If you’re in tech, chances are you have data to manage.

Data grows fast, gets more complex and harder to manage as your company scales. The management wants to extract insights from the data they have, but they do not have the technical skills to do that. They then hire you, the friendly neighbourhood data scientist/engineer/analyst or whatever title you want to call yourself nowadays ( does not matter you’ll be doing all the work anyways ) to do exactly just that.

You soon realise that in order for you to provide insights, you need to have some kind of…

All of us have heard about the title —
Data Scientist, the sexiest job of the 21st Century.
But is it really?

For many people who are new to the data industry, Data Science will be the first thing they hear about, and the title just sticks. Suddenly, everyone is either already a Data Scientist, or aspiring to be one.

However, that is not the only job title in the industry.

Data Visualization is the language of Data Scientists.
If you are in the Data Industry, chances are you would need to present data to someone. You can’t do that without some visualization magic.

As the tech industry matures, the demand for better visualizations increases. Each type of visualization has its specialty, just like how a line chart describes trends better than a bar chart.

Choosing the type of visualization is enough of a headache already.
Let’s not mess up the library which will be plotting all of them.

In 2021, millennials are determined to retire early.
The determination does not mean they are lazy. In fact, millennials are more determined to pursue their passion in life, after solving their financial problems.

However, determination does not equal results. Data shows that younger generations are actually having difficulties retiring early compared to previous generations.

Specifically, according to Business Insider, in 1989, boomers (born between 1946 and 1964) were roughly the same age as millennials today (born between 1981 and 1996). …

Hi Everyone,
We’re here to talk about productivity and organization today.
If you work in data, it is even more important to get your life organized,
as your data won’t be.

Productivity is being able to do things that you were never able to do before — Franz Kafka

When I was young, I remember not needing any reminders. I would be able to remember every meeting or errand I had to run. I also thought that taking notes in class was unnecessary, that just reading from the full text would suffice.

Oh, how times have changed…

As we grow…

Hey everyone,
behind every tech product, there is automation to be done to keep data clean and up to date. It's 2021, only knowing how to build cool data models isn't sufficient to survive in the data scientist space. You have to dive deeper.

Nowadays, data pipelines are getting more prioritization.
This is mainly due to the maturity of the industry. Data scientists are realizing that to get clean data for their models, they have to build a solid foundation for their data warehouse.

Therefore, besides dedicated data engineers, data scientists have to understand data pipelining themselves. In this article…

If you’re like me — you like tech, and data.
In this digital era, people are starting to realize that technology is bringing insane benefits to the human race, and it's up to humans, to drive it.

Yes, that's right, and I have heeded my call.

People call the military the country’s first line of defense,
doctors and nurses the medical front liners.
Well, we call ourselves the forefront of innovation.

I believe at some point, you’re a little bit curious about how to enter the data industry. What skills are required and how do you start?

…

Greetings Data Practitioners.
It's 2021, Data Engineering is new the Data Science.
There is an increase in time and investment spent
in the Data Engineering space.

How often have you built models or performed analysis to only realize there was something off with the data you used? — Pretty often if you’ve been in the industry for a while.

To clear our doubts, we often have to consult data engineers to verify the integrity of our data. If we’re the data engineers ourselves, we’ll have to dig into data pipelines to understand the ETL of our data.

As the data…