What is object oriented programming?

‘Object-oriented programming (OOP) is a computer programming model that organizes software design around data, or objects, rather than functions and logic. An object can be defined as a data field that has unique attributes and behavior.’ — Alexander S. Gillis & Sarah Lewis

In Python, everything is an object. Each object contains its own attributes and methods. How do we know this? Check this out:

x = 'string 'x.capitalize() #will be 'String 'x.isalpha() == True #will be Truex.rstrip() #will be 'string'

In the example about we created a string, known by the…

Neural networks are one of the most discussed algorithms in the data science field today. Going all the way back to the 1940s, the idea of a neural network began with a paper by Warren McCulloch and Walter Pitts, a neurophysiologist and a mathematician respectively. Neural networks take inspiration from the human brain and the function of neurons. Their job is to simply recognize patterns. A neural network can perform most of the types of tasks we face in data science from classification to regression and do even more advanced tasks like image recognition and so on. …

The job of a data scientist is to make sense of the data. With that in mind, there are definitely some crossovers into many other fields that thrive on the scientific method and experimentation like medicine. When learning about the process of experimental design, we learn that things can be measured as long as they have something to be compared to. There are many ways to do these kinds of tests, but for now I am going to shine the light on A/B Testing and Multi-armed Bandit Tests.

What is A/B Testing?

We have two different groups of randomly selected…

Clustering is a powerful unsupervised learning technique. Its purpose is to identify specific groups within data that has no prior set labels. I learned about various methods like K-Means Clustering and Hierarchical Clustering, so I decided to take this opportunity to dig deeper and learn something new, for me at least. I am going to explore the mechanics behind DBSCAN clustering, another powerful technique.

What is DBSCAN Clustering?

DBSCAN is an acronym that stands for Density Based Spatial Clustering of Applications with Noise. The key to understanding this is the phrase ‘density based’. …

There are many types of classification models in machine learning including Logistic Regression, K Nearest Neighbors, Random Forests, and Support Vector Machines. In my data science journey, I have learned a lot about the first three and wanted to take this opportunity to educate myself on the last one, Support Vector Machines.

How do they work?

The simple idea behind them is that they work to find an ideal line that best divides the data by class. This way the line becomes the threshold for class predictions. To find this line, a calculation is used to identify the maximum distance…

Need more data? No problem. Let’s say you have a very small dataset for your classification model and you want to give it more data to train on. One thing you can do is generate synthetic data. I am going to show you how to do this using a Python library called faker.

I have downloaded an advertising dataset from Kaggle for demonstration purposes. This is a preview of it:

This classification dataset is used to predict whether or not a person clicked on an ad. …

When learning Python for Data Science you start with datatypes, and then from there you go on to learning about data structures. Data structures are fundamental in this object-oriented programming language because they help to organize your information. What if I told you there are more than just the basic built in datatypes you’re learning about? If you take a look at the collections module you will see there are other options.

The collections module allows us to access new kinds of “container datatypes” that help to facilitate the storage of information. To name a few, the counter, namedtuple, and…

Zachary Greenberg

Data Scientist / Singer

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