- 10 Must-read Machine Learning Articles (March 2020) - Apr 9, 2020.
This list will feature some of the recent work and discoveries happening in machine learning, as well as guides and resources for both beginner and intermediate data scientists.
- Learning from 3 big Data Science career mistakes - Feb 25, 2020.
Thinking of data science as merely a technical profession, like programming, may take you away from your goals. We explain big mistakes to avoid, including not understanding the 2 cultures of statistics, and not understanding the shift to industrial focus.
- AI and Machine Learning In Our Every Day Life - Feb 7, 2020.
The curiosity and buzz around the most talked-about technology -- Artificial Intelligence -- have experts and technophiles busy decoding its exciting future applications. Of course, the use of AI and machine learning is already pervasive in our daily lives, as we review many of these popular features in this article.
- Top 10 Data Science Leaders You Should Follow - Jul 12, 2019.
If you’re in the data science field, I strongly encourage you to follow these giants— which I’ll list down in the section below — and be a part of our data science community to learn from the best and share your experience and knowledge.
- Beyond news contents: the role of social context for fake news detection - Mar 7, 2019.
Today we’re looking at a more general fake news problem: detecting fake news that is being spread on a social network. This is a summary of a recent paper which demonstrates why we should also look at the social context: the publishers and the users spreading the information!
- Natural Language Processing for Social Media - Feb 12, 2019.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Natural Language Processing and how it is used in social media analytics.
- How to Build a Data Science Portfolio - Jul 25, 2018.
This post will include links to where various data science professionals (data science managers, data scientists, social media icons, or some combination thereof) and others talk about what to have in a portfolio and how to get noticed.
- How StockTwits Applies Social and Sentiment Data Science - Mar 9, 2018.
StockTwits is a social network for investors and traders, giving them a platform to share assertions and perceptions, analyses and predictions.
- 70 Amazing Free Data Sources You Should Know - Dec 20, 2017.
70 free data sources for 2017 on government, crime, health, financial and economic data, marketing and social media, journalism and media, real estate, company directory and review, and more to start working on your data projects.
- KDnuggets now a secure site, change in FB counts, and our most liked content - Oct 31, 2017.
KDnuggets has recently converted to a secure https access which reset our facebook "like" counts. However, we saved the data - see which pages were most liked.
- Social Media and Machine Learning Transform Self-service Data Prep - Oct 16, 2017.
Social media and machine learning concepts are transforming self-service data prep into a collaborative data marketplace.
- A Quick Guide to Fake News Detection on Social Media - Oct 10, 2017.
Fake news is an important issue on social media. This article provides an overview of fake news characterization and detection in Data Science and Machine Learning research.
- Data Science Study of “Unite The Right” and social media - Sep 14, 2017.
In response to the violence in Charlottesville, the Data Science Institute at the U. of Virginia is undertaking a unique project to help understand the ways people use social media to physically, and politically engage in the world around them.
- The Top 5 KPIs to Consider When Measuring Your Campaign - Feb 28, 2017.
When it comes to measuring marketing campaign performance or analysing customers in any business, below top 5 Key Performance Indicators (KPIs) needs to be used to strategically drive the business.
- Data Hoarding and Alternative Data In Finance – How to Overcome the Challenges - Jan 13, 2017.
Big data craze inspires firms to save every possible bit of data, with the misconception that the more data you have, the better. Firms must keep data (for compliance purposes) or often aren’t sure what information they need to keep. This post looks at alternative data sources.
- Social Media for Marketing and Healthcare: Focus on Adverse Side Effects - Jan 9, 2017.
Social media like twitter, facebook are very important sources of big data on the internet and using text mining, valuable insights about a product or service can be found to help marketing teams. Lets see, how healthcare companies are using big data and text mining to improve their marketing strategies.
- Misinformation Key Terms, Explained - Aug 20, 2016.
Misinformation has emerged as a key issue for social media platforms. This post will introduce the concept of misinformation and the 8 Key Terms, which provides insights into mining misinformation in social media.
- Exploring Social Media Diversity with Natural Language Processing - Aug 10, 2016.
This post uses natural language processing on Twitter data to determine the diversity of Twitter accounts the author is following. An innovative take on social media analytics.
Pages: 1 2
- Mining Twitter Data with Python Part 7: Geolocation and Interactive Maps - Jul 6, 2016.
The final part of this 7 part series explores using geolocation and interactive maps with Twitter data.
- Mining Twitter Data with Python Part 6: Sentiment Analysis Basics - Jul 5, 2016.
Part 6 of this series builds on the previous installments by exploring the basics of sentiment analysis on Twitter data.
