Foundations: Data, Data, Everywhere
Summary of a course by Google for Data Analytics Professional certificate. For me, the course is well structured and really gives me a strong foundation about data analysis. It enriches a corporate financial analyst, like me, who has been working for years in a corporation. Therefore, I write this article in order to make this valuable theoretical knowledge stick to my mind.
Data analysis is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making. It has a process which has 6 phases, they are : ask, prepare, process, analyze, share, and act. Following is the summary of each phase of the processes.
Data analysis is part of data analytics. It looks like a word play, right? Apparently not, it has own meaning. Data analytics has broader meaning and definition than data analysis. It is a science of data. Therefore, it covers area of data analysis, data ecosystem, and machine learning.
“If you want to make a few important decisions under uncertainty, that is statistics. If you want to automate, in other words, make many, many, many decisions under uncertainty, that is machine learning and AI. But what if you don’t know how many decisions you want to make before you begin? What if what you’re looking for is inspiration? You want to encounter your unknown unknowns. You want to understand your world. That is analytics.” — Cassie on Dimensions of Data Analytics
As a data analyst, we are better equipped with skills of understanding context, technical mindset, data design, and data strategy. I assume the experienced analyst is familiar already with understanding context and data design in term of the definition. Technical mindset is a ability to break things down into smaller steps or pieces and work with them in orderly and logical way. While data strategy is a management of the people, processes, and tools used in data analytics.
The most important resource for data analyst is data itself. It has life cycle, which are:
- Plan: Decide what kind of data is needed, how it will be managed, and who will be responsible for it.
- Capture: Collect or bring in data from a variety of different sources.
- Manage: Care for and maintain the data. This includes determining how and where it is stored and the tools used to do so.
- Analyze: Use the data to solve problems, make decisions, and support business goals.
- Archive: Keep relevant data stored for long-term and future reference.
- Destroy: Remove data from storage and delete any shared copies of the data.
I found the data life cycle has quite same phase with data analysis process from the plan until the analyze. In each of the cycle of the data, there are two core data tools: spreadsheet and structured query language (SQL).
Structured Query Language (SQL) is one of the most useful data analyst tools, particularly when working with large datasets in tables. It follows a unique set of guidelines known as syntax. Following is the example of the SQL query to extract a data from database:
- Use SELECT to choose the columns you want to return.
- Use FROM to choose the tables where the columns you want are located.
- Use WHERE to filter for certain information.
For spreadsheet, we can access more learning resources in here (Microsoft): https://support.microsoft.com/en-us/office/excel-video-training-9bc05390-e94c-46af-a5b3-d7c22f6990bb?ui=en-US&rs=en-US&ad=US
Last part of this foundation course is talking about the data analyst as a profession. Addressing the popular role in data analytics: data analysts and data scientists. It has difference job description and the scope.
Some points of my reflection about the course:
- There is a rigorous and structured theory for both data analysis and data life cycle that each has its own types of phases. This framework really helps me to stand where I am now as a data analyst.
- As the visualization is about conveying the message by easily to understand, it should be simpler than sophisticated graphic. This hits me hard.
