Google Data Analytics Certification Review -Coursera Journey
.png "Google Data Analytics Certification Review -Coursera Journey")
Please note that my blog contains affiliate links. This means that I may receive a commission if you click on a link and make a purchase. However, please rest assured that this does not affect the price you pay for the product or service. I only promote products and services that I believe in and have personally used or researched. Thank you for supporting my blog!
TOPIC OUTLINE
- Introduction
- How Did I Get There?
- List of CoursesTips
- Course 1- Foundations: Data, Data Everywhere
- Course 2 - Ask Questions to Make Data-Driven Decisions
- Course 3 - Prepare Data for Exploration
- Course 4 - Process Data from Dirty to Clean
- Course 5 - Analyze Data to Answer Questions
- Course 6 - Share Data Through the Art of Visualization
- Course 7 - Data Analysis with R Programming
- Course 8 - Google Data Analytics Capstone: Complete a Case Study
- Is Google Data Analytics Certification Worth It? (Verdict)
INTRODUCTION
Google Data Analytics Professional Certificate is a program made by Google Career Certificates, one of the top instructors of Coursera—a 4.8- rating program that helps people in learning data analytics skills (in-demand skills) that will get them job ready in less than 6 months. It is for the students and people who want to shift in their career path, whether they have experience or not. They are welcome to take the course.
With over 1.6 million participants enrolled, the program reflects the significant demand for data analytics roles among companies globally, particularly in today's technology-driven and e-commerce landscape. As businesses increasingly rely on data to derive meaningful insights, these insights play a crucial role in informed decision-making processes.
The lists are the skills you will gain after completing the 8-course series program:
- Spreadsheets
- Data Cleansing
- Data Visualization (Data Viz)
- SQL
- Questioning
- Decision-Making
- Problem-Solving
- Metadata
- Data Collection
- Data Ethics
- Sample Size Determination
- Data Integrity
- Data Calculations
- Data Aggregation
- Tableau Software
- Presentation
- R Programming
- R Markdown
- R Studio
- Job Portfolio
How Did I Get There?
I used to work in e-commerce for
almost two years and I was fascinated with data especially converting into
visual forms. This is my first time since my course is not somehow related to
my job which is why I felt a little bit challenged in learning some new stuff
like SQL and Looker/Google Data Studio.
Actually, the course program is not
totally free but you may apply for financial
aid in Coursera. In my case, the Department of Information and
Communication Technology offered a scholarship in 2022. Luckily, I am one of
the Google Scholars sponsored by them.
Courses
In order to get a professional certificate, you shall complete the eight-course series. The said program's estimated of time completion is about 6 months or less at 10 hours per week (Learn at your own pace). In my case, I finished it in more or less than 5 months.
These are the following courses:
- Foundations: Data, Data Everywhere
- Ask Questions to Make Data-Driven Decisions
- Prepare Data for Exploration
- Process Data from Dirty to Clean
- Analyze Data to Answer Questions
- Share Data Through the Art of Visualization
- Data Analysis with R Programming
- Google Data Analytics Capstone: Complete a Case Study
Tips
In my case. I have a notebook, a highlighter, and a pen for jotting down some important points throughout the course. Bookmark all the external links provided by this program. They might help you as a future reference. Anyways, if you start the program today, these tips will also reiterate in course 1.
To pass every course, you shall obtain 80% and up in quizzes, assignments, hands-on activities, and exams. Quiz, assignments, and hands-on activities usually have 4 items below (easy questions). Weekly Challenges have usually 8 items (moderate questions) and Course Challenges have usually 10-15 items (moderate to hard questions). If you fail the weekly and course challenge succeeding three times, the 4th attempt will be taken after 24 hours.
