Social Media Analytics involves extracting insights from social media data to aid decision making. It has 7 layers including text, networks, actions, hyperlinks, mobile, location and search engines. Common goals are measuring brand loyalty, generating leads, and driving traffic. Challenges include large amounts of diverse and unstructured data from various social media platforms.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Discussed what is Prescriptive Analytics, comparison between Descriptive and Prescriptive Analytics, process, methods and tools. A report presentation conducted at University of East - Manila, Philippines dated July 6, 2017.
Are you curious about the concept of Social Media Listening?
Then read on as these few slides explain it in a very easy to understand manner. If you're new to Social Listening it'll be your first step towards developing a complete understanding.
If you're familiar with it, you can use these slides to explain it to your peers, managers or your clients
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Discussed what is Prescriptive Analytics, comparison between Descriptive and Prescriptive Analytics, process, methods and tools. A report presentation conducted at University of East - Manila, Philippines dated July 6, 2017.
Are you curious about the concept of Social Media Listening?
Then read on as these few slides explain it in a very easy to understand manner. If you're new to Social Listening it'll be your first step towards developing a complete understanding.
If you're familiar with it, you can use these slides to explain it to your peers, managers or your clients
This presentation is on the topic of "What is a Social Media Strategy." The presentation discusses the what, the why, the how, and the do of developing a social media strategy. The presentation is filled with examples and a plethora of resources.
Title: Brand Matters
In this 60 minute intro session, you will learn a general overview of how to create, launch and maintain your brand and why your brand awareness affects how much capital you will get.
- Why your brand matters and how it impacts your success
- Creating & defining your brand
- Launching your brand publicly
- How to build your brand on social networks such as Twitter, Facebook, & Linkedin
In following sessions we will dive deeper into each social network and discuss how to make the most of each social platform.
Digital marketing - why it's important, what are current trends & themes - Social, Mobile & Content, What these mean, how to begin to create a digital strategy.
A guide to social media marketing for small/medium businesses. How create a strategic plan, execution, and analysis of the social media results. Tips for creating content and setting up a process for consistent content development and publishing.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. LESSON OBJECTIVES
• Composition of the seven layers of Social Media Analytics
• Origin and history of Social Media Analytics
• Common goals, KPIs and use cases for Social Media Analytics
• Descriptive, predictive and prescriptive analytics for social media
• Differences between Business Analytics and Social Media Analytics
• Challenges to the efficient use of Social Media Analytics
3. What is it??....
Social Media Analytics is the art and science of extracting valuable
insights from vast amounts of semi-structured and unstructured social
media data to enable informed and insightful decision-making
4. • Seven layers of Social Media Analytics
1. Text
2. Networks
3. Actions
4. Hyperlinks
5. Mobile
6. Location
7. Search engines
14. Some Popular Reasons for Using
Social Media Analytics
• Measure brand loyalty
• Generate business leads
• Drive traffic to owned media (Facebook pages, corporate blogs,
company webpages, organizational microsites, specific mobile
applications, etc.)
• Predictive business forecasting
• Demographics and psychographics around specific audiences and
topics
• Business intelligence and market research
• Business decision-making
Social Media Analytics is the art and science of extracting valuable insights from vast amounts of semi-structured and unstructured social media data to enable informed
and insightful decision-making. In this chapter, we examine this new and constantly emerging field that continues to evolve as social media matures. Social Media
Analytics is a science as it requires systematically identifying, extracting, and analyzing various social media data using a variety of sophisticated tools and techniques
(this book will examine some of the tools and technology to extract and use social media data). However, Social Media Analytics is also an art, which requires analysts,
stakeholders, and business owners to align the insights gained via the analytics with business goals and objectives. We should master both the art and science of Social
Media Analytics to get full value from it.
Social media text analytics deals with the extraction and analysis of business insights
from textual elements of social media content, such as comments, tweets, blog posts,
and Facebook status updates. Text analytics is mostly used to understand social
media users’ sentiments or identify emerging themes and topics.
Social media network analytics extract, analyze, and interpret personal and
professional social networks, for example, Facebook, and Twitter. Network analytics
seeks to identify infl uential nodes (e.g., people and organizations) and their position in
the network.
