InfraNodus.Com — Text Network Visualization and Discourse Analysis

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Top keywords (global influence):

Top topics (local contexts):

Explore the main topics and terms outlined above or see them in the excerpts from this text below.

See the relevant data in context: click here to show the excerpts from this text that contain these topics below.

Tip: use the form below to save the most relevant keywords for this search query. Or start writing your content and see how it relates to the existing search queries and results.

Tip: here are the keyword queries that people search for but don't actually find in the search results.

Data science - Wikipedia Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and ... https://en.wikipedia.org/wiki/Data_science

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What is Data Science? Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals ... https://datascience.berkeley.edu/about/what-is-data-science/

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Here's why so many data scientists are leaving their jobs Mar 28, 2018 ... My take on why data scientists and machine learning engineers topped the list of developers looking for new jobs. It highlights the less ... https://towardsdatascience.com/why-so-many-data-scientists-are-leaving-their-jobs-a1f0329d7ea4

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Data Science | Coursera Learn Data Science from Johns Hopkins University. Ask the right questions, manipulate data sets, and create visualizations to communicate results. https://www.coursera.org/specializations/jhu-data-science

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Data Science | Codecademy Become a Data Scientist. Data Science is one of the fastest growing fields in tech. Get this dream job by mastering the skills you need to analyze data with SQL ... https://www.codecademy.com/learn/paths/data-science

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Oracle Buys DataScience.com May 16, 2018 ... Oracle announced that it signed an agreement to acquire DataScience.com, adding a leading data science platform to the Oracle Cloud, ... https://www.oracle.com/corporate/acquisitions/datascience/

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What Is Data Science? A Beginner's Guide To Data Science | Edureka Jun 20, 2019 ... Data Science is the future of Artificial Intelligence. Learn what is Data Science, how can it add value to your business and its various lifecycle ... https://www.edureka.co/blog/what-is-data-science/

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What REALLY is Data Science? Told by a Data Scientist - YouTube Jun 22, 2018 ... Want to land jobs at Facebook/Google/Microsoft/Amazon? Learn how to do that here: http://techinterviewpro.com/ ▻ Resume Template and ... https://www.youtube.com/watch?v=xC-c7E5PK0Y

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What Is Data Science, and What Does a Data Scientist Do ... This article describes the "pillars" of data science expertise, the role and responsibilities of a data scientist, differences between related roles, and the data ... https://www.innoarchitech.com/blog/what-is-data-science-does-data-scientist-do

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Data Science Online Courses | Udacity Build expertise in data manipulation, visualization, predictive analytics, machine learning, and data science. With the skills you learn in a Nanodegree program, ... https://www.udacity.com/school-of-data-science

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How to Become a Data Scientist Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist's role combines computer science, ... https://www.mastersindatascience.org/careers/data-scientist/

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Insight Data Science Fellows Program An intensive 7-week post-doctoral training fellowship bridging the gap between academia & data science. Join top companies working on data science. https://www.insightdatascience.com/

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Data Scientist: The Sexiest Job of the 21st Century Meet the people who can coax treasure out of messy, unstructured data. https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century

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Data Science Online Bootcamp | Become a Data Scientist | Thinkful™ Built to land you a job as a data scientist or your money back. Work 1-on-1 with a mentor to learn best practices, stats, and big data in Thinkful's data science ... https://www.thinkful.com/bootcamp/data-science/full-time/

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Data Science Central Welcome to Data Science Central. The Community of and for Data Scientist. https://www.datasciencecentral.com/

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What is Data Science? | Thinkful But what is it? Data science, in its most basic terms, can be defined as obtaining insights and information, really anything of value, out of data. Like any new field, ... https://www.thinkful.com/blog/what-is-data-science/

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Data Science Bootcamp: The Best Online Training Classes & School Looking for a data science bootcamp? Try Springboard! Get 1:1 mentoring & complete 14 projects to help build your portfolio. ✓ Get a job or your money back! https://www.springboard.com/workshops/data-science-career-track/

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New York Data Science Bootcamp | Flatiron School At Flatiron School, we teach today's in-demand tech skills, through our dynamic, immersive courses taught by experienced, passionate industry professionals ... https://flatironschool.com/career-courses/data-science-bootcamp/

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Data scientist Jobs | Glassdoor Search Data scientist jobs. Get the right Data scientist job with company ratings & salaries. 31893 open jobs for Data scientist. https://www.glassdoor.com/Job/data-scientist-jobs-SRCH_KO0,14.htm

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data-science - Hacker Noon October 21. A Pleasant Way to Kick Off Your Data Science Education- This is CS50 ... Data Science Training and Data Science - Machine Learning With Python. https://hackernoon.com/tagged/data-science

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Data Science Stack Exchange Imputation missing values other than using Mean, Median in python · feature- engineering missing-data data-imputation · 2 hours ago IamTheRealFord. 0. 1 ... https://datascience.stackexchange.com/

