Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training Follow us on: https://www.facebook.com/jigsawacademy https://twitter.com/jigsawacademy http://jigsawacademy.com/
Views: 14675 Jigsaw Academy
Social media data is hot stuff—but it sure can be tricky to understand. In this session, Michelle from Tableau's social media team will share how they analyze social media data from multiple sources. We'll compare methods for collecting data, and discuss tips for ensuring that it answers new questions as they arise. Whether you're new to social media analysis or have already started diving into your data, this session will provide key tips, tricks, and examples to help you achieve your goals.
Views: 12646 Tableau Software
Do you ever wonder if your social media efforts are amounting to anything? Learn how to measure your social media efforts. Subscribe here to learn more of my secret social media tips: https://www.youtube.com/subscription_center?add_user=neilvkpatel Find me on Facebook: https://www.facebook.com/neilkpatel/ Read more on my blog: https://neilpatel.com/blog You're on Facebook, and you're on Twitter. You're on Instagram. Heck, you're on all the main social sites out there. But you want to be popular, and you're not getting enough traffic or popularity or fans. What should you be doing? Hey, everyone, I'm Neil Patel, and today I'm going to be sharing with you the main social media metrics you should be tracking. See, before you could be doing well on the social web, you need to figure out if you're doing the right things or not. If you're not tracking the right metrics, you won't know if you're doing the right things or if something's wrong or if things are going well and you just need to give it more time. So let's go over some of the metrics you should be tracking. The first metric you should be tracking is the number of new fans you're getting on a consistent basis. Not just total count, but how many new ones are you getting on a weekly basis? So you can do daily, you can do weekly, you can do monthly, but the goal is to have more and more added every single month. And here's what I mean by this. Let's say you start off with zero fans and the next month you add in 100. Well, the next month after if you add in another 100, that's not that great, because you already had 100. You want to be growing faster and faster month over month. So if you added 100 the first month, then 150, then 250, then 500, that's great because your growth rate is increasing. But if your growth rate is decreasing, in which you're only adding 100 each month, that means you're not growing at a fast pace, and eventually, you're going to cap out. So, keep track of your followers and fans. And also, this is the second metric, keep track of your competitors' growth. Your competitors could be growing faster or slower than you. You ideally want them to be growing slower. If they're growing faster, that just means they're doing something you're not, and you're going to get your butt handed to you. You want to be beating your competitors. You can use socialblade.com. It's a free tool that shows you how fast you're growing compared to how fast your competitors are growing. The third metric that you want to be doing is you need to be tracking engagement. How many comments are you getting per Facebook post? How many people are liking your stuff? How many people are retweeting? How many people are sharing your content on LinkedIn? If that percentage is going down, that means you're not building a thriving community that'll help your content go viral. You want loyal followers. And the way you do this and the way you cultivate this is you respond to people, engage. If you go to my Facebook page or my Twitter profile, I do try to engage with people. Sure, I can't respond to everyone, but I do. When someone gives me a message on Facebook, I do try to respond to them. That helps me build a much more loyal audience. The last metric I have for you that you need to be tracking is how much content are you pushing out there. Right, if you're not pushing out content, pieces, status updates. It doesn't have to be stuff from your business. It could just be industry stuff or educational base that you're trying their help people out. If you're not putting out a lot of information, I'm talking about multiple times a day; you're not going to be building a thriving community. I try to post multiple times a day on all my social profiles, and I track my competitors, and I make sure I post just as much as them, if not more Track those metrics, and you'll get a good idea of if you're growing. Sure, in the end, you can base it off of traffic and sales, and in your Google Analytics you can see if you're getting more social traffic and more sales, but when you're starting off, you're not going to see much traffic. You're not going to see much sales. So by following the metrics that I just gave you, you'll know if you're going in the right direction, and eventually, you should be doing well as long as all those metrics are increasing and looking good.
Views: 15822 Neil Patel
Basics of Social Network Analysis In this video Dr Nigel Williams explores the basics of Social Network Analysis (SNA): Why and how SNA can be used in Events Management Research. The freeware sound tune 'MFF - Intro - 160bpm' by Kenny Phoenix http://www.last.fm/music/Kenny+Phoenix was downloaded from Flash Kit http://www.flashkit.com/loops/Techno-Dance/Techno/MFF_-_In-Kenny_Ph-10412/index.php The video's content includes: Why Social Network Analysis (SNA)? Enables us to segment data based on user behavior. Understand natural groups that have formed: a. topics b. personal characteristics Understand who are the important people in these groups. Analysing Social Networks: Data Collection Methods: a. Surveys b. Interviews c. Observations Analysis: a. Computational analysis of matrices Relationships: A. is connected to B. SNA Introduction: [from] A. Directed Graph [to] B. e.g. Twitter replies and mentions A. Undirected Graph B. e.g. family relationships What is Social Network Analysis? Research technique that analyses the Social structure that emerges from the combination of relationships among members of a given population (Hampton & Wellman (1999); Paolillo (2001); Wellman (2001)). Social Network Analysis Basics: Node and Edge Node: “actor” or people on which relationships act Edge: relationship connecting nodes; can be directional Social Network Analysis Basics: Cohesive Sub-group Cohesive Sub-group: a. well-connected group, clique, or cluster, e.g. A, B, D, and E Social Network Analysis Basics: Key Metrics Centrality (group or individual measure): a. Number of direct connections that individuals have with others in the group (usually look at incoming connections only). b. Measure at the individual node or group level. Cohesion (group measure): a. Ease with which a network can connect. b. Aggregate measure of shortest path between each node pair at network level reflects average distance. Density (group measure): a. Robustness of the network. b. Number of connections that exist in the group out of 100% possible. Betweenness (individual measure): a. Shortest paths between each node pair that a node is on. b. Measure at the individual node level. Social Network Analysis Basics: Node Roles: Node Roles: Peripheral – below average centrality, e.g. C. Central connector – above average centrality, e.g. D. Broker – above average betweenness, e.g. E. References and Reading Hampton, K. N., and Wellman, B. (1999). Netville Online and Offline Observing and Surveying a Wired Suburb. American Behavioral Scientist, 43(3), pp. 475-492. Smith, M. A. (2014, May). Identifying and shifting social media network patterns with NodeXL. In Collaboration Technologies and Systems (CTS), 2014 International Conference on IEEE, pp. 3-8. Smith, M., Rainie, L., Shneiderman, B., and Himelboim, I. (2014). Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Internet Project.
