Click here to learn more about author Steve Miller. Read Part 1 of this blog series here. Between R and Python, analytics pros are covered on most data science bases R-Python. In last month’s blog, I discussed simple webscraping using Python in a Jupyter notebbok, the nifty css-generating tool SelectorGadget, and the Python XML and HTML handling package lxml. […]
A Guide to Python and R: When to Use Which for What
by Angela Guess Roger Huang recently wrote in The Next Web, “At Springboard, we pair mentors with learners in data science. We often get questions about whether to use Python or R – and we’ve come to a conclusion thanks to insight from our community of mentors and learners. Data science is the sexiest job […]
Web-Scraping for Data Science – Part 1
Click to learn more about Steve Miller. Scraping data from the web is a task that’s essential to the data scientist’s hacking portfolio. The complexity of work ranges from sophisticated crawling that mandates understanding the structure of dynamic web pages along with command of css and/or xpath, to the more mundane “just grabbing a table […]
Top Tools Used By Data Scientists
by Angela Guess Andrew Rosenblum recently wrote in Business2Community, “You’ve read about many of the kinds of big data projects that you can use to learn more about your data in our What Can a Data Scientist Do for You? article—now, we’re going to take a look at tools that data scientists use to mine […]
The Evolution of Python for Data Science
Click here to learn more about author Steve Miller. I’ve been programming in Python for almost 15 years. When I started around 2001, I was doing most of my non-statistical work in C and Perl, the early days of Fortran, PL/I, and Pascal thankfully by then long gone. Just as with R for statistical computing, I […]