Today marks 3 days until the Christmas break and the end of 12 weeks of training to become a data analytics consultant. It is by no means the end but is absolutely a welcome break before the final straight.

If you haven’t read any of my previous blogs (I suggest you do here).

Now, that you’re back. My journey to a career in data started when I moved to Australia in April and wanted a change from teaching. COVID and a couple of international school positions that didn’t work out had left me a bit jaded (I chose 2021 to move to Russia). I was looking for some stability and a new challenge.

So fast forward and I will have completed week 12 of the 16-week initial training program by week’s end. I wanted to write this blog to reflect on my time so far and what I had learned and pass on any tips for people thinking of making a similar move. I know there is lots of interest in data as a career.

The Data School: Weeks 1-12: What has it been like?

I have really enjoyed the first 12 weeks overall. There have been ups and downs, good and bad days but overall, I have learned a plethora of new skills and become fairly competent in about 5 new pieces of software. The training focuses on Tableau and Alteryx, but we have also used MS PowerBI, Microsoft SSMS and learned a bit of SQL in my own time. So, there is plenty of variety for folks like me, who like new challenges and learning opportunities, to stay engaged. The down sides are mostly mental exhaustion and some late nights, especially when the client project weeks begin. It will be nice to strike up a better work-life balance once 16 weeks is up.

Weeks 1-5: Adding the Tools to the Talent

The Data School identifies that you possess some talent for working with data and have the necessary commitment and passion during their rigorous selection process. Once you begin, they set about training you in the tools that will make you a good consultant. This begins with some theory on best practices in data management and data visualisation. You then quickly set about implementing this through the main tools of Alteryx and Tableau. Expect to get comfortable presenting to your peers and colleagues from week 1 (our first presentation had about 60 attendees). Having said that the environment is very supportive, and the feedback is constructive.

During this phase you will be encouraged to write a blog a week to help you reflect on what you learn and consolidate your knowledge. You can also start working towards your certification exams in Alteryx and Tableau.  This phase of training is a bit like being back in university. Think 8-hour days of learning and projects to show that you are taking the material on board. The training is very good, and you have exposure to lots of experienced consultants for different aspects of the training. The topics range from data wrangling, spatial data, machine learning to agile project management, data modelling, dashboard building and server administration.

# Tip: Make sure you stay on top of your blogs and get to the certifications asap. Things begin to stack up in the client project weeks and time to accomplish these tasks is sparse.

Weeks 6-12: The Client Projects

Once you get through the first phase of training the workload intensifies and your opportunity to reflect and self-study evaporates. We began our client projects working with a social enterprise regulatory body. This was good fun and a little less pressure as we were working with the Sydney cohort. They were nearing the end of their training phase and getting ready to go on placements. The remote element of the team added some logistical hurdles, but we were able to come together and produce a good result for the client.

We had further projects working on-site with a major utilities company looking at their network maintenance realities versus expectations, this was using Tableau Prep and Tableau. We assessed training needs for the digital transformation and engagement team for a large charity using PowerBI and streamlined government reporting for a well-known University using Alteryx. Our last project week thus far was looking at staff churn and building an ML pipeline for an international construction materials company, again using Alteryx. So, you can see the breadth of clients and types of projects is quite varied.

The fact that you get the data on a Monday and deliver on Friday afternoon means that these projects are pretty time pressured. While they are great at showcasing what the Data School can achieve in short timeframes, it also means you can expect to be under pressure to find insights and deliver for the clients, particularly as you still train in the mornings.

#Tip: Timeboxing is key to successful client projects as is knowing when to focus on the deliverables. It can be tempting to “boil the ocean” and overdeliver but sometimes this led us into adding more time pressure and running down dead ends. Focus and meeting the needs and having a finished package before moving onto the extras.

Overall Musings

As I said at the top of the article, I have really enjoyed my time at the Data School so far. I get to work with passionate and like-minded people every day and apply newly developed (and long standing) skills every day. I would never have believed I would be able to achieve the things I am capable of today 12 weeks ago. Though I am a bit of a cynic. However difficult the project weeks are we know that real-world scenarios are normally allotted much more time and development. However, knowing what can be achieved in relatively short periods of time will certainly be helpful going forward. The resources you have access to are fantastic and everyone is eager to help and provide ideas, even with their own projects going on.

Do I have any regrets? Not really. I do miss the buzz of the classroom and being on my feet most of the day (looking at a screen is definitely something I will have to get used to) but I enjoy solving problems and every day is full of problems to be solved.

#Tip: Make sure you tap into the huge amount of experience and resources at your disposal. Don’t be afraid to ask for help and there is no such thing as a stupid question.




The Data School
Author: The Data School