Views: 38099 Alexandra Ott
You use social networks every day, but how can we understand how they work to affect our decisions, our careers, our health, and our histories? The field of Social Network Analysis is the dynamic and highly adaptable group of techniques that let us quantify and understand the complex structures and flows of relationships, thoughts, and things between people around the world. Look at your own social networks at these links: Check your own personal Facebook social network with Touchgraph: http://www.touchgraph.com/facebook Check your own personal LinkedIn social network with Socilab: http://socilab.com/ Check your own personal Twitter social network with Mentionmapp: http://mentionmapp.com/ Social Network Analysis can enrich the research of faculty and the studies of students—look for workshops run by the Duke Network Analysis Center and classes featuring graph theory, network theory, and social networks. Networks are everywhere—what will you discover with them?
Views: 27347 Mod•U: Powerful Concepts in Social Science
Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 19530 Bharatendra Rai
Bernie Hogan completed his BA(hons) at the Memorial University of Newfoundland in Canada, where he received the University Medal in Sociology. Since then he has been working on Internet use and social networks at the University of Toronto under social network analysis pioneer Barry Wellman. Bernie received his Masters of Arts at Toronto in 2003, and defended his PhD Dissertation in the Fall of 2008. His dissertation examines how the use of ICTs alters the way people maintain their relationships in everyday life. In 2005 he was an intern at Microsoft’s Community Technologies Lab, working with Danyel Fisher on new models for email management. RESEARCH Bernie Hogan’s research focuses on the creation, maintenance and analysis of personal social networks, with a particular focus on the relation between online and offline networks. Hogan’s work has demonstrated the utility of visualization for network members, how the addition of new social media can complicate communication strategies, and how the uneven distribution of media globally can affect the ability of people to participate online. Currently, Hogan is working on techniques to simplify the deployment of personal network studies for newcomers as well as social-theoretical work on the relationship between naming conventions and identities. #datascienceclasses
Views: 1239 The Alan Turing Institute
This video demonstrates a prototype system for visual-interactive analysis of large georeferenced microblog datasets, describing the design of the system and detailing its application to the VAST 2011 Challenge dataset. The dataset models an epidemic outbreak in a fictitious metropolitan area. The video shows how the system can detect the epidemic and analyze its development over time. The system was implemented by Juri Buchmueller, Fabian Maass, Stephan Sellien, Florian Stoffel, and Matthias Zieker at the University of Konstanz (they also produced this video). Further information on the system and the VAST challenge dataset can be found in E. Bertini et al., "Visual Analytics of Terrorist Activities Related to Epidemics," Proc. IEEE Conf. Visual Analytics Science and Technology (VAST 11), IEEE CS, pp. 329ñ330, 2011. From Computer's May 2013: http://www.computer.org/csdl/mags/co/2013/05/index.html. Visit Computer: http://www.computer.org/computer.
Views: 1472 ieeeComputerSociety
In this step by step project from the SAP HANA Academy, we illustrate a simple example of building an application using native development tools for SAP HANA. This video walks through using Text Analysis on the social media data used in this project. To access the code snippets used in the video series please visit https://github.com/saphanaacademy/Live2
Views: 2906 SAP HANA Academy
Add in-demand data science skills to your resume: https://www.thelead.io/data-science-360/ If you run a business or organizations, it's important to know what your customers are saying about you - whether if its a form of a blog, social media post, review or comment. This is where text mining and text analysis comes into play. Data scientists build text mining algorithms to mine texts from customers and map out a word cloud to understand their customers. Dr. Lau shows us how to do text analysis in this Data Crunch episode. Text analysis data files for this episode: https://goo.gl/Y5YwRH Google Alerts: https://www.google.com.my/alerts Brandwatch: https://www.brandwatch.com/ =============== Where to follow and learn more from LEAD: Website: https://www.thelead.io Facebook: https://www.facebook.com/thelead.io/ Instagram: https://www.instagram.com/theleadio/ ================ LEAD is an institute in Malaysia, where we provide courses in Data Science, Full Stack Web Development, Digital Marketing & Business, for individuals and corporates — so they can find better careers or to build successful businesses. We teach career-ready skills that our students can use right away in their jobs or find a job. Rather than taking years to learn and master a subject, we have designed our courses to shortcut our students to be competent in the workspace. So we gathered a group of experts in their fields, to teach and mentor our students. Collectively, our 15+ years in technology mentoring means you’ll get real insights & strategies from the best developers, digital marketers, and data scientists.
Views: 322 LEAD
This demo uses Twitter and Facebook data collected using QVSource connectors to provide a way for a company to compare and analyze Twitter and Facebook activity and sentiment for their company and five of their competitors. This demo takes a look at how often people are tweeting, posting and commenting about a company and their overall sentiment toward the company. Followers, Likes, Share of Voice and Klout are also explored in this demo.
