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…
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…
As data practitioners, we often need to deliver data to whoever needs it.
As time goes by, after hundreds of ad-hoc requests and SQL queries, you soon realize that many of these requests are similar, and can be automated.
At the same time, the traditional way of attaching your data as an excel file is way past us. It’s 2021, time to come up with innovative ways to display your data.
Have you ever wanted to extract some data from your Data Warehouse and send it through email, while having the whole process automated?
I’ve got just the…
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.
If you’re reading this, there’s a chance that you may be leaving or planning to leave your current job. Well, we’ve all been there.
I often hear people complaining about their jobs —
Everyone has their reasons for a job switch. Whatever it is, there are some matters to be tendered carefully so that we do not ruin our potential opportunity costs in the future.
Remember, you leaving the company causes them trouble, at least for hiring a…
Greetings data practitioners,
Welcome back to your weekly read of solving real-world problems with data science. Right on queue, one of the highest priority first world problems that Millenials face nowadays is —
Not knowing how to blow up on Youtube.
No worries, data can solve everything,
and we are going to solve the YouTube Problem. Today.
The world-famous video-sharing website. It is no secret that YouTubers make big bucks posting videos on YouTube. If you’re living under a rock, big YouTubers literally make millions per year, while only having to post 1 video every week.
This had further caused…
Greetings Data Practitioners,
Welcome to 2021, we made it.
Last year was a tough year, I think we can all agree to that.
However, among the hardship, be thankful that you are in a field that is still highly in demand despite the scary situation we are all in.
Data never sleeps, even if there is a pandemic going on.
Hence, it is extremely important that you are aware of the trends that are leading the direction of the data industry. Things like —
Greetings data practitioners.
Welcome to 2021, where the new trend is all around Machine Learning.
Most employers, even data scientists themselves, are drowning in the thought of deploying that new Machine Learning model that predicts literally everything for them.
If you have ever been in the field, you would understand that most organizations nowadays don’t have the sufficient fundamentals to even venture into Machine Learning, but they still want to.
They have not even scratched —
That being said, data…
Greetings data practitioners,
I assume you are aware of the increase in demand for people who know how to communicate data. This is exactly why we should always seek to improve, learn more, and sharpen our skills in the data science industry.
Even with all the hype around Machine Learning, Data Visualizations do not lose their importance. Exploratory Data Analysis is often required before any Machine Learning is even planned out.
Large tech companies often need to communicate the data they own.
We are the ones that execute.
A very good example would be google and their known, google trends.
if you’re like me, you like data.
Data can tell a lot of stories when it’s properly presented.
We live in an era where people depend on data for business decisions, which is exactly why presenting data professionally will put you ahead of others.
Visualization gives you answers to questions you didn’t know you had.
— Ben Schneiderman
We can achieve this by using amazing visualizations. We’ve already talked about how you should take advantage of powerful libraries we currently have in Python.
In this write-up, we’re taking it to the next level. We’re going to talk about…
Data Scientist turned Engineer — Crunching data and writing about it so you don’t get headaches.