Meet Dimitris: Data Science and Machine Learning: the wizardry of the 21st century

I have been the data science and machine learning trainer at Young Mavericks for 2 years now. With a computer engineering background, data science and machine learning got my attention during my studies, and still holds my interest very tightly, as to me it feels like the wizardry of the 21st century. I got in contact with Young Mavericks via one of the data scientists who was employed by Young Mavericks at the time.

Why work together with Young Mavericks?

Young Mavericks is the place where I can share my knowledge about things that excite me with people that are eager to learn, and also learn myself new things while getting involved in multiple, wildly different projects.

“Dimitris is young, social and informal, something that matches well with the rest of our company”

What does the machine learning training look like?

During the trainings we explore various algorithms for data analysis, and predictive models, and focus on their practical applications. My goal is to make sure that, after the trainings, the junior data scientists have all the tools necessary to tackle all kinds of real world problems.

What is the added value of this machine learning training?

In this training I challenge my trainees to fathom the various machine learning algorithms from head to toe. This is very important for the choices they make for future projects, because then they will exactly know what is happening with the data behind the scenes.

Some of our Data Scientists about Dimitris:

“Enthusiastic trainer that carefully and seriously thinks along with you in solving problems and issues.”

“Dimitris built up our training from Python basics to neural networks at a fast pace. He let us figure out code so we not only could understand it conceptually but also the logic behind it. And if we didn’t manage to figure it out, he was always there to help us (or to talk about XKCD comics, greek food or the aesthetic quality of your plots.”

ELT, ETL and Data Pipelines: loading Data in an Automated Manner and how to do it yourself

My name is Don and I am something between a data engineer and a data scientist. I automate repetitive tasks, generate insights from the data, manage projects and I often help in an advisory role for these processes. I especially enjoy the creative process required to solve problems using data.

Data Pipeline Implementation: how to do it yourself

These instructions build on what has been discussed in ELT, ETL and Data Pipelines. In that guide, we discussed the problems that arise in storing and using data for a company. In response to those problems, we introduced the concept of Data Pipelines, which helps the company become better aware of the data loading steps and incorporating these steps in the most optimal way to create a Data-driven Culture. We also discussed some specific tooling that can be used to properly deploy Data Pipelines.

Now that we understand the concepts behind Data Pipelines, we will now apply them to implement a functioning Data Pipeline. Just like most of our data engineering processes, we follow a step-by-step plan and provide an implementation strategy for each step.

Hopefully a step-by-step plan will give you a solid foundation when you are constructing your own Data Pipeline as well as the implementation methods. You can find the whole code on our Giftlab.