Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Ways of doing Data Science Engineering and Machine Learning in R and Python
| Date | Stars |
|---|---|
| 2026-07-24 | 617 |
| 2026-07-25 | 618 |
| 2026-07-28 | 618 |
| 2026-07-30 | 618 |
| 2026-07-31 | 618 |
| 2026-08-06 | 618 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Data Science Engineering, your way [](https://gitter.im/jadianes/data-science-your-way?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) ##### An introduction to different Data Science engineering concepts and Applications using Python and R These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python. We will do this from a neutral point of view. Our opinion is that each environment has good and bad things, and any data scientist should know how to use both in order to be as prepared as posible for job market or to start personal project. To get a feeling of what is going on regarding this hot topic, we refer the reader to [DataCamp's Data Science War](http://blog.datacamp.com/r-or-python-for-data-analysis/) infographic. Their infographic explores what the strengths of **R** are over **Python** and vice versa, and aims to provide a basic comparison between these two programming languages from a data science and statistics perspective. Far from being a repetition from the previous, our series of tutorials will go hands-on into how to actually perform different data science taks such as working with data frames, doing aggregations, or creating different statistical models such in the areas of supervised and unsupervised learning. We will use real-world datasets, and we will build some real data products. This will help us to quickly transfer what we learn here to actual data analysis situations. If your are interested in Big Data products, then you might find interesting our series of [tutorials on using Apache Spark and Python](https://github.com/jadianes/spark-py-notebooks) or [using R on Apache Spark (SparkR)](https://github.com/jadianes/spark-r-notebooks). ## Tutorials This is a growing list of tutorials explaining concepts and applications in Python and R. ### [Introduction to Data Frames](https://github.com/jadianes/data-science-your-way/blob/master/01-data-frames/README.md) An introduction to the basic data structure and how to use it in Python/Pandas and R. ### [Exploratory Data Analysis](https://github.com/jadianes/data-science-your-way/blob/master/02-exploratory-data-analysis/README.md) About this important task in any data science engineering project. ### [Dimensionality Reduction and Clustering](https://github.com/jadianes/data-science-your-way/blob/master/03-dimensionality-reduction-and-clustering/README.md) About using Principal Component Analysis and k-means Clustering to better represent and understand our data. ### [Text Mining and Sentiment Classification](https://github.com/jadianes/data-science-your-way/blob/master/04-sentiment-analysis/README.md) How to use text mining techniques to analyse the positive or non-positive sentiment of text documents using just *linear methods*. ## Applications These are some of the applications we have built using the concepts explained in the tutorials. ### [A web-based Sentiment Classifier using R and Shiny](https://github.com/jadianes/data-science-your-way/blob/master/apps/sentimentclassifier/README.md) How to build a web applications where we can upload text documents to be sentiment-analysed using the R-based framework [Shiny](http://shiny.rstudio.com/). ### [Building Data Products with Python](https://github.com/jadianes/data-science-your-way/blob/master/apps/winerama/README.md) Using a [wine reviews and recommendations website](http://jadianes.koding.io:8000/reviews/) as a leitmotif, this series of tutorials, with [its own separate repository](https://github.com/jadianes/winerama-recommender-tutorial) tagged by lessons, digs into how to use Python technologies such as Django, Pandas, or Scikit-learn, in order to build data products. ###
Excerpt of 6,318 characters
Read on GitHub
112
The Gitter Badger · Gitter
1
Bitdeli Chef · Bitdeli · United States
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:26981ccc45411bc4, topic:tutorial, readme:tutorial