#data
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Data Scientist: The Sexiest Job of the 21st Century
Back in the 1990s, computer engineer and Wall Street “quant” were the hot occupations in business. Today data scientists are the hires firms are competing to make. As companies wrestle with unprecedented volumes and types of information, demand for these experts has raced well ahead of supply. Indeed, Greylock Partners, the VC firm that backed Facebook and LinkedIn, is so worried about the shortage of data scientists that it has a recruiting team dedicated to channeling them to the businesses in its portfolio. Data scientists are the key to realizing the opportunities presented by big data. They bring structure to it, find compelling patterns in it, and advise executives on the implications for products, processes, and decisions. They find the story buried in the data and communicate it. And they don’t just deliver reports: They get at the questions at the heart of problems and devise creative approaches to them. One data scientist who was studying a fraud problem, for example, realized it was analogous to a type of DNA sequencing problem. Bringing those disparate worlds together, he crafted a solution that dramatically reduced fraud losses. In this article, Harvard Business School’s Davenport and Greylock’s Patil take a deep dive on what organizations need to know about data scientists: where to look for them, how to attract and develop them, and how to spot a great one.
pandas-dev/pandas: Pandas
Pandas is a powerful data structures for data analysis, time series, and statistics.
A first introduction to data science education in secondary schools: Teaching and learning about data exploration with <span style="font-variant:small-caps;">CODAP</span> using survey data
Abstract In this paper, we will describe an introduction to Data Science for secondary school students. We will report on the design and implementation of an introductory unit on “Data and data detectives with CODAP” in which secondary school students used the online tool CODAP to explore real and meaningful survey data on leisure time activities and media use (so‐called JIM‐PB data) in a statistical project setting as a starting point for data science. The JIM‐PB data set served as a valuable data set that offered meaningful and exciting opportunities for data exploration for secondary school students, and CODAP proved to be a valuable tool for the first explorations of this data.
Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years
In the past 10 years, new data science courses and programs have proliferated at the collegiate level. As faculty and administrators enter the race to provide data science training and attract new ...
Introductory data science across disciplines, using Python, case studies, and industry consulting projects
Abstract Data and its applications are increasingly ubiquitous in the rapidly digitizing world and consequently, students across different disciplines face increasing demand to develop skills to answer both academia's and businesses' increasing need to collect, manage, evaluate, apply and extract knowledge from data and critically reflect upon the derived insights. On the basis of recent experiences at the University of Ttingen, Germany, we present a new approach to teach the relevant data science skills as an introductory service course at the university or advanced college level. We describe the outline of a complete course that relies on case studies and project work built around contemporary data sets, including openly available online teaching resources.
Fakebook: Using Data Science to Spot Fake News
As fake news permeates throughout social media outlets (like Facebook), it's more important than ever to identify what is fact vs. fiction. This is especially true in the age of COVID where the spread of misinformation can impact the spread of the virus. Data scientists are emerging on the front lines of this fight, using programs to help them identify fake news sources. Led by Dr. Teon Brooks, Josh Modeste, and Steven Azeka from the Gotham Data Clinic (fka CiELabs), this workshop will explore the “life cycle” of scientific research and how scientists come to consensus about how we interpret data. We’ll examine what to look for in credible news sources, and the consequences of leaning on not-so-trusty pseudoscientific sources. Then you’ll put that knowledge to work by developing a rubric for identifying fake news. No experience necessary! https://www.eventbrite.com/e/fakebook-using-data-science-to-spot-fake-news-tickets-137833492687
G6 Graph Visualization Framework in JavaScript
G6 is a graph visualization framework with simplicity and convenience. Based on the ability of customization, it provides elegant graph visualization solutions, helping developers build applications for graph visualization, analysis, and editing.
データサイエンティストを目指す人のpython環境構築 2016 - Qiita
pythonの環境構築について "python 環境構築"でググると20万件くらいヒットしますが、割と内容が古いです。 タイトルにはデータサイエンティストと書いてありますが、データサイエンティスト以外にもanacondaはおすすめです。 2.x or 3.x? 3.xは...