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Data Science Bootcamp Houston - Become a Data Scientist ... Launch a career in data science with General Assembly's Data Science Immersive program - a world-class career accelerator in Houston. Get started in minutes! https://generalassemb.ly/education/data-science-immersive/houston

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Data Science Online Courses | LinkedIn Learning, formerly Lynda.com Data science is one of todays top careers. Get the training you need to get ahead —or stay on top—in fields such as data analysis, mining, visualization, and big ... https://www.linkedin.com/learning/topics/data-science

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University of Wisconsin Data Science Degree Online Find your future in big data. Earn your data science degree in the online University of Wisconsin Master of Science in Data Science program. https://datasciencedegree.wisconsin.edu/

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Data Science Weekly Data Science Weekly Newsletter: A free weekly newsletter featuring curated news, articles and jobs related to Data Science. https://www.datascienceweekly.org/

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NYU Center for Data Science – Harnessing Data's Potential for the ... Apply for the MS Data Science degree! Deadline: January 22 ... CDS' Yann LeCun wins ACM Turing award, the Nobel of Computer Science. Read more about ... https://cds.nyu.edu/

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Data Science Bootcamp | Online & On-Campus | Galvanize Become a Data Scientist in Galvanize's 13-week Data Science Immersive Bootcamp. Our coding bootcamps are offered in Austin, Denver, Boulder, Seattle, NYC ... https://www.galvanize.com/data-science-bootcamp

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Data science (for skill-building & earning to give) - Career review Jun 13, 2015 ... Cultures of learning and mentorship, often reasonable hours and flexible hours. • High starting salaries; graduates of data science bootcamp ... https://80000hours.org/career-reviews/data-science/

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Data Science | UCLA Continuing Education Gain hands-on experience in data management and visualization, machine learning, statistical models, and more for a career in data science. https://www.uclaextension.edu/digital-technology/data-analytics-management/certificate/data-science

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What is a data scientist? | SAS Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems ... https://www.sas.com/en_us/insights/analytics/what-is-a-data-scientist.html

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GPU-Accelerated Workflows for Data Science | NVIDIA Unblock machine learning, analytics, and data discovery for faster, better results with GPU-accelerated data science. https://www.nvidia.com/en-us/deep-learning-ai/solutions/data-science/

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Data Science - Facebook Research Data scientists at Facebook conduct large-scale, global, quantitative research to gain deeper insights into how people interact with each other and the world ... https://research.fb.com/category/data-science/

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Human Data Science - IQVIA To solve the biggest challenges in human health, and realize the potential of big data in healthcare, we need more than data science. We need human data ... https://www.iqvia.com/insights/human-data-science

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R for Data Science This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and ... https://r4ds.had.co.nz/

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What is data science? – O'Reilly Jun 2, 2010 ... Why do we suddenly care about statistics and about data? In this post, I examine the many sides of data science — the technologies, the ... https://www.oreilly.com/radar/what-is-data-science/

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The Data Science Venn Diagram — Drew Conway Sep 30, 2010 ... One of the best sessions I attended focused on issues related to teaching data science, which inevitably led to a discussion on the skills ... http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

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Data Science Definition Aug 17, 2017 ... The definition of Data Science defined and explained in simple language. https://techterms.com/definition/data_science

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The Data Science Bowl: Home The 2019 Data Science Bowl ® is now live! It's time to take on your biggest challenge yet: illuminating pathways to childhood learning. Analyzing data* from PBS ... https://datasciencebowl.com/

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Project Notes Text Settings

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merged nodes:
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Word Count	Unique Lemmas	Characters	Lemmas Density
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Network Structure Insights

mind-viral immunity:

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stucture:

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The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.

In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.

The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.

The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.

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Narrative Influence Propagation:

The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.

The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).

The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.

propagation dynamics: | alpha exponent: (based on Detrended Fluctuation Analysis of influence)

We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).

Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).

For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.

Degree Distribution:

(based on kolmogorov-smirnov test)

Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.

This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.

If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.

Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.

Main Topical Groups:

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The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).

We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.

Most Influential Elements:

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We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.

Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.

The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.

Network Structure:

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Reset Graph Export: Show Options

Action Advice:

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Structural Gap

(ask a research question that would link these two topics):

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A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.

This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.

Latent Topical Brokers

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These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.

These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.

Emerging Keywords

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Evolution of Topics

(frequency / time)

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The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative frequency of occurrence.

Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).

Main Topics

(according to Latent Dirichlet Allocation):

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LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.

We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.

Most Influential Words

(main topics and words according to LDA):

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We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.

Sentiment Analysis

positive: | negative: | neutral:
reset filter

We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.

The approach is based on AFINN and Emoji Sentiment Ranking

Sentiment analysis works for English language only. Contact us @noduslabs to propose a language and to get updated about the new features.

Network Statistics:

Top Relations / Bigrams

(both directions):

The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default as it allows us to better detect general patterns.

As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).