Views: 5176 QlikView Demos
See the full course: https://systemsacademy.io/courses/complexity-theory/ Twitter: http://bit.ly/2HobMld A brief overview to the new area of social network analysis that applies network theory to the analysis of social relations. Transcription: Social network analysis is the application of network theory to the modeling and analysis of social systems. it combine both tools for analyzing social relations and theory for explaining the structures that emerge from the social interactions. Of course the idea of studying societies as networks is not a new one but with the rise in computation and the emergence of a mass of new data sources, social network analysis is beginning to be applied to all type and scales of social systems from, international politics to local communities and everything in between. Traditionally when studying societies we think of them as composed of various types of individuals and organizations, we then proceed to analysis the properties to these social entities such as their age, occupation or population, and them ascribe quantitative value to them. This allows social science to use the formal mathematical language of statistical analyst to compare the values of these properties and create categories such as low in come house holds or generation x, we then search for quasi cause and effect relations that govern these values. This component-based analysis is a powerful method for describing social systems. Unfortunately though is fails to capture the most important feature of social reality that is the relations between individuals, statistical analysis present a picture of individuals and groups isolates from the nexus of social relations that given them context. Thus we can only get so far by studying the individual because when individuals interact and organize, the results can be greater than the simple sum of its parts, it is the relations between individuals that create the emergent property of social institutions and thus to understand these institutions we need to understand the networks of social relations that constitute them. Ever since the emergence of human beans we have been building social networks, we live our lives embed in networks of relations, the shape of these structures and where we lie in them all effect our identity and perception of the world. A social network is a system made up of a set of social actors such as individuals or organizations and a set of ties between these actors that might be relations of friendship, work colleagues or family. Social network science then analyze empirical data and develops theories to explaining the patterns observed in these networks In so doing we can begin to ask questions about the degree of connectivity within a network, its over all structure, how fast something will diffuse and propagate through it or the Influence of a given node within the network. lets take some examples of this Social network analysis has been used to study the structure of influence within corporations, where traditionally we see organization of this kind as hierarchies, by modeling the actual flow of information and communication as a network we get a very different picture, where seemingly irrelevant employees within the hierarchy can in fact have significant influence within the network. Researcher also study innovation as a process of diffusion of new ideas across networks, where the oval structure to the network, its degree of connectivity, centralization or decentralization are a defining feature in the way that innovation spreads or fails to spread. Network dynamics, that is how networks evolve overtime is another important area of research, for example within Law enforcement agencies social network analysis is used to study the change in structure of terrorists groups to identify changing relations through which they are created, strengthened and dissolved? Social network analysis has also been used to study patterns of segregation and clustering within international politics and culture, by mapping out the beliefs and values of countries and cultures as networks we can identify where opinions and beliefs overlap or conflict. Social network analysis is a powerful new method we now have that allows us to convert often large and dense data sets into engaging visualization, that can quickly and effectively communicate the underlining dynamics within the system. By combine new discoveries in the mathematics of network theory, with new data sources and our sociological understanding, social network analysis is offering huge potential for a deeper, richer and more accurate understanding, of the complex social systems that make up our world. Twitter: http://bit.ly/2TTjlDH Facebook: http://bit.ly/2TXgrOo LinkedIn: http://bit.ly/2TPqogN
Views: 41175 Systems Academy
How has your social media been faring over the past quarter? The past month? One way to find out is to run a social media report that compares some of your key stats over a given period. In this video we'll look at how to create social media reports you can export. Check out Buffer for super simple social media scheduling and analytics: http://bufferapp.com Subscribe here so you don’t miss our future videos (it's free!) - http://bit.ly/BufferYT Ask us any questions on social media or here in the comments. We’d love to answer them! Follow us in these places: Twitter - https://twitter.com/buffer Facebook - https://www.facebook.com/bufferapp Google+ - https://plus.google.com/+Bufferapp/ Instagram - https://instagram.com/buffer
Views: 12704 Buffer
Download the Social Media Dashboard from https://indzara.com/2016/09/social-media-dashboard-metrics-excel-template/ This Social Media Dashboard (Excel Template) is designed to consolidate metrics across multiple Social networks such as Facebook, Twitter and YouTube and present in a single-page dashboard. If you are trying to create a monthly view of business impact of content posted across all the social networks by your business, then download this free template to create an instant monthly dashboard. This template does not automate the data collection process. Once we are able to compile the monthly aggregated data from the different social networks, we can use this template to instantly build the dashboard as shown below. FEATURES OF SOCIAL MEDIA DASHBOARD The Social Media Dashboard template has the following features Add up to 8 social media channels. Choose only ones relevant to your business. Designed for Facebook, Twitter, Google+, YouTube, Blog, LinkedIn, Pinterest and Instagram 7 social media metrics (Audience, Activity, Applause, Amplification, Conversation, Conversion and Revenue) Choose to display metrics as either absolute volumes or rate per activity Add each month’s data and store all data in one file Automatically calculates Month over Month (MOM) change % Control alerts by setting thresholds on each metric Alerts highlight only metrics which exceed thresholds (with red or green arrows) Dashboard displays large numbers with M (millions) or K (thousands) for better readability INSTRUCTIONS (HOW TO USE SOCIAL MEDIA DASHBOARD) The template is very easy to use. Step 1: Choose the social networks to include in dashboard Check the boxes next to social networks you would like to display in the dashboard. Step 2: Set Thresholds for change in metrics Before we talk about these thresholds, let’s discuss the metrics the dashboard presents. Audience: Number of Fans/Followers/Page Likes (depending on the network) Activity Number of Posts/Updates Applause: Number of Likes; Applause Rate: Average number of Likes per Activity Amplification: Number of Shares; Amplification Rate: Average number of Shares per Activity Conversations: Number of Comments; Conversation Rate: Average number of Comments per Activity Conversions: Number of Conversions; Conversion Rate: Average number of Conversions per Activity