I completed the Google Data Analytics Certificate Course — Here is what I learnt
I went in as a curious cat to explore a new Google course, however I learnt so much in a short time the impactful pool of information from the Google Data Analytics Certificate Course on Coursera. My biggest takeaway from the course is how to undertake and complete successfully a project’s data analysis process by using the APPASA framework to data analysis. I also learnt useful resources and tools such as Data Visualization with Tableau, processing, analyzing and visualizing data with R Studio (R programming language), while I capped my learning with a capstone data analysis project.
I really learnt a lot exploring the APPASA framework to a Data Analysis project lifescycle. The process opened me to learning new functions, formula, formatting and sorting techniques with Google Spreadsheet (which can be replicated on MS Excel) as well as useful hacks to querying large datasets using SQL on Google Bigquery.
The APPASA framework is really at the center of the course hence I will be summarizing gems I discovered while learning about the APPASA framework to Data analysis:
A — Ask Questions: The ask framework to data analysis taught me how to ask SMART and effective questions. I also learnt how to structure how I think, how to ask the right questions, summarizing data, putting data into context, and finally managing team and stakeholder expectations in the Ask stage of the process framework.
P — Prepare: The prepare framework taught me the intrinsic value of preparing my data, in the data analysis life cycle. The sources where I gather my data from and how I prepare this raw and unrefined data is an important pillar in the success of my data analysis process building. At a high level I learnt how data is generated, features of different data types, fields, and values. I learnt about database structures, the role of metadata in data analytics and to cap it off how to write Structured Query Language (SQL) functions
P — Process: After data preparation comes data processing. The tools and processes used by data analysts to clean data so it can be free from bias and great data source for analysis. The process framework really allowed me to learn lots of Spreadsheet functions, formulas and know-hows to better help me rearrange, clean and process data. I learnt a great deal about conditional formatting in this module. At a high level I learnt about connecting business objectives to data analysis process, identifying clean and dirty data and segregating them, cleaning small datasets using spreadsheet tools, cleaning large datasets by writing SQL queries, documenting data-cleaning processes for team mates and organization members to keep track.
A — Analysis: This is one of the gems of this course as I picked up lots of skills I did not know I needed on Google spreadsheets. I learnt how to effectively use conditional formatting, functions and formulas to analyze data. At a high level I learnt how to sort data in spreadsheets, how to sort data in Bigquery using SQL queries, filtering data in spreadsheets, converting data, formatting data, substantiating data analysis processes and more. This course brought me to the nitty gritty of analyzing small and large scale data sets be it on spreadsheets or in a database.
S — Share: In this module I learnt one of the pillars to data analysis life cycle, called data visualization. I learnt how to share and visualize data to key stakeholders in depthly using the Data visualization tool called Tableau. At a high level I learnt the importance of Design thinking, how data analysts use visualizations to communicate about data, the benefits of Tableau for presenting data analysis findings, data-driven storytelling, similarities and differences between static data visualizations tools and dynamic dashboards
A — In the Act stage, I learnt how to use data to tell important business case stories, find pattern and insights from this data and use this information as insights that will guide the stake-holders business decision process based on quality, unbiased and well informed data point stories from my data analysis process. At a high level I learnt how to do data analysis with the R programming language. I learnt about the Programming languages and environments
- R packages
- R functions, variables, data types, pipes, and vectors
- R data frames
- Bias and credibility in R
- R visualization tools
- R Markdown for documentation, creating structure, and emphasis
The APPASA framework to data analysis lifecycle is a tested and proven framework that has guided lots of Data Analysts around the world to do quality data analysis work in their respective organizations.
The 8 course series really dug deep in introducing the foundation of data analysis to learners and it also focused on upskilling learners to have practical skills that can be used in starting and completing a data analysis project from top to bottom with little to no supervision. I finished the course in under 8 weeks and I could attest to how impactful the knowledge I learnt from the course is to how I work with data in my daily job. I finished the course feeling like a superhero. You can get to see my credly course certificate here
This is the first of a two part-series. I would be documenting how I used the APPASA framework as well as other resources to complete my Google Data Analytics Certificate Capstone project in my next post.