- Mining Twitter Data with Python Part 5: Data Visualisation Basics - Jun 29, 2016.
Part 5 of this series takes on data visualization, as we look to make sense of our data and highlight interesting insights.
- Mining Twitter Data with Python Part 4: Rugby and Term Co-occurrences - Jun 27, 2016.
Part 4 of this series employs some of the lessons learned thus far to analyze tweets related to rugby matches and term co-occurrences.
- Mining Twitter Data with Python Part 3: Term Frequencies - Jun 22, 2016.
Part 3 of this 7 part series focusing on mining Twitter data discusses the analysis of term frequencies for meaningful term extraction.
- Mining Twitter Data with Python Part 2: Text Pre-processing - Jun 20, 2016.
Part 2 of this 7 part series on mining Twitter data for a variety of use cases focuses on the pre-processing of tweet text.
- Mining Twitter Data with Python Part 1: Collecting Data - Jun 15, 2016.
Part 1 of a 7 part series focusing on mining Twitter data for a variety of use cases. This first post lays the groundwork, and focuses on data collection.
- Online Privacy – Why the Odds are Against You? - Nov 4, 2015.
Infographic on Data Brokers explains how personal information is collected and sold, leaving people with few options to opt-out of it.
Pages: 1 2
- Northwestern MOOC Specialization: “Social Marketing – How to Profit in a Digital World;” Lexalytics CMO Seth Redmore Featured Faculty Member - Oct 12, 2015.
Six-part series offered through Coursera will teach entrepreneurs, executives, and marketing professionals how to manage, measure, and monetize social media marketing programs.
- 8 Things to Check when you analyze Twitter data - Dec 16, 2014.
A review of biases and issues on large scale studies of human behavior in social media discussed by a recent paper published on Science.
- SBP15 Grand Data Challenge - Dec 5, 2014.
Use social media analytics on public data to help analyze and explore social inequality and aid the disadvantaged in SBP15 Grand Data Challenge. Submissions due Jan 20.
- Upcoming Webcasts on Analytics, Big Data, Data Science – Nov 25 and beyond - Nov 24, 2014.
Social Media to Actionable Insights, NoSQL and Big Data in the Government, NoSQL Database Architecture, All-vs-All: Correlation Using Spark/Hadoop, The 2015 Analytics Predictions Webinar, and more.
- Interview: Saikat Mukherjee, ShareThis on Why Marketers can no longer Ignore Social TV? - Aug 20, 2014.
We discuss the role of Analytics at ShareThis, the emergence of Social TV, better user behavior insights through Social TV, major challenges with Social TV analytics, interesting insights, future trends, recommendation and more.
- Top KDnuggets tweets, Aug 13-14: Boyfriend as a statistically “significant” other - Aug 15, 2014.
xkcd: Boyfriend as a statistically "significant" other; Interesting Social Media Datasets; Sibyl: a System for Large Scale Machine Learning at Google; We don't need such hype: "Big Data scientists get 100 recruiter emails a day".
- ASE International Conference on Big Data Science 2014: Day 4 Highlights - Aug 8, 2014.
Highlights from the presentations by Data Science leaders from UC Berkeley, Clark Atlanta Univ, Florida Institute of Technology, Rober Bosh LLC and HP on day 4 of ASE Conference on Big Data Science 2014, Stanford.
- ASE International Conference on Big Data Science 2014: Day 3 Highlights - Aug 5, 2014.
Highlights from the presentations by Data Science leaders from UC Davis, UT Dallas, Northrop Grumman Corp and NIST on day 3 of ASE Conference on Big Data Science 2014 held in Stanford University.
- Interview: Vita Markman, LinkedIn on Practical Solutions for Sentiment Mining Challenges - Aug 4, 2014.
We discuss sentiment data models, significance of linguistic features, handling the noise in social conversations, industry challenges, important use cases and the appropriateness of over-simplified binary classification.
- New Book: Social Media Mining – free PDF download - Apr 22, 2014.
Social Media Mining integrates social media, social network analysis, and data mining to enable students, practitioners, researchers, and managers to understand the basics and potentials of this field.
- SAS: Research Statistician Developer - Apr 2, 2014.
Create innovative software to apply cutting-edge analytical techniques working with textual data from customer comments, social media, emails, and other sources.
- Viewpoint: Social Media Analysis: What is missing - Feb 15, 2014.
Social Media Analysis is a powerful tool if we discover customer sentiment from millions of online sources and not just go behind the numbers. Businesses are using the power of social media to gain a better understanding of their markets.