Revenue: Total Revenue Generated; Revenue per Activity: Revenue generated per Activity Why thresholds? When we display the Month over Month change for each metric, the dashboard can become very distracting with a lot of numbers. The purpose of the dashboard is to allow us to focus only on metrics that require our attention and action. So we use thresholds to only flag metrics that exceed our thresholds. Once a certain metric’s month over month (MOM) change exceeds the threshold in positive direction, Green arrows appear on dashboard. If the change exceeds the threshold in negative direction, Red arrows appear on the dashboard. The metrics that are within the thresholds are grayed out so that they do not distract us from the action-required metrics. Step 3: Enter monthly data for social media channels in DATA sheet. Data for all the 8 social networks are stored in the same table. You can add a new row for each month. (How to enter data in Excel Tables?) Step 4: View DASHBOARD sheet Now that we have entered our data, we can view the Dashboard in the DASHBOARD sheet. Change Month drop down to view stats for that month Change Metric type to show absolute volumes or rate per activity For example, if there are 200 conversations from 10 Facebook posts in Mar 2016, 200 is the absolute volume of Conversations and 200/10 = 20 is the Conversation Rate. Print or share DASHBOARD sheet as PDF, if needed The Dashboard sheet is set up as print-friendly. Using the in-built Excel features, we can either print the sheet or export to PDF and share with our colleagues or clients. If you find the template useful, please share with your friends. If you have any suggestions to improve the template, I would love to hear from you. Please post your thoughts in the comments. Simple and Effective Excel Templates: http://indzara.com/ Free Excel Templates: http://indzara.com/free-excel-templates/ Premium Excel Templates: http://indzara.com/shop/ Small Business Management Templates: http://indzara.com/small-business-excel-templates/ Project Management Templates: http://indzara.com/project-management-excel-templates/ HR Templates: http://indzara.com/hr-excel-templates/ Personal Finance Templates: https://indzara.com/personal-finance-free-excel-templates/ Free Excel Course: http://indzara.com/useful-excel-for-beginners/ Social: Subscribe to YouTube: http://www.youtube.com/user/theindzara?sub_confirmation=1 Facebook: https://www.facebook.com/theindzara YouTube: https://www.youtube.com/user/theindzara LinkedIn: https://www.linkedin.com/company/indzara Twitter: https://www.youtube.com/user/theindzara
Views: 13893 Indzara
With over 2.72 billion users, social media platforms such as Twitter and Facebook generate vast quantities of data every day. Analysis of this data can help us try to understand how people think and act. Social media analysis played a key role in guiding Obama’s 2012 election campaign and some credit agencies have used social media data to determine whether to grant loans to individuals. But concerns have been raised about the implications for privacy, security and public trust. Who can access our data and under what circumstances? What can social media data practically be used for? What are social media platforms doing to ensure users are fully aware of how their data could be used and allow them to opt out of data sharing? Should we be more careful about what information we share on social media? Our expert speakers include: - Professor Helen Margetts (Director of the Oxford Internet Institute [University of Oxford] and Faculty Fellow at the Alan Turing Institute) - Jefferson Bailey (Director, Web Archiving Programs at Internet Archive - Professor David Vincent (Privacy historian at the Open University, and author of 'Privacy. A Short History') The event will be chaired by Timandra Harkness (science communicator and author of' 'Big Data: Does Size Matter?') The Data Debates is a collaboration between the AHRC, the Alan Turing Institute, the British Library, and the ESRC and aims to stimulate discussion on issues surrounding big data, its potential uses, and its implications for society. #TheDataDebates
Views: 533 The British Library
In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 265413 Siraj Raval
Learn how In-GPU-memory databases can change the way of analyzing real world Big Data sets such as Social Media entries, webpage hits or business data. Analytical queries in databases often involve calculations of extremely large areas of aggregated values as input for further processing like conditional calculating (if-then-else) or top-k evaluation and therefore often run into memory problems. The Social Analytics Showcase demonstrates the power of Jedox GPU processing for Big Data analyses: The app stores millions of localized tweets on the multidimensional Jedox Server, breaks them down to its relevant content, and analyzes its geographic information in just a fraction of a second.
Views: 399 Jedox
My website: http://smediahub.com/ Online social media represent a fundamental shift of how information is being produced, transferred and consumed. The present tutorial investigates techniques for social media modeling, analytics and optimization. First we present methods for collecting large scale social media data and then discuss techniques for coping with and correcting for the effects arising from missing and incomplete data. We proceed by discussing methods for extracting and tracking information as it spreads among the users. Then we examine methods for extracting temporal patterns by which information popularity grows and fades over time. We show how to quantify and maximize the influence of media outlets on the popularity and attention given to particular piece of content, and how to build predictive models of information diffusion and adoption. As the information often spreads through implicit social and information networks we present methods for inferring networks of influence and diffusion. Last, we discuss methods for tracking the flow of sentiment through networks and emergence of polarization. Lecture's slides: http://videolectures.net/site/normal_dl/tag=612868/single_leskovec_social_01.pdf Tutorial website: http://snap.stanford.edu/proj/socmedia-kdd/ Jure Leskovec: http://cs.stanford.edu/people/jure/
Views: 10294 Thomas Joslyn
Anatoliy Gruzd (Dalhousie University) discusses how to do automated analysis of information and social networks using social media data. As social creatures, our online lives just like our offline lives are intertwined with others within a wide variety of social networks. Each retweet on Twitter, comment on a blog or link to a Youtube video explicitly or implicitly connects one online participant to another and contributes to the formation of various information and social networks. Once discovered, these networks can provide researchers with an effective mechanism for identifying and studying collaborative processes within any online community. However, collecting information about online networks using traditional methods such as surveys can be very time consuming and expensive. The presentation explores automated ways to discover and analyze various information and social networks from social media data.
Griffith University's Professor Bela Stantic, Head of School of Information and Communication Technology and Director of Big Data and Smart Analytics lab - IIIS, explains why big data analysis is important. He says the tourism industry, in particular, can benefit by not only identifying sentiment but also trends relating to weather. Professor Stantic says big data mining could also be used by health inspectors to track food poisoning cases, or emergency response teams to track the movement of people and their needs following a natural disaster. He says more and more companies are looking to big data analysis to gain a competitive edge over their competitors.
