#iTrainYouGain Stories: Hear From Geologists Who Gained a Rock Solid Foundation in Python for Data Science
Ho King Siang, Maxolvin Sintore (Max), and Balqis Nasiruddin are data analysts for Shell Sarawak. Being trained in the field of geology, all three analysts could see the large role data science would play in the future and sought out our Python for Data Science course.
We caught up with them during the course to get to know them further, and are excited to share with you their thoughts on the importance of data science in their field—well, in all fields for that matter!
Changing Landscape of Industries
There are two ways companies can turn a profit. It’s either by increasing the cost of their product or making production cheaper. More efficient production means there will less likely be a leakage, therefore the product is cheaper. This also applies to the oil and gas (O&G) industry.
“A wave of digitalization is sweeping through the O&G industry. New digital solutions are being implemented in areas as diverse as geological surveying, drilling and refining. The result is significant efficiency gains and cost savings, ” King Siang told us. He explained that previously, data aggregation was done manually.
“Information was extracted from multiple excel sheets and it took a period of time to clean up and massage the data before any analysis work can be carried out. With Python, a script using “Requests” and “beautifulsoup” will fetch the data and we can use “Panda” and “Scikit” for analysis, which would reduce a great amount of time.” His colleague Max then added, “There are three experienced Python users in our office and our top management were impressed by their efficiency. They wanted to multiply these three.”
According to their colleague Balqis, not knowing Python meant that her ability to visualise information was pretty limited. “I had no programming experience so I had to rely on just Excel and Spotfire.” Aside from the ease of merging data, Excel also has an upper limit as to how much a spreadsheet can contain.
Another participant who wished to remain anonymous is currently doing a PhD in Statistics, and is a self-described “hardcore R user.” When she heard Balqis explaining about data visualisation, she chimed in and said “Python is easier as a language! It actually feels like a language.” She added that R is very academia centric and industry practitioners aren’t familiar with it. (If you’re contemplating which language to start learning first and would like to understand more on the differences between R and Python, read our recent comparison article here.)
We’ve talked about the reasons why the trio decided to take up our 3-day Python for Data Science course. Aside from the above-mentioned company pain points, there was also another external driver. The recent slump in oil prices meant that the company not only had to be more efficient in production but also more efficient in selecting where to invest their resources. O&G companies are cutting costs wherever they can, in order to survive.
This practical outlook grounded their decision to upskill. King Siang said that “we have to be competent and stay relevant in the current market.” Max shared the same sentiments when he added that “In order to stay relevant, you need to constantly upskill or else you have nothing to provide.” Compounding on that fear, he pointed out that newer graduates will come with basics in programming and data science.
Max also realised that most people who apply data science in their work aren’t from IT backgrounds. Anyone without an IT background but wants to upskill isn’t alone. Balqis added that for data analysts, having a degree isn’t enough. In the past, reading technical documents and manuals could land you a job but today knowing Python is expected from hopeful applicants.
For some professionals, the ever-changing job landscape pushes people to be better. Then there are those whose personal interests become their biggest motivating factor. The same can be said about these practitioners of the Earth Sciences. All three had specific interests that became their life’s fuel. King Siang said that while his day job was very technical, photography and graphic design were his passions. Max, on the other hand, said that he spent most of his time gardening. While playing with soil sounds like a geologist stereotype, he does away with soil and toys around with hydroponic gardening instead. Balqis wasn’t far off from her colleague’s creative side interests. She told us that she had a background in arts but university and career choices got in the way.
When asked whether her creative juices made her feel excited about data visualisation, she quickly exclaimed “Yeah!”
When technology subsumes tasks, anyone who wants to remain relevant must find additional value. This means the elimination of routine tasks, and the ability to focus on creative decisions. All jobs in the future will follow the same trajectory. The decision made by King Siang, Max and Balqis to upskill with Python signifies an important trend that will separate corporate working professionals who will thrive in the fourth industrial revolution apart from those who will lag behind.
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