Starting a new adventure

The world runs on data so I have decided to go back to my roots and begin a journey into Data Science with Python. Using my growing knowledge software software development mixed with Data Science will hopefully open up a path to becoming a Data Engineer.

This is kind of exciting for me because I love learning new things. Additionally this is a project my girlfriend and I are doing togehter. Although some aspects of Machine Learning are not entirely new to us as we took certain classes during college while studying Statistics which covered some of the basic principles used in Machine Learning and Deep Learning such as Logistic Regression, KNN, PCA, etc.

If these terms seem unfamiliar to you and you find the urge, to take a deep dive then maybe you should join on the journey as well.

Before that I will say why it is important to have a basic understanding of these concepts.

In the era of big data I think Machine Learning is a must know

Applications of Machine Learning and Deep Learning

Here are just a few of machine learning applications we use in daily life.

Virtual Private Assistants

Siri and Alexa are able to hear what You are saying and then transform your words into a correct output based on what you have said. In a process known as natural language processing and Natural Language Understanding.

Image Classification

One big aspect of machine learning and deep learning is the ability to classify images. An example of this would be face detection software many people use on their phones today.

Optical Character Recognition

How are machines able to scan bar codes and read mail addresses. These things would be tedious to do manually but with the help of machine learning characters can be scanned easily and efficiently, using Optical Character Recognition.

Recommendations

How is Amazon able to recommend items that we would like based on what we have bought previously?

This algorithm collects data on your shopping history and compares it to other users with your similar buying habbits and ranks the top items bought together, this is where the other “users who bought this have also bought this” comes in.

Where to start

Because we are in the age of massive information overload we might ask where to start on a Data Science Journey.

There are two sure fire ways to get lost when learning new technologies today.

1. Not Starting at all
2. Being overwhelmed by too much information.

Point number two hits close to home because as someone who loves learning I can sometimes spend hours without having a clear path to learning. Jumping from tab to tab reading and not doing.

Solution

Because I wanted to have a clear path I decided to look for books or a guideline to help. After some research my girlfriend and I found this…

I can not really advertise learning in 3 months watching videos at 2x speed but because this is not something we are entirely learning from scratch I can be okay with it.

So for the next 3 months we will be on this epic journey and we will keep posting our progress so far.

Outline

Based on the video we decided to build a calendar outline that anyone could follow along as well. The outline contains 3 spreadsheets based on different months and the courses we will be taking during those months. Feel free to use our outline as well.

3 Month Data Science Outline

Progress

• Week 1: Introduction To Python for Data Science (Blog Post To Come)

Marcus Crowder

I have fun solving problems and breaking things