Over the course of the last year I have been working through the Google data analytics professional certificate on Coursera. The course offers a path into a career as a data analyst where you will learn in demand skills in as little as six months if you study 10 hours a week.  

So what does the course actually offer? The course is split into 8 individual courses each aimed at teaching you a particular set of skills or knowledge required to become a data analyst. This starts with the course, foundation, data, data everywhere. This takes you through, what is data, where does it come from, how it is stored/managed and what jobs are on offer for prospective data analysts. This is great introduction for someone with little to no knowledge of working with data however if you have had to utilize excel or any other type of data tool then this course may be a bit basic for you. I would however still recommend going through as there may well be things that you have not heard off that can pick up and after all this is required to obtain the full certificate at the end.

Course 2, Ask questions to make data-driven decisions. This for me was where thing got interesting. The course takes you through how to ask the right questions after all its all good being able to perform great data analysis however if you don’t ask the right questions then your analysis will not produce the desired outcome. Coming from a background in science most of this was already things that I knew and had experience off however it is always good to get another perspective on things and a definition and process to follow. This is also were we get our first introduction to Kaggle a data science platform that will be explored in more depth later in the course and where a large number of the datasets used will come from

 

Course 3, Prepare data for exploration. Here we are introduced to data types and structures,  as well as data ethics and data organization and storage. We are also introduced to databases and learn a little bit about we can use them including a brief introduction to SQL which is explored later in the course. As with the to previous course this course is about building your knowledge of the world of data without actually doing much data exploration. The aim is to prepare you for what comes next rather than teaching skills that you will require. Whilst some may find this a bit of a waste of time, these course do a great job in laying that foundation that we can then build on with key principles introduced and a basis for how to perform data analysis is introduced. Even if you have experience of some form of data analysis these course are great for identifying bad habits and gaps in your knowledge that will help you going forward.

 

Course 4, Process data from dirty to clean. So after three largely theoretical courses we are finally here, its time to get properly hands on and start performing data analysis. This course takes us through how to process and clean data in excel including key functions and processes to follow. It also gives us are first proper introduction to SQL and how we can clean data in SQL. It also takes us through the importance of data integrity and why we should always document any changes that are made to data. Overall, I rate this course very highly, it is comprehensive and the instructor Sally is fantastic at explaining the different processes and is clearly well versed in data analytics as you would expect from an analyst at google. In a previous career I spent 3 long years as a secondary school teacher and as such whilst I believe that the set up and delivery of the content is first rate the course fails to build on this when it comes to the tasks that you are set. Most of the tasks that you go through are the same ones that are gone through in the videos. I found myself pausing the videos to follow along and then having completed this moving onto the task to find that it was the same thing. I recognize that on an online course it is difficult/impossible to provide individual feedback and so setting marked coursework can’t really be done. However only once in the entire course is a task set asking you to use previously learned knowledge and apply it to a new set of tasks with a cheat sheet provided with the solutions. I wish that there was far more of this in the tasks as I feel it would have prepared me more for the capstone project at the end. It is only fair however to point out that this is my only major negative gripe about this course and overall I really enjoyed working my way through it.

Course 5, Analyze data to answer questions. Ok over halfway and into the really exciting parts of the course. This course takes us through how to organize our data using both spreadsheets and SQL, how to format and aggregate our data and finally how to perform simple data calculations. Again our instructor  Ayanna is fantastic Google really did a great job in picking its presenters all of them are excellent. As should be expected the course utilizes google sheets as the spreadsheet program of choice but provides options for using excel as well. If I had a criticism of this course is that it is still very basic and anyone familiar with spreadsheets will be familiar with most of the concepts presented however there is always something new to learns and the SQL part of the course is truly excellent. I do wish however as I previously stated that more hands-on options were given for practice.

Course 6, Share data through the art of visualization. This was the course I was most looking forward to learning about Tableau and dashboards for presenting data. However, I was quite disappointed with this section as it didn’t live up to my expectations. As always Kevin our instructor is excellent, but I just feel like this course could have delivered more. This of course could have been me hoping to learn things that I didn’t know and magically improve my data visualization. I felt as though there was not a lot of hands on practice as in other course, here most of the work had already been done for you it was back to largely lecture style videos which take you through everything without you doing to much. This is of course just my opinion and maybe my expectations were too high, I will definitely be exploring other course on visualization tools after this course however.

Course 7, Data analysis with R programming. Ok so as much as I was disappointed with the previous course this one is superb. It is a great introduction to R and programming. Back when I was working on my PhD I did a little bit of programming in Python and found it mystifying. I could not get my hear around the syntax and even after months could only do simple functions to manipulate the codes that I was running. Carrie our instructor does a brilliant job at decoding (pun intended) the mysteries of R programming. We get introduced to the basics, how to create dataframes and ggplot2. Again, we could have received more hands on problems but as this is something that most people will not have come across before just following through the problems presented in the videos and subsequent tasks will teach you a massive amount about coding in R and potential problems that you will encounter. By far my favorite section in this course was the visualization section. Being able to manipulate graphs and charts by typing code is something that I have found that I really enjoy. Indeed it is the reason why I still have not fully completed the capstone project as I just keep playing around with R and pulling more visualizations out of the data. This is by no means a comprehensive R course and I would recommend that you expand on this if you want to however, I found that this course and choosing to do a capstone project only using R was enough to vastly improve my coding skills.

Course 8, Google data analytics Capstone: Complete a case study. Ok so it is finally here the part we have all been working towards, the capstone project. As I stated above, I chose to use R for my capstone project. The reasons for this were that this was the programming language that I was the least familiar with and so wanted to use the project as a way of developing my coding skills further. The project could just have easily been completed using SQL in combination with Excel. There were three possible case studies to choose form, first the Cyclistic case study analyzing data from a bikeshare company. The 2nd project was looking at how a wellness company can play it smart analyzing how consumers use their smart devices. The final option was to choose your own project and analyze a dataset of your choosing. I chose the Cyclistic dataset for my project. I used to run triathlons and the idea of exploring the cycling dataset appealed to me more than the other projects. I intend to go back to look at the 2nd option using SQL and excel to complete this project. The capstone project for the two main options does a fantastic job at guiding you through the data and what questions need to be asked and therefore what analysis should be performed. There is also a guide on performing the analysis in all of the programs discussed during the course. This is however very limited analysis and I found myself wanting to do more with the data at hand. This was probably the only downside to the capstone project. Therefore, once I had answered the main questions in the course outline, I expanded on the analysis to be performed, further expanding my R programming knowledge and learning new skills. You can find this project in my portfolio on this website.This is the part of the course I enjoyed the most. Putting all the knowledge and skills I had learned or gained to use and finally being able to add a project to my portfolio. Hopefully the first of many.

 

Overall, I would highly recommend the Google Data Analytics Professional Certificate especially to people who don’t have a strong background in data analytics and excel experience. The courses are well structured and presented and although there are improvements that could be made, I thoroughly enjoyed the course for the most part and especially the capstone project. I learned a huge amount about how to structure a data analytics project and key concepts in exploring, cleaning, structuring, analyzing and presenting data. I was even more excited to hear however that Google has just released the Advanced Data Analytics Professional Certificate which I have already enrolled in and am loving. I will review this at a later date however if you have a background in data analytics and want to advance your data science skills I would highly recommend checking it out.

Until then keep exploring the data. Neil