Social media actions (Intermediate Metrics) analytics deals with extracting, analyzing,
and interpreting the actions performed by social media users, including likes, shares,
mentions, and endorsement. Actions analytics are mostly used to measure popularity,
influence, and prediction in social media.
. Hyperlink analytics is about extracting, analyzing, and interpreting social media
hyperlinks (e.g., in-links and out-links). Hyperlink analysis can reveal sources of
incoming or outgoing web traffic to and from a webpage or website.
Mobile analytics is the next frontier in the social business landscape. Mobile analytics
deals with measuring and optimizing user engagement through mobile applications (or
apps for short).
We shall discuss about Mobile Analytics in greater detain in our next lesson
Location analytics, also known as spatial analysis or geospatial analytics, is concerned
with mining and mapping the locations of social media users, contents, and data.
Search engines analytics focuses on analyzing search engine data to gain valuable
insights into a range of areas, including trends analysis, keyword monitoring, keyword
research, search results and search engine marketing (text ads, etc.).
Based on Google Trends data, the term Social Media Analytics appeared over the
Internet horizon during 2008, and interest in it (based on Internet searches for the term)
has steadily increased since then. Social Media Analytics was present as a cottage
industry,, as
early as 2003 In 2008, Google Trends
began to detect enough usage of the term “Social Media Analytics” to show up in its
trend reporting, and the subject is becoming ever-more popular as we move towards
2020. No doubt, the growth in the development and usage of various social media
channels spawned Social Media Analytics, as the means to better understand and
harness “social data.”
Social media has become one of the main ways people express themselves.
Because of this activity, Social Media Analytics is gaining prominence among both the
research and business communities.
The main purpose of Social Media Analytics is to enable informed and insightful
decision-making by leveraging social media data
The following are some sample questions that can be answered with social media
analytics:
• What are customers using social media saying about our brand or a new product
launch?
• Which content posted over social media is resonating more with clients or
customers?
• How can we harness social media data (e.g., tweets and Facebook comments) to
improve our product/services?
• Is the social media conversation about our company, product, or service positive,
negative, or neutral?
• How can we leverage social media to promote brand awareness?
• Who are our infl uential social media followers, fans, and friends?
• Who are our infl uential social media nodes (e.g., people and organizations) and
what is their position in the network?
• Which are the social media platforms driving the most traffi c to our corporate
website?
• Where is the geographical location of our social media customers?
• What are the keywords and terms trending over social media?
• How current is our business with social media, and how many people are
connected with us?
• Which websites are linked to our corporate website?
• How are my competitors doing on social media?
Note the Social media’s role informing each business function/stakeholder in this digram
The questions, use cases, and goals that inform social media can be measured using
Key Performance Indicators such as share of voice and sentiment score
Descriptive analytics is mostly focused on gathering and describing social media data
in the form of reports, visualizations, and clustering to understand a business problem.
Actions analytics (e.g., number of likes, tweets, and views) and certain aspects of
text analytics are examples of descriptive analytics. Social media text (e.g., user
comments), for instance, can be used to understand users’ sentiments or identify
emerging trends by clustering themes and topics. Currently, descriptive analytics
accounts for most Social Media Analytics.
Predictive analytics involves analyzing large amounts of accumulated social media
data to predict a future event. For example, an intention expressed over social media
(such as buy, sell, recommend, quit, desire, or wish) can be mined to predict a future
event (such as a purchase). Alternatively, a business manager can predict sales
fi gures based on past visits (or in-links) to a corporate website. The TweepsMap tool,
for example, can help users determine the right time to tweet for maximum alignment
with the right audience time zone
While predictive analytics help to predict the future, prescriptive analytics suggest the
best action to take when handling a situation or scenario. 4 For example, if you have
groups of social media users that display certain patterns of buying behavior, how
can you optimize your offering to each group? Like predictive analytics, prescriptive
analytics has not yet found its way into social media data.
Social media data is high-volume, high-velocity, and highly diverse, which, in a sense,
is a blessing regarding the insights it carries; however, analyzing and interpreting
it presents several challenges. Analyzing unstructured data requires new metrics,
tools, and capabilities, particularly for real-time analytics, that most businesses do not
possess
These are some of the social media analytical tools that you can use .
