teach you how to clean data using spreadsheets and SQL, as well as how to verify and report your data cleaning results. This is an important skill for data analysts, as it ensures that the data they are working with is accurate and reliable. Here are…
Scenario: calendar dates for a global company Calendar dates are represented in a lot of different short forms. Depending on where you live, a different format might be used. Now, think about what would happen if you were working as a data analyst for a global company and didn’t check date formats. Well, your data integrity would probably be questionable.…
What is dirty data? Earlier, we discussed that dirty data is data that is incomplete, incorrect, or irrelevant to the problem you are trying to solve. This section summarizes: Types of dirty data Duplicate data Description Possible causes Potential harm to businesses Any data record that shows up more than once Manual data entry, batch data imports, or data migration…
Knowing a variety of ways to clean data can make a data analyst’s job much easier. Learning Objectives How a junior data analyst uses SQL In this reading, you will learn more about how to decide when to use SQL, or Structured Query Language. As a data analyst, you will be tasked with handling a lot of data, and SQL…
When we clean data, you make changes to the original dataset. It’s important to verify the changes we make are accurate and to let your teammates know about the changes. In this part of the course, we’ll learn to verify that data is clean and report our data cleaning results. With verified clean data, we are ready to begin analysing!…
Organising data makes the data easier to use in your analysis. In this part of the course, We’ll learn the importance of organising your data through sorting and filtering. We’ll explore these processes in both spreadsheets and SQL as you continue to prepare your data. Learning Objectives Course content Course 5 – Analyse Data to Answer Questions Module 1: Organise…