Views: 751 Griffith University
👋 So happy to have you joining us for our Buffer webinar all about social media data and analysis. In this webinar, you'll learn: * How to set benchmarks and calculate growth rates * 3 ways to analyze top-level social media stats * Spreadsheet tricks for maximizing your data learnings Hosted by Buffer's Director of Marketing, Kevan Lee, this webinar will be filled to the brim with advice on leveling up your social media data skills. We can't wait to help you see what's working on your social media campaigns and where to double down for more great results! -- Links discussed today: Benchmarks - https://goo.gl/Xyzdni Engagement - https://goo.gl/ykbCER Rates - https://goo.gl/jZXrbX -- Check out Buffer for super simple social media scheduling and analytics: http://buffer.com Subscribe here so you don’t miss our future videos (it's free!) - http://bit.ly/BufferYT Ask us any questions on social media or here in the comments. We’d love to answer them! Check Us Out On Social :) Instagram - https://instagram.com/buffer Twitter - https://twitter.com/buffer Facebook - https://www.facebook.com/bufferapp Pinterest - https://www.pinterest.com/bufferapp/ Snapchat - buffersnaps
Views: 3158 Buffer
What is SOCIAL DATA ANALYSIS? What does SOCIAL DATA ANALYSIS mean? SOCIAL DATA ANALYSIS meaning - SOCIAL DATA ANALYSIS definition - SOCIAL DATA ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Social data analysis is the data-driven analysis of how people interact in social contexts, often with data obtained from social networking services. The goal may be to simply understand human behavior or even to propagate a story of interest to the target audience. Techniques may involve understanding how data flows within a network, identifying influential nodes (people, entities etc.), or discovering trending topics. Social data analysis usually comprises two key steps: 1) gathering data generated from social networking sites (or through social applications), and 2) analysis of that data, in many cases requiring real-time (or near real-time) data analysis, measurements which understand and appropriately weigh factors such as influence, reach, and relevancy, an understanding of the context of the data being analyzed, and the inclusion of time horizon considerations. In short, social data analytics involves the analysis of social media in order to understand and surface insights which is embedded within the data. Social data analysis can provide a new slant on business intelligence where social exploration of data can lead to important insights that the user of analytics did not envisage/explore. The term was introduced by Martin Wattenberg in 2005 and recently also addressed as big social data analysis in relation to big data computing. Systems are available to assist users in analyzing social data. They allow users to store data sets and create corresponding visual representations. The discussion mechanisms often use frameworks such as a blogs and wikis to drive this social exploration/Collaborative intelligence. Social networking services are increasingly popular with the development of Web 2.0. Many of these services provide APIs that allow easy access to their data by responding to user queries with the requested data in the form of XML or JSON formatted strings. In order to protect privacy of their users, services such as Facebook require that the person requesting data has the necessary data access permissions. Services may also charge users for access to their data. Sources of social data include Twitter, Facebook, news websites, Wikipedia and We Feel Fine. Some APIs only allow access to data in small quantities, hence indexing the data in bulk can become a challenge. Six_Apart was the first social media company to provide a (free) firehose of content for all the posts in their network (provided over XMPP). Twitter later came along and provided a firehose as did companies like Spinn3r, Datasift, and GNIP. In most cases, we want to find out the relationships between social data and another event or we want to get interesting results from social data analyses to predict some events. There are some outstanding articles in this field, including Twitter Mood Predicts The Stock Market, Predicting The Present With Google Trends etc. In order to accomplish these goals, we need the appropriate methods to do the analyses. Usually, we use statistic methods, methods of machine learning or methods of data mining to do the analyses. Universities all over the world are opening graduate program in Social Data Analysis. When talking about social data analytics, there are a number of factors it's important to keep in mind (which we noted earlier): Sophisticated Data Analysis: what distinguishes social data analytics from sentiment analysis is the depth of the analysis. Social data analysis takes into consideration a number of factors (context, content, sentiment) to provide additional insight. Time consideration: windows of opportunity are significantly limited in the field of social networking. What's relevant one day (or even one hour) may not be the next. Being able to quickly execute and analyze the data is an imperative. Influence Analysis: understanding the potential impact of specific individuals can be key in understanding how messages might be resonating. It's not just about quantity, it's also very much about quality. Network Analysis: social data is also interesting in that it migrates, grows (or dies) based on how the data is propagated throughout the network. It's how viral activity starts—and spreads.
Views: 362 The Audiopedia
5 leading tools for Data Analytics, Web Analytics, Big Data Analysis, Social Media Analytics, Predictive Analysis, Business Analysis, Mobile apps — all in one cloud. ▶ Adobe Mobile Services — analytics tool for mobile apps that also includes acquisition link tracking, deep links, messaging and geo-targeting. ▶ Adobe Analytics Report Builder — an Excel plug-in to query analytics data in real-time. Ideal for those who used to work in Microsoft Excel. ▶ Reports and Analytics — web analytics for beginners. ▶ Ad Hoc Analysis — analytics tool for advanced analysts with unlimited real-time segmentation options. For those who spend most of their time analysing the data and looking for insights. ▶ Analysis Workspace — #1 data analytics tools for all type of users. 🎓 Individual Trainings on Adobe Analytics https://training.osadchuka.com 📌 SUBSCRIBE https://www.youtube.com/channel/UCq8K5VisTEpzz9aCEHlsz2Q?sub_confirmation=1 - - - - - - - - - - - - WATCH OTHER VIDEOS - - - - - - - - - - - - 🎥 Adobe Analytics vs Google Analytics https://youtu.be/QsiuTFsW4e0 🎥 Analysis Workspace Tutorials https://www.youtube.com/watch?v=UhAx8rHUrCY&list=PLdHxf9so_4cwoWOpjjCXs9fwifUhk5Syp 🎥 Bitcoin Analysis in Adobe Analytics https://youtu.be/jO1pZJwbjpM 🎥 Actual vs Target Analysis https://youtu.be/6HpBbVyzG8M - - - - - - - - - - - - USEFUL LINKS - - - - - - - - - - - - - - - - - - 🔗 Adobe Mobile Services https://marketing.adobe.com/resources/help/en_US/mobile/usage_overview.html 🔗 Report Builder https://www.adobe.com/data-analytics-cloud/analytics/report-builder.html 🔗 Reports and Analytics https://marketing.adobe.com/resources/help/en_US/sc/user/t_running_report.html 🔗 Ad Hoc Analysis https://www.adobe.com/data-analytics-cloud/analytics/ad-hoc-analysis.html - - - - - - - - - - - - LET’S GET CONNECTED - - - - - - - - - - - - 📷 Instagram https://www.instagram.com/adobeanalytics_pro/ 👤 LinkedIn https://linkedin.com/in/andreyosadchuk #AdobeAnalytics #WebAnalytics #Analytics #AnalyticsTraining #WebAnalyticsTraining
Views: 1842 Andrey Osadchuk
Do you need to capture and analyze social media content? Discover how NVivo can import and analyze social media content from Twitter, Facebook, LinkedIn and YouTube. http://www.qsrinternational.com
Views: 5332 NVivo by QSR
08 How to analyze competitor's social media for SEO talks about researching competitor's social media interaction. Created by https://goo.gl/5MTRxf (one of Australia's most affordable search engine optimization service providers) this video lessons explains how to analyze the engagement of online competitors. Almost all of the social interaction and intelligence gathering platforms on internet give you research data in its visual form. You can save time and better understand what your competitors are doing on their social media marketing. Visualizing this research data for a paid fee is usually within the reach of large businesses as they must at all times be in the know as far as what they competitors are doing on social media area. For example: promotions, new campaigns, new price sets, how popular is a particular social media marketing campaign. You can and should also utilize your competitor's social media marketing strategies. You can visit this website to learn more: https://www.socialbakers.com/ Furthermore, realize that you actually can manually analyze your competitors social media presence, because most of the data these large social analytic platform offer you is actually freely available to general public. Meaning, you can visit your competitors social profiles and dig deep within those social profiles. For example: majority of YouTube videos have statistics made available public by the channel owner. You can then analyze these stats to see how your competitors are interacting on social platforms like YouTube. Keeping an close eye on the video views, likes, dislikes, shares and critically communication which are usually visible through comments and replies. Similar analysis can be made through Facebook and Twitter. Although almost all of the data will be available for you to analyze and research, you should definitely research the communication which is taken place on these platform. To learn more about what that means simply browse through "How to SEO related videos" that can be found at: https://www.youtube.com/user/rankyaseoservices/videos These videos also contain all of the video statistics for public to view and research, and this is true for almost all of the popular videos on YouTube. Because you need to understand that it is more important to analyze your ideal customer's wants, needs, likes, dislikes and aspirations, because only then can you create your landing pages according to what you have identified as important for your ideal customers. Above all else remember, many social media analytics can show you stats in form of numbers, but your ultimate online success will be dependent on your ability to provide answers, solutions to your customers. That is why, although all of the tools available for analyzing your competitor's social media for SEO are plentiful, none of them can reveal the human element which is critical for you to research and provide solutions for. If you believe this video tutorial has contributed to your understanding, then why not share it around so that others may also learn from it. Thank you for sharing this URI: https://youtu.be/vzrsCWjE568 This video session was first uploaded by #rankyaseoservices for my loyal fans, subscribers and new visitors. Thank you for learning with me, and thank you for subscribing.
