Github is at the same time one of the most useful technologies developers use and one of the most annoying, especially for novices. I think things are compounded for beginners by having to learn terminal at the same time. This blog will go through what you need to do to get off the ground and offer some commentary from someone slightly above beginner level on common mistakes or why some things just are not intuitive.

Being a successful coder will always involve working with other coders on the same project. There’s simply not enough time or brainpower in the possession…


If you are an aspiring data scientist searching for a job, you are going to end up looking in all sorts of places and industries you may not know much about. Recently through a connection, I got the opportunity to interview as a data analyst at a consulting firm. Everything was going smoothly then at the end my connection asked me if I was familiar with a case interview. I played it off, but in reality had no idea what he was talking about. After some research, I learned that it is a business-minded interview specifically administered by consulting firms…


Database management is not as sexy as a lot of the machine learning content on medium, but it is important to understand at any data science position, from the most entry-level to the most senior. Today’s blog will be about database normalization. Database normalization at its core is about organizing a database and reducing redundancies to make it more efficient. In relational database management, this concept is incredibly important. If related tables contain duplicate entries, much of their purpose of gaining efficiency is lost.

The ideal table is limited to one purpose and each row is a unique entry. Say…


I am okay at python. I can use all the packages I know and quickly learn any that I do not. I have heard object-oriented programming (OOP) in the background a lot but it was not till recently that I took the time to fully understand why it is such an important development that forms the backbone of not only python but many other of the most popular languages.

What is coding like without OOP?

To fully understand OOP, I think it’s important to understand what the alternatives are. If you are only well versed in Python, like me, you might think that certain ways python…


source “OK what is a groupby object again?”

There are some functions or methods that you know … kinda; when you think about them its fuzzy and your knowledge is not to the point where five minutes of googling can fill all the gaps. Theres are often the functions that in the heat of a project you just end up avoiding and never getting better at. For me, one of those is the pandas groupby function. Groupby is incredibly powerful and I know how to get a sum or something simple but more advanced operations seem to require hours of trial and error to get exactly what I…


The learning rate is one of those pesky hyperparameters that as data scientists it’s our job to fine tune. It is the rate at which the weights in a model are updated based on the current error. The loss function is convex and for our purposes here continuous (If the loss function is not continuous then you can run into local minima that cause all sorts of problems but that’s a story for another day). In other words if we tried every possible weight a parameter then plotted the loss at each point it would look something like this:

If I have learned anything from watching online lectures about gradient descent, the worse your drawings are, the more likely you are to be a math professor.

Our…


There’s a common adage that data scientists spend 90% of their time cleaning data and 10% modeling. With image classifiers, it is more like 99% cleaning to 1% modeling. This is because a neural network needs images to be a standardized size. How many pictures do you come across on a google image search that are all the same size? There are a bevy of different approaches for standardizing images and it is important to remember that no method is necessarily better or worse than another. Each one has its own drawbacks and applications. Oftentimes your ultimate limiter will be…


I started watching the Deep Learning lecture series for insights on how to get PyTorch up and running and the ins and outs of Neural Nets. In the process, I have learned so much more than that. Sylvian Gugger and Jeremy Howard constantly make a tangible effort to teach machine learning in a way that will produce great products. In the second lesson, they show a simple way to get a GUI up and running for prototyping an image classifier application. …


I recently graduated from Flatiron’s data science Bootcamp. After graduation, I certainly did not know everything, but I felt like I had a pretty good grasp on a general process and workflow. Then I started reading about neural nets extensively and saw that they were quickly becoming the answer to a lot of machine learning problems. One creative example was using time series data; a modern neural network can be fed images of graphs and predict trends from visual analysis. Neural networks are incredibly flexible and you are only limited by your creativity in how you utilize them.

That being…


Do you ever get feedback on a project that just makes you cringe because it seems so damn obvious? That's what happened this past Friday for me. My teacher said we had a good project but the conclusion and business recommendation were not quite there. He suggested putting our fancy new model into business terms. Today I will do that on this blog and hopefully give you guys a road map for how to present the conclusions you reached based on your complicated model to non-data scientists.

First I will introduce you to our project a little bit. My partner…

Aidan Coco

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