COURSE 1- FOUNDATIONS: DATA, DATA EVERYWHERE
Sir Tony, Google’s Project Manager, is the instructor of this course; This course introduced the world of data analytics, understanding the data ecosystems, data life cycle, and all about data. Skills that should be embodied by the data analysts. How to think analytically. Jobs that can be fitted in a data analytics role
Also, the course discusses a glimpse of the six processes in data analytics designed by Google. Introduction of Spreadsheets, SQL, and Data Visualizations.
The lists are the skills you will gain after completing Course 1:
- Data Analysis
- Data Management
- SQL
- Business Analysis
- Data Visualization
- Probability & Statistics
- Spreadsheet Software
- Statistical Programming
- General Statistics
DATA ANALYTICS FOR BEGINNERS I GOOGLE DATA ANALYTICS CERTIFICATE
Week 1 - Introducing Data Analytics (ETA: 6hrs)
- Data - Collection of Facts
- Data Analysis - Collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making
- Data Analyst - someone who collects, transforms, and organizes data in order to help make informed decisions
- Six Phases of Data Analysis
- Four Types of Business Analytics
- Harvard Business Model - Key Competitive Advantage
- Four Types of Business Analytics
- Business Analytics vs. Data Science
- Origins of the Data Analysis
- Big Data Analysis Life Cycle
Week 2 - All About Analytical Thinking (ETA: 3hrs)
- Aspects of Analytical Skills
- Analytical Thinking - identifying and defining a problem and then solving it by using data in an organized, step-by-step manner.
- Stages of Data Life Cycle
- Variations of Data Life Cycle
- US Fish and Wildlife Service
- US Geological Survey
- Financial Institutions
- Harvard Business Schools
- Prepare quantitative and Qualitative Data
- Process- Cleaning
- Analyze - Trained to look for patterns
- Share - Sharing high-level insights to the executive level of the team
- Act - the most critical part
- Definition of Attributes, Observations, and Formulas
- SQL
- The Power of Data in Business
- Decoding the Job Description
- Data Analytics vs. Data Scientists vs. Data Specialists
- Interview Best Practices
Insights
I'll give Course 1 a 4.8 rating because it introduces all the courses in this program in a glimpse. The instructor is informative and the readings are enough to be learned by the students but not in a deeper way because it will tackle in the succeeding course.
Data! Data! Data! I can’t make bricks without clay. -Sherlock Holmes
The lists are the skills you will gain after completing Course 2:
- Business Analysis
- Cloud Computing
- Data Management
- Data Analysis
- Collaboration
- Leadership and Management
- Communication
- Conflict Management
- Big Data
- Spreadsheet Software
- Critical Thinking
- Business Communication
Week 1 - Effective Questions (ETA: 5hrs)
- Six Common Types of Problems
- Making Predictions
- Categorizing Things
- Spotting Something Unusual
- Identifying Themes
- Discovering Connections
- Finding Patterns
- SMART Questions
Week 2 - Data-Driven Decisions (ETA: 3hrs)
- Data Inspired Decision Making
- Quantitative Data vs Qualitative Data
- Quantitative Tools vs Qualitative Tools
- Dashboards vs. Reports
- Types of Dashboards
- Small Data vs. Big Data
- Four V's of Big Data
- Spreadsheets and the Data Life Cycle
- Plan
- Capture
- Manage
- Analyze
- Archive
- Destroy
- Type of Errors
- #DIV/0
- #ERRORS
- #N/A
- #NAME?
- #NUM!
- #VALUE!
- #REF!