Views: 9598 RankYa
Entrepreneurs & leaders often don't use data and social media analytics to adjust their approach to dissemination of their messages. Just like applied behavior analysis (ABA) practitioners adjust their interventions based on trends in the data, entrepreneurs must change social media strategies that are not working. Some areas discussed in this video are: - Facebook Closed Groups - Klout Score Metrics - Youtube Audience Retention - Google Analytics - Facebook Dark Posts - LinkedIn Social Selling Index (SSI) Support Us & Advertise Here https://www.patreon.com/brettdinovi #hackinghumanbehavior #appliedbehavioranalysis Check out our leadership & ABA webinars here https://brettdassociates.com/payment-... Brett DiNovi is a Board Certified Behavior Analyst & the founder & CEO of the largest award-winning behavioral consultation group of its kind on the East Coast of the United States which deploys over 400 consultants throughout NJ, PA, NY, DE, & worldwide through the use of remote video consultation. https://www.brettdassociates.com/ https://www.linkedin.com/in/brett-dinovi-bcba-8b12aa7/ https://twitter.com/Bdinovi https://www.facebook.com/brett.dinovi.9 https://www.instagram.com/brettdinovi/ https://www.ghostcodes.com/bdinovi
Views: 301 Brett DiNovi
This video is the first in a series that walks through all necessary steps for social media data mining and analysis with Raspberry Pi. Part 1 describes all the necessary hardware for the project and how to set up that hardware in just five minutes. Recorded for the University of Maine at Augusta.
Views: 2480 James Cook
The objective of this channel is to give you an overview of pandas in analytics for business practitioners especially as Marketing/ Social Media Analyst tapping on big data: pandas is a DataFrame Framework, a library that stores data in a highly efficient spreadsheet format and functions. Efficient in: Data Structure (numpy) Computing time (since DataFrame is processed by C++, it runs in a well streamlined computing environment) Highly optimized and updated processes And I will end the sharing with some planned resources to help you learn analytics in the future. Feel free to access my github for Twitter Social Media Analysis (http://bit.ly/2koxDdZ) This is the playlist where I am going to explain step by step of this tutorial (https://youtu.be/YnMhFV8Q_K4) Hopefully by the end of this video you could be more inspired to learn analytics and follow through the journey Feel free to open my repository(contains powerpoint slides at): https://drive.google.com/drive/folders/0B7MOgjR94z_veUdHVGV4aENZSkk
Views: 1757 Vincent Tatan
Sentiment Analysis of Social Media Texts Saif M. Mohammad and Xiaodan Zhu October 25, 2014 - Morning Tutorial notes Abstract: Automatically detecting sentiment of product reviews, blogs, tweets, and SMS messages has attracted extensive interest from both the academia and industry. It has a number of applications, including: tracking sentiment towards products, movies, politicians, etc.; improving customer relation models; detecting happiness and well-being; and improving automatic dialogue systems. In this tutorial, we will describe how you can create a state-of-the-art sentiment analysis system, with a focus on social media posts. We begin with an introduction to sentiment analysis and its various forms: term level, message level, document level, and aspect level. We will describe how sentiment analysis systems are evaluated, especially through recent SemEval shared tasks: Sentiment Analysis of Twitter (SemEval-2013 Task 2, SemEval 2014-Task 9) and Aspect Based Sentiment Analysis (SemEval-2014 Task 4). We will give an overview of the best sentiment analysis systems at this point of time, including those that are conventional statistical systems as well as those using deep learning approaches. We will describe in detail the NRC-Canada systems, which were the overall best performing systems in all three SemEval competitions listed above. These are simple lexical- and sentiment-lexicon features based systems, which are relatively easy to re-implement. We will discuss features that had the most impact (those derived from sentiment lexicons and negation handling). We will present how large tweet-specific sentiment lexicons can be automatically generated and evaluated. We will also show how negation impacts sentiment differently depending on whether the scope of the negation is positive or negative. Finally, we will flesh out limitations of current approaches and promising future directions. Instructors: Saif M. Mohammad, Researcher, National Research Council Canada Saif Mohammad is a Research Officer at the National Research Council Canada. His research interests are in Computational Linguistics, especially Lexical Semantics. He develops computational models for sentiment analysis, emotion detection, semantic distance, and lexical-semantic relations such as word-pair antonymy. Xiaodan Zhu, Researcher, National Research Council Canada Xiaodan Zhu is a Research Officer at the National Research Council Canada. His research interests are in Natural Language Processing, Spoken Language Understanding, and Machine Learning. His recent work focuses on sentiment analysis, emotion detection, speech summarization, and deep learning. The instructors, along with Svetlana Kiritchenko, developed the NRC-Canada Sentiment Analysis System, which was the top-performing system in recent SemEval shared-task competitions (SemEval-2013, Task 2, SemEval-2014 Task 9, and SemEval-2014 Task 4).
