upskill data science

Why You Need to Upskill in Data Science (With Kang)

May 12, 2022

The demand for data science and analytics talents in Malaysia still outstrips the supply. As more companies begin adopting data-driven practices to grow their business, current employees are finding themselves having to quickly upskill – or face redundancy.

In this post, we talk to Kang, a student who recently completed Data Science Uncut Bootcamp, sharing his experience of why it’s important to upskill in data science now.

Meet Kang.

Kang is a seasoned veteran in the manufacturing industry, having served in various capacities. He has worked in the industry as a test engineer, process engineer, quality engineer, and validation engineer.

According to Kang, he has specialized in products such as car radios, cell phones, security systems, hard drives, and card personalization machines.

We also learned that Kang is a certified quality engineer and a Six Sigma Black Belt engineer. Kang mentioned that there are similarities between data science and Six Sigma. In particular, certain analysis methods are quite similar.

Similarities between Data Science and Six Sigma.

The term Six Sigma refers to a set of quality-control tools that businesses can use to eliminate defects and improve processes to help boost their profit. 

A Six Sigma engineer utilizes various tools and techniques to improve business by reducing the likelihood of error. It is a data-driven approach that uses a statistical methodology for eliminating defects.

“In Data Science, we use Python. A Six Sigma engineer might use other statistical tools like Minitab,” said Kang

What drove Kang to pick up data science.

Kang was retrenched in 2017 when his company moved to Thailand. Despite being a certified quality engineer and having Six Sigma Black Belt certification under his belt, he recounts that he still took over 3 months to secure his current job. 

This made Kang realize that he needed to upskill to stay relevant and hireable in the market. At the same time, Kang was interested to be able to use his skills and experience to venture into freelancing – to help companies who need data analysis. 

“Although I am in quality assurance, data is everywhere in our daily interactions, and it’s not limited to a specific area. The concepts of data science can be applied to the manufacturing world as well,” said Kang.

Kang believes that as more companies adopt big data practices, picking up data science means he would be able to provide value to companies – even when he has retired. He also mentions that he decided to pick up data science, so as not to confine himself to just the manufacturing world but being open to working in other industries.

How Kang overcame his challenge in programming.

“I struggle when it comes to programming. But when it comes to data analysis, I’m familiar with that”, said Kang. 

Kang realized that data science isn’t only about programming. One has to have an analytical mindset and domain knowledge as well. Programming is only a tool. 

Kang’s presentation during the Demo Day.

He eventually learned that even as someone without a programming background, programming for data science using Python is very doable. You don’t have to code like a programmer. Especially with Python, the code syntax is simple and there are many Python libraries that you can use.

How Data Science Uncut Bootcamp made Kang more data-skilled.

“Previously my analysis methods were very manual. Now, I incorporate some of the data science practices such as automation to save time,” said Kang. 

What data scientists should focus on is producing results, regardless of the tool they use. Be it an open-source tool like Python or enterprise software like Tableau. You could be really good at programming, but all companies want are results.

“What I loved about Data Science Uncut Bootcamp is that it was a ‘Cook Book Approach’ to learning data science. If you follow the steps, you’ll be able to complete the task – even if you are a complete newbie who has never done programming.” said Kang. 

See Kang’s full Data Science Portfolio here.

In the data science Bootcamp, participants like Kang didn’t learn to program with Python like a developer. Instead, the Bootcamp shows how to apply programming to solve data science

Advice for individuals who wants to break into data science.

When asked Kang for advice he would give it to other people, and here’s what he said. 

“You will struggle over learning data science. But with a program like Data Science Uncut Bootcamp, that follows a step-by-step approach, you should be able to complete it if you persevere,” said Kang. 

Unlike focusing on toy datasets and practice projects, in Data Science Uncut Bootcamp, participants get the chance to work on real-world datasets. This helps them be able to translate their learning into real-world practices. 

During Demo Day, Kang presented his findings on customer churn using a real-world data set.

Take your first lesson in programming for data science.

At LEAD, we have helped hundreds of individuals – including Kang, upskill in data. And we can help you do the same.

Data Science Uncut Bootcamp is a perfect program for beginners and intermediate working professionals to explore data science and pick up industry-relevant skillsets. You can visit the page and see when the next intake starts. 

If you’re not ready to join a Bootcamp, then we’d suggest you start by learning to program for data science with Python. Just take our free training where you’ll build a prediction model with Python

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