- Scope of Work
- The Importance of Context
- Different Stakeholders and Their Goals
- Tips to Communicate Clearly
- Clear Communication is Key
- Tips for Effective Communication
- Limitations of Data
- Meeting: Best Practices
- Leading Great Meetings
"Sometimes people think that data can answer everything and sometimes we have to acknowledge that this is simply untrue." - Sarah
The lists are the skills you will gain after completing Course 3:
- Data Collection
- Spreadsheet
- Metadata
- SQL
- Data Ethics
CONTENT SUMMARY
Week 1 - Data Types and Data Structures
- Data Collections Considerations
- Structured Data vs. Unstructured Data
- Data Modelling Techniques
- Long Data vs. Wide Data
- Data Types in Spreadsheets
- Data Transformation
Week 2 - Bias, Credibility, Privacy Ethics, and Access
- Types of Data Bias
- Identifying Good Data Sources
- Reliable
- Original
- Comprehensive
- Current
- Citations
- Aspects of Data Ethics
- Sites and Resources for Open Data
- US Government Data Site
- US Census Bureau
- Open Data Network
- Google Cloud Public Datasets
Week 3 - All About Data
- Elements of Metadata
- Importing data from other Spreadsheets
- Importing Data from CSV Files
- Importing HTML from web pages
- SQL Best Practices
- Benefits of Organizing Data
- Best Practices when Organizing Data
- Best Practices for File Naming Convention
- Best Practices for Keeping Files Organized
- LinkedIN vs. Github
- Professional Networking
- Online Connections
- Offline Gatherings
"Found it really fascinating that we can fake all of these datasets and synthesize them and allow us to really deliver some cool insights and trends to our hospital systems." -Halie, Analytical Lead
The lists are the skills you will gain after completing Course 4:
- Spreadsheet
- Data Integrity
- Sample Size Determination
- SQL
- Data Cleansing
Week 1 - Importance of Integrity
- Data Replication vs. Data Transfer vs. Data Manipulation
- Other Threats of Integrity
- Human Error
- Viruses
- Malware
- Hacking
- System Failures
- Data Constraints
- Data Type
- Data Range
- Mandatory
- Unique
- Regular
- Cross-Field Pattern
- Primary Key
- Foreign Key
- Accuracy
- Completeness
- Consistency
- Types of Insufficient Data and Ways to Address it
- Data Issues
- 1 - No Data
- 2 - Too Little Data
- 3 - Wrong Data Including Data with Errors
- Sampling
- Human Error
- Viruses
- Malware
- Hacking
- System Failures
- Data Type
- Data Range
- Mandatory
- Unique
- Regular
- Cross-Field Pattern
- Primary Key
- Foreign Key
- Accuracy
- Completeness
- Consistency
- 1 - No Data
- 2 - Too Little Data
- 3 - Wrong Data Including Data with Errors
Week 2 - Data Cleaning is a MUST
- Dirty Data vs. Clean Data
- Types of Dirty Data
- Duplicate Data
- Outdated Data
- Incomplete Data
- Incorrect and Inaccurate Data
- Inconsistent Data
- Data Validation
- Data Integrity Principle
- Completeness
- Validity
- Consistency
- Accuracy
- Data Merging
- Data Mapping
- SQL Databases Features vs Spreadsheets Features
- SQL Functions
Week 4 - Verify and Report on your Cleaning Results
- Verification
- Data Cleaning Checklists
- Documentation
- Best Practices for Change Logs
Week 5 - Data Analyst Hiring Process
- Adding Soft Skills to your Resume
"I was a little bit apprehensive about learning a complete language. Be super curious about whatever data set that you're given" -Evan, Portfolio Manager
COURSE 5 - ANALYZE DATA TO ANSWER QUESTIONS
The lists are the skills you will gain after completing Course 5:
- Data Aggregation
- Spreadsheet
- Data Analysis
- Data Calculations
- SQL
Week 1 - Organize Data for More Effective Analysis
- Four Phase of Analysis
- Organize the Data
- Format and Adjust the Data
- Get input from others
- Transform others
- Sort Sheet and Sort Range
Week 2 - Format and Adjust Data
- Data Validation
- Common Conversions
- Numberic
- Date
- Strings
- Concat Functions
- Best Practices for Searching Online
- Data Aggregation
- Averages
- Minimum
- Maximum
- Sums
- Using Vlookup and Match in Spreadsheets
- SQL Joins
- Inner Join
- Left Join
- Right Join
- Outer Join
- Count and Count Distinct
- Aliasing
- Subquery
- Having
- Few Rules that subqueries must be follow
- Types of Data Validation
- Data Type
- Data Range
- Data Constraints
- Data Consistency
- Data Structure
- Code Validation
- How to create temporary Tables
"Analyze Stage is where you become the expert about your datasets" -Layla, Analytical Lead
COURSE 6 - SHARE DATA THROUGH THE ART OF VISUALIZATION
The lists are the skills you will gain after completing Course 6:
- Data Analysis
- Presentation
- Tableau Software
- Data Visualization
Week 1 - Communicating Your Data Insights
- Data Visualization - graphic representation and presentation of data.
