Reference.

Blog Summary

This blog is the second part of the personal capstone journey taken by me from my days in data science school — for added context to what will be discussed here, please refer to Part I of this series. This data science study is an attempt to showcase how engineering thought and data science practice can be combined together to analyze the aviation industry. Here we will look further into the data gathered in Part I of the study, observe patterns, discuss key takeaways from such patterns, and learn about a variety of regression models used in this study —…


Reference

Inspiration and Blog Summary

As a Mechanical Engineer turned Data Scientist, a large part of my time is spent on discovering ways in which both fields can be combined for the greater good. In my personal opinion, the mechanical engineering industry is in severe need of reform, through a manner that allows new and passionate graduates to directly implement their knowledge into industry, without the need for a PhD. Through this capstone project, I attempted to showcase such connections between engineering and data science by creating a potential business model used in aviation, by implementing a machine learning algorithm to solve a real-world problem…


Referenced from https://www.etsy.com/au/listing/621508533/abstract-branch-photo-tree-branches
Referenced from https://www.etsy.com/au/listing/621508533/abstract-branch-photo-tree-branches
Referenced from Etsy and originally by Gusto Creations. This image is specially chosen for Git’s consistent reference to tree branching!

Blog Summary

In this blog, we will go over a common problem of working on digital work, collaboratively in a real work environment. We will give a brief description of Git and Github, and how they interact with one another. We will go through some command line interface within the Mac terminal. We will also write a little Python through an IDE/text editor. Finally, we will go about setting up Git with a Mac-UNIX environment and how to successfully implement version control using Github.

The Need for Version Control

When it comes to creating digital projects, developers and designers will often undergo several drafts of…


As I have mentioned in a previous article found [here], I have been attending a successful bootcamp to receive a formal data science education. Recently, I have finally reached a milestone which had initially seemed impossible to accomplish — before and during my studies. I am proud to say that I have graduated General Assembly Data Science Immersive camp, the nationally notorious bootcamp able to prepare students for data science industry within three months, consisting of disciplined training and constant professional guidance. With General Assembly, I was able to touch upon many subfields of focus in data science as well…


One of the largest schools of interest in the vast world of data science is machine learning. Machine learning is a field of study focusing on having a computer make predictions as accurately as possible, from data. Today, there are two main types of machine learning used: supervised and unsupervised learning. In supervised learning, a programmer will have access to data that depicts an outcome from a certain pattern of features. The “outcome data” can then be used as a prediction measure for modeling a machine learning process, through training. In supervised learning, classification scoring metrics such as accuracy and…


Introduction

The transition from an industry to Data Science is a unique and an individualistic process. People from different backgrounds can teach themselves data science through textbooks, online resources, and/or through peers. However, often this is not enough to become an established data scientist in industry, as best practices and expectations may not be fully informed to self-taught individuals. While it may not matter to various employers where one is taught on how to be an effective data scientist, it’s usually more credible to express how a guided and structured form of learning has helped somebody enter the world of Data…


Hi, I’m Chris and I am Learning Data Science.

When I think of the ideal job in life, I think of one that demands critical thinking…where solving problems is an every day’s challenge; where practicing mathematics is foundational to your immediate success; and where you get to “do it all” using code! I’m Christopher Kuzemka and I am learning data science.

I’m a graduate from Hofstra University’s Fred DeMatties School of Engineering with a Bachelor of Science in Mechanical Engineering and a minor in Mathematics. I’m a certified E.I.T. with the state of New York and I enjoy programming. I…

Christopher Kuzemka

Data Scientist | Mechanical Engineer | EIT

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