I Attended A 4-Weeks Data Analytics Course With Zero Coding Skills – Here Are 4 Things I Learned

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Too often, we don’t learn something new because we either think it is beyond us or that we have passed that stage. The reality is that it’s our limiting beliefs that are holding us back.

To use an analogy, if we haven’t learned to swim at 20, ride a bicycle at 35 or drive at 50, there’s no reason to think that these skills are beyond us or that it’s too late to learn them. And, it really shouldn’t matter if we switched up the ages either.

We should simply think of them as skills we want to pick up. Similarly, if we are working today, and want to pick up new skills – difficulty or age should not be the stumbling block. All that matters is whether we are committed to learning the new skill.

As a caveat, of course, we need to understand that some skills will take longer to learn. And, to truly master a skill, it takes time.

I Decided To Take Up A 4-Week Data Analytics Course

Over the past four weeks, I’ve been kept busy trying to learn data analytics. At DollarsAndSense, we constantly look at data to guide our decisions in creating content as well – and I thought this would be a good opportunity to look into building up this capability. So, even though I had zero coding knowledge, I decided to take up the 4-week – 8 lessons – data analytics course with Smartcademy.

I’m not alone in thinking that having data analytics skills can lead to better outcomes for myself either. According to the SkillsFuture report on Skills Demand for the Future Economy, the Digital Economy was cited as one of three key growth areas for Singapore, with the Green Economy and Care Economy being the other two. Within it, Data Analysis/Analytics was the second most in-demand job role.

Smartcademy offers a diverse range of tech-centric courses such as  Digital Marketing, User Experience Design, Blockchain, Design Thinking, and more. These courses aim to equip attendees for roles in the Digital Economy.

While I will not proclaim to have mastered data analytics after completing the course, here’s what I did pick up.

#1 Anyone Can Benefit From Picking Up Data Analytics

Throughout the course, we were broken into smaller groups to get hands-on experience working on the solutions. Again, I’ll be the first to admit that I usually learned more from my peers than I imparted to them.

During the different lessons, I was able to interact with various course mates. I spoke to a tertiary student who was on an internship, a relatively-young looking accountant, a gentleman in his 50s who had a much better understanding of the topic than me, and even a radio show producer who was switching to the communications industry.

I could tell that this was a diverse group. Like me, many of them had the support of their companies and colleagues – and wanted to start building such a capability within their existing job roles. There were also others, like the radio producer switching into a new field, who thought that learning data analytics would equip her to do better in a new career.

#2 Having Data Is Great – But We Need To Be Able To Analyse It With Tools Such As Python, SQL, And Perhaps Others

Throughout the course, the instructor gave us examples of how data is collected. The reality is that data is all around us, and many companies already have access to a lot of data. One problem is picking out the specific data that we need.

Far from the perfect sets of data we practiced on in class, he shared that raw data we get in the real world will typically be riddled with issues. Common problems could be missing data, poor data structure, data that has to be merged with other data points, and many more.

Hence, we need to clean up the data – by learning to use tools such as Python and SQL. Even though they may be alien-sounding, even someone with no coding skills can start learning them.

Cleaning data can be quite mundane (one more reason not to think it’s a skill beyond our abilities), but it helps us understand problems within our data and think about it more deeply.

If you guessed it, yes! Before we even start on the data, we have to understand some basic statistical knowledge and language. This will help us frame the problem and input the codes accurately.

Fortunately, we don’t have to memorise any of the coding languages. We can pick up some basic ones with enough practice. To input codes that are more intermediate (to us beginners, at least), we can actually learn a lot from online sources by “Googling” other individuals’ problems and the solutions that were suggested.

One of my course mates also sent me a handy link I could go to get solutions if I ever got stuck on a problem. Thank you!

#3 We Need To Tell A Story With Data

After we’ve cleaned our data, we need to understand what the data is telling us. This means presenting it in a visual format. In the course, we learned data visualisation with Python and Tableau. Having learned to use Tableau, I think that’s the one we should aim to use.

The instructor shared, and I fully agree, that no one is going to appreciate lines after lines and columns after columns of data you’ve cleaned, so don’t even think of presenting excel sheets or raw data.