- Four Elements of Effective Data Visualization (David McCandless)
- Information
- Topic
- Goal
- Visual Form
- Frameworks for Organizing your Thoughts About Visualization
- Principles of Design
- Balance
- Emphasis
- Movement
- Pattern
- Repetition
- Proportion
- Rhythm
- Variety
- Unity
- Graphs and Charts
- Correlation vs Causation
- Static vs Dynamic Visualization
- Tableau
- Elements of Effective Visuals
- Five Phases of the Design Process
- Pro Tips for Highlighting Key Information
- Ways to Make Data Visualization Accessible
- 3 Data Storytelling Steps
- Engage your Audience
- Create Compelling Visuals
- Tell the Story in an Interesting Native
- Spotlighting
- Static Data vs Live Data
- Data Storytelling
- Understand to make a Great Slideshows
Week 4 - Putting it all Together
- McCandless Method
- Good Data Presentation
- Telling your Data Story
- Preparing the Question and Answer
- Type of Objections
- Q and A Best Practices
- Important aspects to a Presentation
"One of your biggest considerations when creating a data visualization is where you'd like your audience to focus"
The lists are the skills you will gain after completing Course 7:
- Data Analysis
- R Markdown
- Data Visualization
- R Programming
- RStudio
Week 1 - Programming and Data Analytics
- R vs Python
- Programming
- Coding
- Spreadsheets vs. SQL vs. R
- Tips for Learning Programming Languages
- R Studio Environment
- Operator
- Functions
- Variable
- Vectors
- Lists
- Data Structures
- Data Frame
- Nested if
- Packages
- Tibbles
- Tidy Data Standards
- File Naming Conventions
- Operators
- Arithmetic Operators
- Relational Operators
- Logical Operators
- Assignment Operators
- ggplot2
- Mapping(R)
- Common Problems when Visualizing in R
- Types of Smoothing
- Facets Functions
- Filtering and Plots
- Annotations
- RMarkdown
- R Notebook
- Jupyter Notebook
- Kaggle
- Google Colab
"The more exposure I've gotten to R, the more I realize a lot of what I would try to do that way I can just do within one program" -Carrie, Research Manager
The lists are the skills you will gain after completing Course 8:
- Data Analysis
- Creating Case Studies
- Data Visualization
- Data Cleansing
- Developing a Portfolio
Week 1 - Learn About Capstone Basics
- What to Include in Portfolio?
- What to include in Case Study?
- What platforms align with your interests and passions?
- Where do you want to spend more time after this program?
- Kaggle
- Github
- Blog
- Tableau
- Crafting your Online Portfolio
- Top Tips for Interview Success
"In any occupation, across the globe, across various industries. having a knack and understanding of data is going to be crucial for everyone" -Rishie, Global Analytical Skills Curriculum Manager.
CONCLUSION: WHY YOU SHOULD TAKE THIS COURSE
COMPREHENSIVE CURRICULUM
The program features over 180 hours of interactive content, including modules in data cleaning, data visualization, SQL, and R programming. Designed by Google Professionals, the curriculum mirrors real-world scenarios, ensuring you gain practical, job-ready skills.
.png)
.png&description=Google Data Analytics Certification Review -Coursera Journey){kind=link}
0 Comments