The Google Data Analytics Professional Certificate course is a blessing for anyone who wants to enter the world of data analytics. The course covers the fundamentals skills for tools that a data analyst would need – Spreadsheets, SQL, Tableau and R.
I have been doing the work of a data analyst using excel and data quality at my workplace without myself realizing it. I had done a lot of data cleaning and transformation using enterprise data quality tool. Over a period of time, I started getting excited seeing data everywhere and wanted to do more with it. I used my existing SQL skills to do exploration in my company’s data. But whatever I did was haphazard. I wanted to get a proper understanding of all the things that a data analyst do to extract useful information from the data. One fine evening I was checking on Google and came across the course that Google was offering in partnership with Coursera. A quick scan through the curriculum made me understand what the course had to offer and made me want to go through it.
The Professional Certificate had 8 modules in total.
Foundations: Data, Data, Everywhere
The course talks about data, data and data. It gives a high level view of what a data analyst goes on a regular basis, especially highlighting about the data analysis process – Ask, Prepare, Process, Analyze, Share, Act. Each of the phases was covered in separate modules later. This module also gives details regarding the data life cycle – plan, capture, manage, analyze, archive and destroy, and also highlights the importance of the data life cycle.
Ask Questions to Make Data-Driven Decisions
This is the most overlooked part of the data analysis as many do not pay much attention to it. But, this is the most important step in data analysis as it is crucial to ask the right questions in order to positively find out what needs to be found from the data. Without doing this part correctly, one might end up with totally irrelevant analysis. This module mainly discusses about techniques and framework using which one can ask effective questions. It teaches you how to ask questions using the SMART methodology ( Specific, Measurable, Action oriented, Relevant, Time Bound).
Prepare Data for Exploration
This module covers the ways to access, extract, filter and sort the data as data comes in various formats. It starts by informing us the ways for data collection – forms, surveys, questionnaires, readings/observations, cookies etc. Before data is collected one needs to be clear on what data is needed, how much data, the data types/formats , sample population, source of the data and the time frame from which the data is needed has to be decided. Once data is collected, it can be modelled and transformed in an organized and structured manner. This module also talks about the bias that can occur with the data. Finally, it gives us a way by which we can conclude a set of data is good or bad. You need to check if the data is ROCCC? – Reliable, Original, Comprehensive, Current and Cited.
Process Data from Dirty to Clean
In this module, you will learn about how to do data cleaning. Clean Data + Alignment to Business Objective = Accurate Conclusions. Whenever you deal with insufficient data, you have to modify the objective with the available data or collect more data or get a new dataset with all required data. This module also talks about sampling – importance of correct sample size, confidence level and statistical power. Dirty data can be duplicate data, outdated data, incomplete data, incorrect data, inaccurate data or inconsistent data. In the end of the module, you will learn different functions or methods to clean dirty data using spreadsheets and SQL.
Analyze Data to Answer Questions
This module is all about exploring and analyzing the data to find answers. It highlights four phases in the analysis process – organize the data, format & adjust the data, get input from others and transform the data. You will get the opportunity to work on SQL functions while completing thr assignments.
Share Data through the Art of Visualization
Sharing the story is again one of the most important part of the data analysis process. This is important because no matter how well you find answers, but if you do not present the findings in the right way, then it is useless. In this module, you will learn about the art of visualization, things to consider and things to avoid while making a presentation, design principles.
Data Analysis with R Programming
This is where you dive more deep into data analytics. R is an open source language that is widely used to data analysis. (There is a world wide debate for R vs Python, but let’s discuss that elsewhere. Google said that they had chosen R as they felt that it was easier to learn than Python for newbies). This module teaches you the basics of R and you get good amount of assignments to get hands-on experience in using data wrangling and data visualization packages.
Capstone Project
In the last module, you get to complete a capstone project. Though it is not mandatory, it is highly recommended that you do it as you can showcase all the skills you learnt during the course to potential recruiters. You can consider this as your first project in your data analytics portfolio and then keep adding more projects to your portfolio.
Thanks for your blog, nice to read. Do not stop.