January 17, 2019 — Kathy Bautista, Senior Sales Initiatives Manager, Academic Sales
As many teachers make the transition back into classes after the holidays, quite a few have plans to update lessons to include segments that introduce data science concepts. Why, you ask?
According to a LinkedIn report published last week, the most promising job in the US in 2019 is data scientist. And if you search for the top “hard skills” needed for 2019, data science is often in the top 10.
Data science, applied computation, predictive analytics… no matter what you call it, in a nutshell it’s gathering insight from data through analysis and knowing what questions to ask to get the right answers. As technology continues to advance, the career landscape also continues to evolve with a greater emphasis on data—so data science has quickly become an essential skill that’s popping up in all sorts of careers, including engineering, business, astronomy, athletics, marketing, economics, farming, meteorology, urban planning, sociology and nursing.
January 10, 2019 — Brian Wood, Lead Technical Writer, Document and Media Systems
So far in this series, I’ve covered the process of extracting, cleaning and structuring data from a website. So what does one do with a structured dataset? Continuing with the Election Atlas data from the previous post, this final entry will talk about how to store your scraped data permanently and deploy results to the web for universal access and sharing.
January 3, 2019 — Wolfram Blog Team
Mark Greenberg is a retired educator and contributor to the Tech-Based Teaching blog, which explores the intersections between computational thinking, edtech and learning. He recounts his experience adapting old game code using the Wolfram Language and deployment through the Wolfram Cloud.
Chicken Scratch is an academic trivia game that I originally coded about 20 years ago. At the time I was the Academic Decathlon coach of a large urban high school, and I needed a fun way for my students to remember thousands of factoids for the Academic Decathlon competitions. The game turned out to be beneficial to our team, and so popular that other teams asked to buy it from us. I refreshed the questions each year and continued holding Chicken Scratch tournaments at the next two schools I worked in.
December 13, 2018 — Jesika Brooks, Blog Editor - EduTech, Public Relations
A version of this post was originally published on the Tech-Based Teaching blog as “Computational Lesson-Planning: Easy Ways to Introduce Computational Thinking into Your Lessons.” Tech-Based Teaching explores the intersections between computational thinking, edtech and learning.
Sometimes a syllabus is set in stone. You’ve got to cover X, Y and Z, and no amount of reworking or shifting assignments around can change that. Other factors can play a role too: limited time, limited resources or even a bit of nervousness at trying something new.
But what if you’d like to introduce some new ideas into your lessons—ideas like digital citizenship or computational thinking? Introducing computational thinking to fields that are not traditionally part of STEM can sometimes be a challenge, so feel free to share this journey with your children’s teachers, friends and colleagues.
December 6, 2018 — Tuseeta Banerjee, Research Scientist, Machine Learning
Julian Francis, a longtime user of the Wolfram Language, contacted us with a potential submission for the Wolfram Neural Net Repository. The Wolfram Neural Net Repository consists of models that researchers at Wolfram have either trained in house or converted from the original code source, curated, thoroughly tested and finally have rendered the output in a very rich computable knowledge format. Julian was our very first user to go through the process of converting and testing the nets.
We thought it would be interesting to interview him on the entire process of converting the models for the repository so that he could share his experiences and future plans to inspire others.
November 20, 2018 — Brian Wood, Lead Technical Writer, Document and Media Systems
Thanks to the Wolfram Language, English teacher Peter Nilsson is empowering his students with computational methods in literature, history, geography and a range of other non-STEM fields. Working with a group of other teachers at Deerfield Academy, he developed Distant Reading: an innovative course for introducing high-level digital humanities concepts to high-school students. Throughout the course, students learn in-demand coding skills and data science techniques while also finding creative ways to apply computational thinking to real-world topics that interest them.
In this video, Nilsson describes how the built-in knowledge, broad subject coverage and intuitive coding workflow of the Wolfram Language were crucial to the success of his course:
November 13, 2018 — Jesika Brooks, Blog Editor - EduTech, Public Relations
This post was initially published on Tech-Based Teaching, a blog about computational thinking, educational technology and the spaces in between. Rather than prioritizing a single discipline, Tech-Based Teaching aims to show how edtech can cultivate learning for all students. Past posts have explored the value of writing in math class, the whys and hows of distant reading and the role of tech in libraries.
It’s November, also known as National Novel Writing Month (NaNoWriMo). This annual celebration of all things writerly is the perfect excuse for would-be authors to sit down and start writing. For educators and librarians, NaNoWriMo is a great time to weave creative writing into curricula, be it through short fiction activities, campus groups or library meet-ups.
During NaNoWriMo, authors are typically categorized into two distinct types: pantsers, who “write by the seat of their pants,” and plotters, who are meticulous in their planning. While plotters are likely writing from preplanned outlines, pantsers may need some inspiration.
That’s where Wolfram|Alpha comes in handy.
October 25, 2018 — Christopher Carlson, Senior User Interface Developer, User Interfaces
Images and machine learning were the dominant themes of submissions to the One-Liner Competition held at this year’s Wolfram Technology Conference. The competition challenges attendees to show us the most astounding things they can accomplish with 128 or fewer characters—less than one tweet—of Wolfram Language code. And astound us they did. Read on to see how.
September 6, 2018 — Brian Wood, Lead Technical Writer, Document and Media Systems
In my previous post, I demonstrated the first step of a multiparadigm data science workflow: extracting data. Now it’s time to take a closer look at how the Wolfram Language can help make sense of that data by cleaning it, sorting it and structuring it for your workflow. I’ll discuss key Wolfram Language functions for making imported data easier to browse, query and compute with, as well as share some strategies for automating the process of importing and structuring data. Throughout this post, I’ll refer to the US Election Atlas website, which contains tables of US presidential election results for given years:
August 23, 2018 — Brian Wood, Lead Technical Writer, Document and Media Systems
As the technology manager for Assured Flow Solutions, Andrew Yule has long relied on the Wolfram Language as his go-to tool for petroleum production analytics, from quick computations to large-scale modeling and analysis. “I haven’t come across something yet that the Wolfram Language hasn’t been able to help me do,” he says. So when Yule set out to consolidate all of his team’s algorithms and data into one system, the Wolfram Language seemed like the obvious choice.