Nicholas Leong

I crunch data so you don't get headaches.

[Nicholas Leong @nickefy]

A data consultant and a data engineer walk into a bar. The engineer says, "I'll have a craft beer, and my friend here will have a clear explanation of our infrastructure setup."

The consultant replies, "I'll just have water!"

I'll see myself out.

[Jaewon Yun @JaewonYun4]

Python for Data Science — A Guide to Pandas by @nickefy in @TDataScience https://t.co/8mpZwiQFay

[Nicholas Leong @nickefy]

You've heard of DEV, UAT, and MASTER git branches for Software Developers. But have you ever wondered if it's the same structure for Data Engineers?

https://t.co/BTSqpquRD8

We too, have to build and maintain products, it's just data products. #DataScience #programmer

[Nicholas Leong @nickefy]

You've heard of DEV, UAT, and MASTER git branches for Software Developers. But have you ever wondered if it's the same structure for Data Engineers?

https://t.co/BTSqpquRD8

We too, have to build and maintain products, it's just data products. #DataScience #programmer

[Nicholas Leong @nickefy]

It's 2021, stop using Matplotlib and Seaborn. Plotly offers full customization and interactivity with your graphs while remaining aesthetically pleasing.

https://t.co/eo2oZKmnU1

I'll convince you it's the best data visualization library out there. #DataScience #Python

[Nicholas Leong @nickefy]

If you had invested $100 in S&P500 back in 1950, your investment would amount to >$100k today.

https://t.co/o5PjLshGse

I simulate actual investment returns using S&P500's historical data along with inflation to estimate the time taken for retirement #DataScience #money

[Nicholas Leong @nickefy]

If you had invested $100 in S&P500 back in 1950, your investment would amount to >$100k today.

https://t.co/o5PjLshGse

I simulate actual investment returns using S&P500's historical data along with inflation to estimate the time taken for retirement #DataScience #money

[Nicholas Leong @nickefy]

Being productive allows us to do things that we were never able to do before. It's not just at work, but life in general.

https://t.co/YIqkkbLnin

Here are a few tools that helped me be efficient at what I do. I hope it does the same for you. #productivity #DataScience

[Konstantin Lyakhov @rifcoru]

4 Data Pipeline Practices You (Probably) Didn’t Know About by @nickefy in @TDataScience https://t.co/YMaBMFWBgv

[Nicholas Leong @nickefy]

Do you know that anyone can retire in 10 years if they invest half of their income, regardless of how much they make? I've written about it here

https://t.co/IrxNPCnuL5

I've included an actual 10-year data simulation along with some graphs. Check it out! #DataScience #money

[Nicholas Leong @nickefy]

Implement these Data Engineering Practices into your Data Pipelines!

https://t.co/kqnyTbDQJW

Pipelines fail, it's inevitable. With these concepts, you will be able to sleep soundly at night even when they do. #DataScience #DataEngineering

[Nicholas Leong @nickefy]

People call the military the country's first line of defense, 
doctors and nurses the medical front liners. 
As data practitioners, we call ourselves the forefront of innovation.

#DataScience #dataengineering #MachineLearning

[Nicholas Leong @nickefy]

@NYCDataSci @NYCDataSci Anytime. Excited to see the news and content you have for us. I am a writer myself and am keen to share my experiences as well.

[Nicholas Leong @nickefy]

The common issue with Data Pipelines is scaling.
Deploying them the old way makes your files unorganized and your code unreadable.

https://t.co/r30KuMZfR8

Hence, I've designed a simple Apache Airflow framework to save time and ensure scalability.
#DataScience #DataEngineering

[Nicholas Leong @nickefy]

Machine Learning is Fancy.
But most of Data Scientists and Engineers spend time wrangling data. It is also the most asked topic in interviews.

https://t.co/AU6wLkNCBl

Get up to speed on the techniques in Pandas.
It is your first step into Data.
#DataScience #dataengineering

[Nicholas Leong @nickefy]

Youtube makes money.
In this article, we will use data to answer the million-dollar question - What kind of videos get more views?

https://t.co/9xlzioXGCj

We explore youtube data, specifically on the video title, tags, and category. You'd be surprised about the findings!

[Nicholas Leong @nickefy]

Youtube makes money.
In this article, we will use data to answer the million-dollar question - What kind of videos get more views?

https://t.co/9xlzioXGCj

We explore youtube data, specifically on the video title, tags, and category. You'd be surprised about the findings!

[Nicholas Leong @nickefy]

SQL is the backbone of any Data Practitioner.
You have to get your data from somewhere before performing any sort of ML, analysis, or visualizations.
More often than not, it's from a SQL Database.

https://t.co/nkmGUpb6ue

Here are some SQL commands you might have missed.

[Nicholas Leong @nickefy]

Stop attaching data in emails as attachments!

https://t.co/jw2ylZRDJC

I designed a pipeline that Extracts Data from your Data Warehouse, transforms it into a Pretty HTML Table, and sends it as the Email Content itself using Apache Airflow. Check it out!

#Python #DataScience

[Nicholas Leong @nickefy]

Stop attaching data in emails as attachments!

https://t.co/jw2ylZRDJC

I designed a pipeline that Extracts Data from your Data Warehouse, transforms it into a Pretty HTML Table, and sends it as the Email Content itself using Apache Airflow. Check it out!

#Python #DataScience