About the Data Science category

About the Data Science category
0

#1

Discuss data science, machine learning, statistics, and anything related to working with data.


#2

I think we should keep data science as a unique category and see if people start asking questions. I would agree in fragmenting that into other ones if people start asking too different questions under that category.

Actually Quincy, I would suggest something like the following. Usual questions in the DS room:

  • Becoming a Data Scientist (what to do? courses? difference to other titles?)
  • Best data sources for data science practionners
  • Best Tools for Data Science (libraries/packages in R? Python? Scala? JavaScript?)
  • Help with Code Snipes
  • Best Methods to solve a problem (ML or unsupervised? DL?)
  • Fundamental Concepts
  • Data Visualization
  • Data Science Events
  • Ethics in Data Science
  • Showing my Data Science Project!

I think this is a good list? If we target user interests instead of technical DS topics, I think that would make more sense…

Categories could be like:

  • Data Science: Events
  • Data Science: My Project
  • Data Science: Courses and Tutorials
  • Data Science: Data Sources and Real Practice
  • Data Science: Code Revision
  • Data Science: Which method should I use for…?
  • Data Science: Fundamental Concepts
  • Data Science: Data Viz
  • Data Science: Ethics
  • Data Science: Languages, Packages and Libraries
  • Data Science: Big Data and Cloud

And probably…:

  • Data Science: Getting jobs

Proposal: adding additional categories to forum, including design, testing, security, and machine learning
#3

I think we got this granular in labels when the forums first started. However, I think we rolled them back to simpler general categories for easier navigation. I’m sure @QuincyLarson can comment on the reason behind this.

So I’d guess that he’d be opposed to making the categories that fine grained. Plus, with the search functionality, I think it’ll be easier to maintain just to have a broad category, then recommend to search from that tag to find events, jobs, etc.


#4

Agree. I am sure it is too much. But then I don’t see why to keep ML out of DS as aggregating category :wink:

Anyway, no plans to take this into a long discussion. I am ok with any decision taken!


#5

Thanks for the healthy discussion, though! :slight_smile:

To really get meta, the true way to go about this would be to gather data/conduct polls on the perceived divide between ML and DS…

…Soooo Google Trends is probably the closest and quickest data-driven way to decide! :wink: (grapes was my “control” search term :laughing:)

But yes, I agree with @evaristoc that the ML and DS divide is otherwise quite arbitrary.


#6

:joy: We should have a grapes category! It is very popular!!


#7

Great thread, for starters if you own a website, then google analytics is the best tool to understand how data works and how can you use it to benefit your site.


#8

Hello
let me start with Data Science, In simple words it is the analysis of huge Data to give an output based on the past data. Let me take an example of an institute where each day thousand of new data are to be kept. and based on the particular feedback of the students ,the institute focused on the field which has more positive feedback and try to develop on the field where people give negative feedback. all this process is done through the help of data science by a data scientist. The major concept for data scientist are statistic,some programming languages etc. So there is a need of Data Scientist in every field no matter whether it is a college or a factory or anything else.

Machine learning on the other hand is a branch where it gives an ability to learn from the experience without being programmed.the perfect example of Machine Learning is developing a game .Machine learning have a good scope in the present .

Statistics is the process of summarizing huge data to give simple output which can be understand easily.It is very much important for Data Science .

Thank You