On a similar note, Joseph Misiti has compiled a large list of machine learning specific resources. The list is titled, Awesome Machine Learning, and it includes resources for various languages, NLP, visualization, and more.
Both lists are on Github, so if you notice something missing from the list, feel free to add it. Contributions are welcome.
As the field of data science continues to grow and mature, it is nice to begin seeing some distinction in the roles of a data scientist. A new job title gaining popularity is the data engineer. In this post, I lay out some of the distinctions between the 2 roles.
A data scientist is responsible for pulling insights from data. It is the data scientists job to pull data, create models, create data products, and tell a story. A data scientist should typically have interactions with customers and/or executives. A data scientist should love scrubbing a dataset for more and more understanding.
The main goal of a data scientist is to produce data products and tell the stories of the data. A data scientist would typically have stronger statistics and presentation skills than a data engineer.
Data Engineering is more focused on the systems that store and retrieve data. A data engineer will be responsible for building and deploying storage systems that can adequately handle the needs. Sometimes the needs are fast real-time incoming data streams. Other times the needs are massive amounts of large video files. Still other times the needs are many many reads of the data.
In other words, a data engineer needs to build systems that can handle the 3 Vs of big data.
The main goal of data engineer is to make sure the data is properly stored and available to the data scientist and others that need access. A data engineer would typically have stronger software engineering and programming skills than a data scientist.
It is too early to tell if these 2 roles will ever have a clear distinction of responsibilities, but it is nice to see a little separation of responsibilities for the mythical all-in-one data scientist. Both of these roles are important to a properly functioning data science team.
There are an abundance of statistical programming languages available, and the fine folks at DataCamp started to compile some of the data about the languages. They then produced the infographic at the bottom of the post. To start with, SAS, R, and SPSS are the 3 languages being compared.
Here are 3 bits of information based upon the infographic:
If you want a job – use SAS
If you want to use the language of Kaggle winners – use R
If you want to read analysis in an academic journal – use SPSS
I would love to see Python added to the infographic, but it might be much harder to get accurate numbers for Python since it is general programming language not just a statistical programming language. I would also love to see some benchmarks around both speed and number of steps(lines) to complete certain tasks. Anyhow, enjoy the infographic for yourself. Is there anything else you would like to see compared?