Views: 32865 emnlp acl
This video shows how to use SNA package to analyze social networks in R programming language. Learn the basics of R language and try data science! Ram Subramaniam Stanford
Views: 79504 Ram Subramaniam
Data mining algorithms are focused on finding frequently occurring patterns in historical data. These techniques are useful in many domains, but for fraud detection it is exactly the opposite. Rather than being a pattern repeatedly popping up in a data set, fraud is an uncommon, well-considered, imperceptibly concealed, time-evolving and often carefully organized crime which appears in many types and forms. As traditional techniques often fail to identify fraudulent behavior, social network analysis offers new insights in the propagation of fraud through a network. Indeed, fraud is not something an individual would commit by himself, but is often organized by groups of people loosely connected to each other. The use of networked data in fraud detection becomes increasingly important to uncover fraudulent patterns and to detect in real-time when certain processes show some characteristics of irregular activities. Although analyses focus in the first place on fraud detection, the emphasis should shift towards fraud prevention, i.e. detecting fraud before it is even committed. As fraud is a time-evolving phenomenon, social network algorithms succeed to keep ahead of new types of fraud and to adapt to changing environment and surrounding effects.
Views: 8940 Bart Baesens
Approaches to analysis of digital data for social science research. Focus is on both digitized methods those that have been adapted to the online environment and those that have been designed specifically for analysis of webcontent including social media data. For more methods resources see: http://www.methods.manchester.ac.uk
Views: 1222 methodsMcr
Abstract: People love to talk on the web. How can we listen to what they're telling us, and why would we want to? This workshop will discuss some methods of collecting social media data to construct larger---and in some cases, more naturalistic---datasets than laboratory-based experiments yield. We'll cover methods for building datasets through Python-accessible Twitter APIs, and structuring both the search query and the experimental question to obtain data that is appropriate in both content and amount. We'll also discuss connections between data and metadata, with a focus on geolocation, as well as ways to collect online conversations and interactions. Instructor: Gabriel Doyle (Stanford University) --- Before running this tutorial, you'll need to sign up with the Twitter API. Follow the instructions here: https://github.com/Data-on-the-Mind/2017-summer-workshop/blob/master/doyle-twitter/README.md --- Part of the Data on the Mind 2017 summer workshop: http://www.dataonthemind.org/2017-workshop Funded by the Estes Fund: http://www.psychonomic.org/page/estesfund Organized in collaboration with Data on the Mind: http://www.dataonthemind.org Videography by DeNoise Studios: http://www.denoise.com Workshop hashtag: #dataonthemind
Views: 692 Berkeley Institute for Data Science (BIDS)
This Digital Analytics Fundamentals Tutorial will give an introduction to Digital Analytics, introduction to Data Analytics followed by some real life examples. Digital Analytics is the science of analysis that focuses on Internet data. It involves the collection, analysis, and data-informed decisions leading to the optimization of an organization's digital ecosystem and supporting business processes. Data from websites, mobile applications, social media, Internet of Things, or third party sources are commonly combined with back-office Customer Relationship Management (CRM) and Sales systems to inform business decisions. Digital Analytics has become an integral part of core business strategies, workflow optimization, and maintaining a competitive edge. This Digital Analytics Tutorial will explain the topics listed below: -( 00:14 ) Introduction to Digital Analytics -( 01:31 ) Introduction to Data Analytics -( 02:04 ) Understanding the 10/90 Rule -( 03:30 ) Understanding 20/80 Rule -( 08:48 ) Digital Analytics Continuous Improvement Process and Methodology -( 15:18 ) Tools, Technology, and Data Integration -( 17:23 ) Digital Analytics Real-life Example #DigitalMarketing #SimplilearnDigitalMarketing #DigitalMarketingCourse #DigitalMarketingCertification #DigitalMarketingCertifiedAssociate Subscribe to Simplilearn channel for more Social Media Marketing Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 For More Digital Marketing Tutorial videos, check our Digital Marketing tutorial for beginners playlist: https://www.youtube.com/watch?v=xA_yMYN19ug&list=PLEiEAq2VkUULa5aOQmO_al2VVmhC-eqeI Digital Marketing Articles: https://www.simplilearn.com/resources/digital-marketing?utm_campaign=Digital-Analytics-Fundamentals-WaDjcajOmUo&utm_medium=Tutorials&utm_source=youtube To Gain in-depth knowledge of Digital Analytics and other Digital Marketing concepts, check out Advanced Web Analytics Course: https://www.simplilearn.com/digital-marketing/web-analytics-certification-training?utm_campaign=Digital-Analytics-Fundamentals-WaDjcajOmUo&utm_medium=Tutorials&utm_source=youtube ------------------------------------- What’s the focus of this Advanced Web Analytics course? Advanced Web Analytics is the science of analysis that focuses on Internet data. It involves the collection, analysis, and data-informed decisions leading to the optimization of an organization's digital ecosystem and supporting business processes. Data from websites, mobile applications, social media, Internet of Things, or third party sources are commonly combined with back-office Customer Relationship Management (CRM) and Sales systems to inform business decisions. Web Analytics has become an integral part of core business strategies, workflow optimization, and maintaining a competitive edge. This Web Analytics course covers fundamental concepts of analytics and deep dives into web, social, content and mobile analytics common scenarios and covers the popular web analytics tools used by marketers across the major industry domains. Although this course approaches on learning Digital Analytics from a managerial perspective, it will showcase tips and techniques for the most common google web analytics platform and several other relevant tools along the way. -------------------------------------- What are the course objectives of this Advanced Web Analytics Course? Advanced Web Analytics training gives participants well-rounded knowledge in digital data analytics, including: 1. How to leverage data from various sources to conduct quantitative and qualitative research, and deliver actionable, data-informed business insights 2. How digital data analytics drives important insights for all aspects of your customer’s life cycle across the entire digital world 3. Uncover and learn about the various analysis capabilities enabled through digital data 4. How to better inform business decisions with rigorous analysis techniques ---------------------------------------- Who should take this course? This Digital Analytics training module is suitable for professionals who want to learn and specialize in digital analytics to boost their skillsets in the digital marketing industry and beyond. This course is best suited for participants who are: 1. Online Web Analytics Implementers 2. Online Web Analytics Data Reporters 3. Digital Analysts 4. Digital Marketers 5. Managers 6. Web Analytics Certification Aspirants 7. Digital Analytics Certification Aspirants ---------------------------------------- For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0"
Views: 9332 Simplilearn
Learn how to create a social media dashboard using Google Data Studio and Supermetrics. You'll learn exactly how to create a concise report to show your likes and followers on Facebook, LinkedIn and Twitter, plus include metrics from Google Analytics. Get the dashboard template: http://lovesdata.co/IoDyk Get Supermetrics: http://lovesdata.co/pLtr2 (affiliate link) Join my Google Data Studio course: https://www.lovesdata.com/courses/google-data-studio EXTRA RESOURCES: Tutorial on creating a Google Analytics dashboard using Google Data Studio: https://www.youtube.com/watch?v=zHpxMIiJrTA Tutorial on creating a Google Search Console dashboard using Google Data Studio: https://www.youtube.com/watch?v=O4sIwhahak0 JOIN MY FREE GOOGLE ANALYTICS COURSE: http://lovesdata.co/rJ0WL SUBSCRIBE FOR MORE VIDEOS: http://www.youtube.com/subscription_center?add_user=lovesdata JOIN THE CONVERSATION! Twitter: https://twitter.com/lovesdata Facebook: https://www.facebook.com/LovesData/ LinkedIn: https://www.linkedin.com/company/loves-data Google+: https://plus.google.com/+LovesData Welcome to my YouTube channel! I'll be teaching you how to get the most out of your data to improve your website and online marketing, with a focus on Google AdWords and Google Analytics. Thanks for your support!
