Hi everybody who is on the screen right now.
If you click on this story, I would like to thank you for reaching this page.
My name is Pathairush Seeda, or you can call me PAT. I was born in Thailand, and now I’m 28 years old. I’m a little brother from a Thai family.
I’m now working as a data scientist/engineer in Thailand's top 50 listed companies.
Also, I have been writing a Medium since October 2020. …
Learning new trends from watching Korean Netflix’s series.
Spoiler alert: this article may contain information about this drama. Please feel free to skip it first if you have not watched it yet. But, if you don’t mind, let’s dive in!
Recently, I have watched the Netflix series called STARTUP. It’s a Korean drama that is on-air every SAT and SUN at 9 PM. The story is about a group of people who dream of establishing a startup business on their own.
Seem straightforward and not interested, right?
But, the exciting part is that the main character of this series is a
Data analytics, science, and engineering have grown much popularity in the last few years. It creates a new standard for the industry. Every company needs to invest or establish a data office within their organization.
It becomes standard in 2020 that you can have a prediction model for marketing leads, improving your check-in method with facial recognition., or looking at the elegant dashboard for making a business decision.
Exceptional use cases always come first to build the momentum of the analytics trend. Executives want to see a result before investing a massive amount of funds into a new direction.
The technical problems are hidden under those use cases. When we are doing the analytics alone or with few people in the group without a proper working standard, it is easy to make a mistake without noticing. …
I point out the importance and data quality issues in the previous article.
The quicker you realize the problem with your data, the better you can deliver a valid conclusion to drive the business.
When you have limited time to do the analysis, I hope this tutorial helps you like a checklist for ensuring the data condition before presenting to the audience.
Today I will show you the
code snippet for checking the data condition. The topics will cover units of analysis, missing values, duplicated records, Is your data makes sense, and truth changing over time.
The tutorial will be written in the
pandas library. The most famous data manipulation library in python. …
Time is limited, you have to spend it wisely
In the working world, everyone is in a rush. For the company's high-level executive, their calendar has been filled with a lot of important meetings.
Your 1-month project has to wrap up and present to them within 30 minutes or less. You have to give them all the needed information for a decision.
Everything has to be well prepared.
There is no room for any struggle, confusion, and ambiguity. The presentation deck needs to be clear and precise enough to move forwards with any actions.
How could I make it perfect in the first month of working after graduating from college? That’s what I asked myself. The answer was NO. …
Office workers, White-collar, and Salary-man describe those who work for a company to receive their wages at the end of each month. I am one of them, and I have worked as a salary-man for five years in Thailand.
Disclaimer: This is my thought. I am only 28 and work only in Thailand for my whole career life. The opinion on each topic could be varied depending on the reader’s location and culture.
The salary-man has a very stable behavior in their working life. They usually wake up in the morning to start working from 9 to 5 during weekdays. …
Outstanding features can be used across many applications.
In my earlier article, I’ve already pointed out the 5 fundamental domains of feature engineering. It involves statistics, time, ratio, crossing, and geo-location domains.
To add value for our reader upon that point
Today, we will focus on the customer level feature. The customer level is the most entity we deal/talk about with. We often touch them individually through various campaigns.
Also, we can use the customer level feature for explaining the persona of the whole portfolio.
In this article, I will illustrate a
code snippet that you can make use of your machine learning model instantly. …
Feature engineering is a fundamental stack of building the data science model. We can use the features both in data analysis and machine learning models. The informative feature guides you to the incredible underlying insight.
You can make an impact decision that you have never imagined before.
We usually start the analysis with the raw data set. The data stores in the transaction level that each row specifies what item and how much quantity has sold. Feature engineering is to
Disclaimer: I took my master’s degree in Applied statistics from the National Institute of Development and Administration (NIDA), Thailand. The following is my opinion on the journey through this course compared to my online learning experience.
The quick answer is “Depends” on what you are looking for.
In a data-driven generation, we decide on the concrete data analysis report.
The consequence of the decision could be worth nothing to a million dollars.
The data always makes you feel confident and comfortable before taking any big actions. However, What if the analysis you have is wrong? What would it be then …
As a matter of fact, data validation is a critical process of any data operation ranged from reporting, dashboard, and modeling. If you crunch the flawed data, you cannot expect any right action from it. …