Putting our data in a visual format is akin to telling a story. For example, we can use a bar chart to detail profitability by year or by product type. We can use a line chart to depict revenue trends. We can use a pie chart to show the proportion of revenue that is exposed to a single client, industry, or country.

Above is an example of a Tableau Dashboard I created using data sets provided in class. These can be simply created after uploading our cleaned data sources in excel sheet.

We also have to remember that these visuals are simply tools for us to present a story. For example, if we are selling many products, and the data shows that profitability for a certain product type is low, we need to be able to present it in a visual format – such as the Profit Trends line chart (bottom left). The narrative is yours to control after that – whether you think certain measures need to take place. The data and visuals simply back up your narrative.

#4 It Has Always Been About How Much (And What) You Want To Learn

As the adage goes, “Rome was not built in a day.” I gained a great deal of knowledge on the topic and learned to use tools such as Python, SQL and Tableau, but anyone will agree that we cannot stop learning about data analytics after taking a 4-weeks course.

A short course like this gives attendees a good grounding in the topic. In fact, Smartcademy shared with me that a significant number of individuals have found data-related roles after taking up their course. Nevertheless, we must continue building on and practicing what we’ve learned in the course to succeed in the field. We can do this by attending other physical or online courses, read forums, or even run through the problems that we encounter with friends in the field.

I also subscribe to the motto that practice makes perfect. For the capstone project – a final project we had to submit at the end of the course – I found out exactly what I hadn’t absorbed during my classes, which brings home the point that you can’t learn everything by theory and a single walkthrough alone. When picking up a skill, it’s all about practice, practice, practice.

We need to stay open-minded and inquisitive. We can go online to try to find solutions on our own. Sometimes, there is a quick fix. “Figuring it out” this way also gives us the most gratification (at least to me). If we need more help, we can ask questions on the Telegram group that we have access to from Day 1. The instructor and other teaching assistants would be around to provide help whenever we need. From my experience, I found my peers to be just as helpful. I remember asking a question during the course, and one of my classmates, whom I had a chat with before, texted me privately to ask if I had solved the problem.

Lifelong Learning Is The Only Way To Remain Relevant In The Workforce

Traditionally, acquiring skills is broken into three phases in our lives. 1) Going to primary and secondary school to learn basic subjects; 2) At our tertiary stage, we should think about the career we want to enter and learn the relevant skills.; and 3) specific training that happens within the confines of the functions we perform on the job.

Adding a fourth element to the learning phases is picking up entirely new skills when we are already in the workforce. This can naturally shield us against being made redundant, and be especially important as technology is rapidly advancing. More than that, we can also set ourselves up to enter exciting new fields or roles with better job prospects and remuneration in the #NewNormal.

The Data Analytics course I took with Smartcademy has equipped me with a new and valuable set of skills. But I need to continue putting it to work, either with DollarsAndSense and/or on my own, to build my expertise in data analytics.

I’ve started looking at DollarsAndSense articles that give us the best affiliate revenue. Currently, we are probably not tracking it in a way I would describe as a “best practice”. As an online publisher, we’re already collecting data via Google Analytics (providing page view numbers) and tools such as Rebrandly (tracking clickthroughs).

Using this data, and cleaning it up in excel, I can start creating a story illustrating exactly which articles and how many articles are giving us the bulk of our current affiliate dollar returns. This information can then guide the team to turn up advertising dollars on the articles to scale up our returns or even create similar content for other affiliate partners.

Again, this is just one way (and only the first way) I have thoughts about utilising my newfound data analytics knowledge to improve our revenue and data collection DollarsAndSense. There are probably better and more useful ways to apply my knowledge, and with more practice and self-learning, I will hopefully figure that out.

What gave me even greater confidence to build my skills in a new area is that the government is encouraging individuals to pursue lifelong learning. For example, my Data Analytics course is accredited under the Institute of Banking and Finance Singapore (IBF) Standards Training Scheme (IBS-STS). Eligible Singaporeans or PRs can receive up to 90% IBF funding support. The remaining amount of the course fees can also be paid with my SkillsFuture credits. Upon completion of my course, I will be presented with a professional Data Analytics Certificate, which will be a valuable add-on to my resume.

Smartcademy’s next Data Analytics course starts in September 2022. You can also look at other relevant courses it offers, including Digital Marketing, User Experience Design, Blockchain, Design Thinking, and others.

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