Foundations: Data, Data, Everywhere - Course 1
Overview
Course 1 in the Google Data Analytics Professional Certification lays the foundation for the rest of the certification program. I got an overview of the data analysis process and learned how important it is to drive data-driven decision-making. The data life-cycle was covered in detail. And finally, we covered primary hard and soft skills and began to dip our toes into SQL and spreadsheet basics.
Data Analysis Process
Google’s data analysis process is broken down into 6 steps. These first three steps are: Ask questions and define the problem, Prepare data by collecting and storing the information, and Process data by cleaning and checking the information. The first three steps are vital to the success of your analysis.
The final three phases of the data analysis process are: Analyze the data to find patterns, relationships, and trends, Share your findings with key stakeholders and subject-matter experts, and Act on the insights derived from the analysis.
The Ask phase makes sure that you have identified the problem, have created a data collection plan, checked your process for bias, and communicated with key stakeholders.
The Prepare phase ensures that you are collecting the correct data effectively and are storing the data securely to protect private information.
The Process phase entails data cleaning to make sure the entries are standardized, checked for errors, and lacking missing information.
The Analyze phase is carried out using data analysis tools like SQL, spreadsheets, and programming languages like Python or R. These tools allow analysts to identify patterns, relationships, and trends in the dataset.
The Share phase uses visualization tools like Tableau, R Studio, or simple spreadsheet visualizations to better communicate findings with stakeholders.
The Act phase allows leaders to utilize the findings to take action and remedy a problem or achieve a business goal.
Key Data Analyst Skills
The Foundations course introduces learners to the skills a data analyst needs to develop to be successful. These are Curiosity, Understanding Context, a Technical Mindset, Data Design capabilities, and Data Strategy.
We are also introduced to some of the important aspects of analytical thinking like being able to visualize data, strategize about goal achievement, becoming problem-oriented, finding correlations in data sets, and the ability to be both a big-picture and detail-oriented thinker. We also covered methods to determine the root cause of a problem and how to think about gap analysis.
Data Phases and Tools
This portion of the course taught me about the Data Life-Cycle and its underlying phases. These are Planning what data types to collect and how to do it, Capturing that data, Managing it responsibly and securely, Analyzing it to solve problems, Archiving it for possible later use, and Destroying it to keep company data private.
Introduction to SQL and Spreadsheets
Here we get our introduction to spreadsheets, query languages, and visualization tools.
Spreadsheets - We took on a few simple projects with Excel using the software’s formula, function, and visualization features. We used data provided by Google.
Query Languages - We learned several basic SQL queries and their syntax to retrieve information from a database. I included an SQL syntax cheat sheet I’ll be filling out. We covered:
WHERE, FROM, and SELECT commands,
Making annotations,
ALIAS commands,
and conditions.
Visualization Tools - We discussed how the popular data visualization tools Tableau and R Studio can help analysts effectively communicate findings.