Views: 14683 Loves Data
This video walks through the process of loading social network data into R for use with the package igraph by 1) typing in a short edge list into an R script), 2) importing a CSV file of an edge list, 3) importing a CSV file of an adjacency matrix. Shot for the University of Maine at Augusta
Views: 12736 James Cook
VideoLectures.Net Single Lectures Series View the complete series: http://videolectures.net/single_lecture_series/ Speaker: Jure Leskovec, Computer Science Department, Stanford University License: Creative Commons CC BY-NC-ND 3.0 More information at http://videolectures.net/site/about/ More talks at http://videolectures.net/ Online social media represent a fundamental shift of how information is being produced, transferred and consumed. The present tutorial investigates techniques for social media modeling, analytics and optimization. First we present methods for collecting large scale social media data and then discuss techniques for coping with and correcting for the effects arising from missing and incomplete data. We proceed by discussing methods for extracting and tracking information as it spreads among the users. Then we examine methods for extracting temporal patterns by which information popularity grows and fades over time. We show how to quantify and maximize the influence of media outlets on the popularity and attention given to particular piece of content, and how to build predictive models of information diffusion and adoption. As the information often spreads through implicit social and information networks we present methods for inferring networks of influence and diffusion. Last, we discuss methods for tracking the flow of sentiment through networks and emergence of polarization. 0:00 Social Media Analytics: Part 1: Information flow 1:35 Information and Networks 2:37 Social Media: Big change 3:37 Social Media: Opportunities 4:01 Social Media: Value proposition 4:28 Applications: Reputation management 5:30 Applications: Citizen response 6:37 Applications: Real-time citizen journalism 7:12 Applications: Social media marketing 7:57 Applications: Human behaviour analysis 8:34 The tutorial: Social Media 9:33 Tutorial Outline (1) 9:59 Part 1 of the Tutorial: Overview 10:35 Social Media Data: Spinn3r 12:00 Tracing Information Flow
Views: 5728 VideoLecturesChannel
Caitlin Macleod https://2018.pycon-au.org/talks/45252-accessing-and-analysing-your-own-social-media-data/ What information do social media websites really collect and store about you? I will show you how to access that data from a few different social media pages and analyse it for your own use, even if you've never used python data analytics tools before! Python, PyCon, PyConAU, australia, programming, sydney This video is licensed under CC BY 3.0 AU - https://creativecommons.org/licenses/by/3.0/au/ PyCon Australia (“PyCon AU”) is the national conference for the Python Programming Community, bringing together professional, student and enthusiast developers with a love for developing with Python. PyCon AU, the national Python Language conference, is on again this August in Sydney, at the International Convention Centre, Sydney, August 24 - 28 2018. Python, PyCon, PyConAU
Views: 308 PyCon Australia
[http://blue.host/EwsZ303GsQd] Tracking four basic metrics (total traffic, source of traffic, bounce rate, and conversion rate) can help small business owners fine-tune their web strategy and, ultimately, increase profits. There are countless things on your website that you can measure and analyze, but in this video, we focus on four key metrics for beginners. Total Traffic — The number of people visiting your site is a strong indicator of its overall health. This measurement can also help you identify what you’re doing right. For example, if you get a huge spike in traffic after publishing a guest post, you know your visitors liked the content. Source of Traffic — This measurement tells you how people are finding your website online. This can help you create a strategy going forward. If you find that most of your new visitors come for your social media posts, you’ll know that you should direct your efforts at creating more social content. Bounce Rate — A bounce is when a visitor leaves your site before clicking on another page. A high bounce rate can mean that the people coming to your site aren’t sticking around to make a purchase. This could be for a variety of reasons, including unappealing content, unattractive website design, or a hard-to-navigate menu. Conversion Rate — Conversion rate is the percentage of visitors who actually do what you’re asking them to do on your site, such as buy your product or subscribe to your blog. The higher your conversion rate, the better your site is doing. A low conversion rate can signal that your content and call to action are weak. Tracking web analytics does require time and effort, but carefully deciphering the data can lead to a more effective website — and more sales. VISIT THE BLUEHOST BLOG FOR MORE WEBSITE TIPS https://www.bluehost.com/blog/ SUBSCRIBE TO OUR CHANNEL https://www.youtube.com/subscription_center?add_user=bluehost CONNECT WITH US Facebook: https://www.facebook.com/bluehost Twitter: https://twitter.com/bluehost Google+: https://plus.google.com/+bluehost
Views: 40439 Bluehost
This video is eighth in a series for beginners in the use of an inexpensive, accessible Raspberry Pi computer to carry out social media data mining and analysis. In this installment, I walk through the process for extracting hashtag, URL (web address), and mentioning data from Twitter posts ("Tweets") and saving them in CSV files that are linked by a common reference to Tweet ID. Coming up in installment #9: using input commands to customize searches without changing the underlying Python code.
Views: 899 James Cook
www.datameer.com Dr. Michael Wu of Lithium Technologies breaks down social network analysis.
Views: 117 Big Data & Brews