in Mathematics, Python, R Programming, Science, Technology

Python & Data Science

A fair warning to the readers: this blog post reeks of geek so proceed at your own risk.

With the explosion of the ways human beings are creating, collecting data these days thanks to ease of storage (storage media has been becoming increasingly affordable (in other words, cheaper and cheaper). Additionally it has become increasingly easier to generate and collect data from so many devices (smart phones, satellite systems, cameras, social media etc. et.c). So with this incredible amount of data (literally in the tera-bytes) there is a need to utilize to make sense of the data and to utilize it in useful ways (to make better predictions, make more money, avoid more crimes, design better medicine, create better products, sell more services etc. etc.). Companies (as well as universities) have realized how important it is to do science with data. As a result, the discipline that has recently become very popular is Data Science. Just do a Google search for Data Science and you’ll be amazed at how much information there is out there.

Data Science usually requires skills such as Mathematics, Statistics, SQL, Data Analysis, Business Domain Knowledge and Programming in R or Python. Python and R are the two most popular programming languages with the Data Science discipline these days. So if you’ve a solid Math background and love to do programming, this might be something you might want to look into. It seems like a really interesting field with a lot of opportunities for really meaningful work.

A data scientist typically works in one dedicated domain area: it could be anywhere or anything. For instance, you could be working in marketing, medicine, product engineering, urban design, smart cities, transportation, astrophysics or whatever else you’re into or find opportunities in. There are sure to be a lot of opportunities in pretty much any discipline. Exciting careers for bright students. Study your Mathematics well!

Please follow and like us: