How data visualization will help in your career and why one should choose it.
See this video of Hans Rosling, a master of data visualisation: https://www.youtube.com/watch?v=jbkSRLYSojo
Data visualization is a set of processes that uses visual representations of data to help people understand patterns and insights more easily than textual data. It has several benefits, such as turning information into a visual landscape to focus on important details, helping new insights and innovations emerge. Effective data visualization examples include charts that use the size and color of boxes to indicate values or timeframes when certain events commonly happen. Overall, data visualization can help managers make sense of large amounts of information and find elegant solutions by transforming data into beautiful and clarifying graphics.
Data visualization is a complex set of processes which is like an umbrella that covers both information and scientific visualization simultaneously. We can’t ignore the benefits of data visualization for its accurate quantities, as it is easily comparable. It also lends valuable suggestion pertaining to the usage of its technique and tools. Scientifically its effectiveness lies in our brain's ability to maintain a proper balance between perception and cognition through visualization.
This document discusses 5 limitations of spreadsheets for data analysis and visualization and provides alternatives:
1. Spreadsheets can't handle large, diverse datasets from multiple sources like databases and data warehouses. Integrating and analyzing all relevant data is important for accurate insights.
2. Complex calculations and macros can slow down spreadsheets, wasting time. Connecting to live data sources allows fast analysis of large datasets.
3. Blending and cleaning data from different sources is difficult in spreadsheets. Joining datasets on common fields provides a unified view.
4. Spreadsheets offer limited basic charts but advanced visualizations like maps and dashboards provide faster, more intuitive understanding.
5. Interactive dashboards with up
Better decisions, by design - Data visualisation for decision supportMischa Weiss-Lijn
Data-driven decision-making can become the norm for people across your organisation if you enable them to use vision to think, by harnessing the data they need with well-designed visualisations. In this talk we’ll look at why this is important and how it makes a difference. We’ll see how visualisation impacts different types of users and will look at the different types of data visualisation and their relevance to the work environment. Finally, we’ll explore some examples of the data visualisation projects we’ve worked on to help professional users make better decisions, faster.
Data visualizations make huge amounts of data more accessible and understandable. Data visualization, or "data viz," is becoming largely important as the amount of data generated is increasing and big data tools are helping to create meaning behind all of that data.
This SlideShare presentation takes you through more details around data visualization and includes examples of some great data visualization pieces.
This document discusses different types of mapping and how they can help with organization and productivity. It defines mind maps as hand-drawn diagrams with a central concept and linked ideas, while mindmanager maps are digital diagrams created with software. The document outlines the basic steps for creating a mind map by hand and emphasizes that the most important rule is to do what works for the individual. Various applications of mapping are mentioned for focus, review, strategizing, and more.
This document discusses how spreadsheets can limit data analysis capabilities and recommends integrating data from multiple sources and using better visualization tools. It notes that spreadsheets cannot handle large datasets, integrate different types of data, or create advanced visualizations needed for insightful analysis. Better tools allow users to blend, clean, and visualize integrated data in interactive dashboards with current data feeds to gain a holistic understanding and answer unanticipated questions.
Data visualization is a set of processes that uses visual representations of data to help people understand patterns and insights more easily than textual data. It has several benefits, such as turning information into a visual landscape to focus on important details, helping new insights and innovations emerge. Effective data visualization examples include charts that use the size and color of boxes to indicate values or timeframes when certain events commonly happen. Overall, data visualization can help managers make sense of large amounts of information and find elegant solutions by transforming data into beautiful and clarifying graphics.
Data visualization is a complex set of processes which is like an umbrella that covers both information and scientific visualization simultaneously. We can’t ignore the benefits of data visualization for its accurate quantities, as it is easily comparable. It also lends valuable suggestion pertaining to the usage of its technique and tools. Scientifically its effectiveness lies in our brain's ability to maintain a proper balance between perception and cognition through visualization.
This document discusses 5 limitations of spreadsheets for data analysis and visualization and provides alternatives:
1. Spreadsheets can't handle large, diverse datasets from multiple sources like databases and data warehouses. Integrating and analyzing all relevant data is important for accurate insights.
2. Complex calculations and macros can slow down spreadsheets, wasting time. Connecting to live data sources allows fast analysis of large datasets.
3. Blending and cleaning data from different sources is difficult in spreadsheets. Joining datasets on common fields provides a unified view.
4. Spreadsheets offer limited basic charts but advanced visualizations like maps and dashboards provide faster, more intuitive understanding.
5. Interactive dashboards with up
Better decisions, by design - Data visualisation for decision supportMischa Weiss-Lijn
Data-driven decision-making can become the norm for people across your organisation if you enable them to use vision to think, by harnessing the data they need with well-designed visualisations. In this talk we’ll look at why this is important and how it makes a difference. We’ll see how visualisation impacts different types of users and will look at the different types of data visualisation and their relevance to the work environment. Finally, we’ll explore some examples of the data visualisation projects we’ve worked on to help professional users make better decisions, faster.
Data visualizations make huge amounts of data more accessible and understandable. Data visualization, or "data viz," is becoming largely important as the amount of data generated is increasing and big data tools are helping to create meaning behind all of that data.
This SlideShare presentation takes you through more details around data visualization and includes examples of some great data visualization pieces.
This document discusses different types of mapping and how they can help with organization and productivity. It defines mind maps as hand-drawn diagrams with a central concept and linked ideas, while mindmanager maps are digital diagrams created with software. The document outlines the basic steps for creating a mind map by hand and emphasizes that the most important rule is to do what works for the individual. Various applications of mapping are mentioned for focus, review, strategizing, and more.
This document discusses how spreadsheets can limit data analysis capabilities and recommends integrating data from multiple sources and using better visualization tools. It notes that spreadsheets cannot handle large datasets, integrate different types of data, or create advanced visualizations needed for insightful analysis. Better tools allow users to blend, clean, and visualize integrated data in interactive dashboards with current data feeds to gain a holistic understanding and answer unanticipated questions.
Crispino Cabral Kako Kamboh has completed the online Specialization "Excel to MySQL: Analytic Techniques for Business" from Coursera, consisting of 5 courses in business metrics, data analysis in Excel, data visualization in Tableau, managing big data with MySQL, and increasing real estate profits through data analytics. The Specialization taught skills to frame business challenges as data questions and use tools like Excel, Tableau, and MySQL to analyze data, create models and forecasts, design visualizations, and communicate insights, culminating in a capstone project applying these skills to a real-world business process.
This document discusses big data and how it can be interpreted in different ways. It notes that data can be both stubborn and stupid depending on how it is interpreted and used, and that data analysis can have both positive and negative outcomes. The document recommends that managers take informed decisions by aligning business goals, choosing alternative actions based on data analysis, and considering different problem solving perspectives to make the most of big data insights.
Techniques of Data Visualization for Data & Business AnalyticsMercy Akinseinde
This document provides an outline for a training on techniques of data visualization. It begins with introducing data visualization and its benefits such as making large amounts of data easy to summarize and see patterns. It discusses essential skills needed for data visualization as well as core principles like understanding context and purpose. The document then covers technology tools for data visualization, focusing on Tableau. It provides an overview of Tableau products and their differences. Finally, it outlines learning areas for Tableau, such as installation, connecting data, and creating different visual output types. The overall purpose is to provide staff skills in visualizing and communicating data through cutting-edge tools.
A short workshop from MERL Tech 2016 on how we can think more purposefully about telling stories with our data and designing visualizations to bring those stories to life in global health and development.
Data visualization is a technique for representing data in a graphical format to help people understand the significance of the data. It enables decision makers to see analytics visually and identify patterns. Data visualization is important as it can identify areas needing improvement, clarify factors influencing customer behavior, and help predict sales. It provides advantages like enhanced business insights, trend identification, and predictive analysis. Choosing the right visual is key to effective data visualization.
The document discusses simplifying analytics by focusing on important data and how to use it to improve business outcomes, rather than complex analytics. It recommends building an environment to accelerate data processing for faster insights and decisions. Companies should leverage business intelligence, data visualization, and data discovery tools, as well as machine learning models, to automate analysis and gain insights from large data sets. Different problems may require hypothesis-based or discovery-based approaches. The key is to identify important data, delegate analysis to tools when possible, visualize data for better understanding, uncover hidden patterns, and customize the approach to the specific problem and data.
The document provides the results of an IQ test, indicating a score of 126 and identifying the test taker as a "Visual Mathematician". It explains that this means they have strong visual-spatial and mathematical processing abilities, along with strengths in logic. As a Visual Mathematician, the test taker is able to understand and manipulate patterns visually and numerically to solve problems and come up with ideas that simplify processes. They tend to excel in clearly defined work environments and strategic activities like chess that involve pattern recognition.
Big data and visual analytics can provide new insights. Dr. Tomasz Bednarz discusses using analytics methods, models, training and visualization on big data to gain insights. Challenges include developing fast, efficient algorithms to analyze large, complex datasets from different sources and types.
The Importance of Data Visualisation in AnalyticsAndy Kriebel
Data visualization is important for data analysis because it allows analysts to find unknown patterns in data and make an impact by communicating their findings to stakeholders in order to change the world. Specifically, visualizing data makes it easier to spot problems, opportunities, and insights that may have otherwise gone unnoticed. This allows organizations to use data to do good in their communities by showing what resources are needed where and how people can help. In short, data visualization has the potential to identify issues and solutions that could positively change the world.
Data visualization is the graphical presentation of data that enables decision makers to easily understand complex concepts and identify patterns. It is an effective way to convey large amounts of information because the human brain processes visual representations better than text. Data is like soil that can bloom into flowers - meaning interesting patterns and insights are revealed when data is explored and visualized creatively. For businesses, data visualization can help uncover emerging trends to gain a competitive advantage and spot issues to address before they become problems. It is also an important tool for communicating insights to others in an engaging way. In conclusion, data visualization is a wise investment for making sense of big data.
This document discusses data visualization in the context of big data. It notes that data visualization is important for making sense of large datasets and gaining insights. However, visualizing big data presents challenges related to scalability, heterogeneity, and speed. Effective visualization of big data requires tools that can handle its scale and complexity through techniques like cloud computing and advanced user interfaces. The document also provides examples of different visualization techniques like word clouds and line charts that can be used to display different data types.
Stop searching for that elusive data scientistYogita Bansal
Companies are increasingly seeking data scientists to drive data-based decision making, but there is a lack of qualified candidates. To address this, companies should build effective teams by coordinating existing resources, promoting a data-focused culture, and encouraging all members to contribute insights from available data. Even small groups can draw meaningful conclusions and make informed decisions by maximizing their current capabilities.
This presentation contains the key ideas from the article "Simplify your analytics strategy" by Narendra Mulani published in HBR. This presentation is a part of my internship under Prof. Sameer Mathur, IIM-L
Building your own skills is one step in strengthening how you use visualization in your work, but fostering organizational change can be hard. Here are a few quick considerations on how to nurture data visualization as a personal skill and as an organizational value, and tips for successful collaborations on data visualization activities.
Originally presented as part of the HC3 Innovation Webinar Series on March 8, 2017.
Data visualization is the graphical representation of information and data. It is used to communicate data or information clearly and effectively to readers by leveraging the human mind's receptiveness to visual information. Effective data visualization can improve transparency and communication, answer questions, discover trends, find patterns, see data in context, support calculations, and present or tell a story. Common tools for data visualization include charts, graphs, maps, and diagrams. Specialized roles involved in data visualization include data visualization experts, data analysts, business intelligence consultants, tool-specific consultants, business analysts, and data scientists.
This document discusses data visualization and provides examples related to Egyptian elections. It defines data visualization as visually communicating information clearly and effectively. It also outlines the elements of an effective infographic. Several examples are presented that visualize Egyptian political party maps, election results, and presidential election results. Tools for creating visualizations like Infogr.am, Visual.ly, and Gephi are also mentioned. Finally, it describes the data visualization lifecycle from data collection to analysis to creating visualizations.
Katya Vladislavleva - Tech Startup Day 2015StartUps.be
This document discusses building a data science company called DataStories. It provides an overview of the company's services in creating mathematical models to predict and optimize key performance indicators from customer data. The document introduces the team members and shows growth in the company's total turnover from 2011-2016. It provides advice on scaling up the company, such as embracing communication technology, trusting your passion, and seeking mentorship.
Data Visualization Resource Guide (September 2014)Amanda Makulec
A summary guide to data visualization design, including key design principles, great resources, and tools (listed by category with short explanations) that you can use to help design elegant, effective data visualizations that help share your message & promote the use of your information.
Note that the tools & resources highlighted are suggested, and inclusion should not be considered as an endorsement from JSI.
Crispino Cabral Kako Kamboh has completed the online Specialization "Excel to MySQL: Analytic Techniques for Business" from Coursera, consisting of 5 courses in business metrics, data analysis in Excel, data visualization in Tableau, managing big data with MySQL, and increasing real estate profits through data analytics. The Specialization taught skills to frame business challenges as data questions and use tools like Excel, Tableau, and MySQL to analyze data, create models and forecasts, design visualizations, and communicate insights, culminating in a capstone project applying these skills to a real-world business process.
This document discusses big data and how it can be interpreted in different ways. It notes that data can be both stubborn and stupid depending on how it is interpreted and used, and that data analysis can have both positive and negative outcomes. The document recommends that managers take informed decisions by aligning business goals, choosing alternative actions based on data analysis, and considering different problem solving perspectives to make the most of big data insights.
Techniques of Data Visualization for Data & Business AnalyticsMercy Akinseinde
This document provides an outline for a training on techniques of data visualization. It begins with introducing data visualization and its benefits such as making large amounts of data easy to summarize and see patterns. It discusses essential skills needed for data visualization as well as core principles like understanding context and purpose. The document then covers technology tools for data visualization, focusing on Tableau. It provides an overview of Tableau products and their differences. Finally, it outlines learning areas for Tableau, such as installation, connecting data, and creating different visual output types. The overall purpose is to provide staff skills in visualizing and communicating data through cutting-edge tools.
A short workshop from MERL Tech 2016 on how we can think more purposefully about telling stories with our data and designing visualizations to bring those stories to life in global health and development.
Data visualization is a technique for representing data in a graphical format to help people understand the significance of the data. It enables decision makers to see analytics visually and identify patterns. Data visualization is important as it can identify areas needing improvement, clarify factors influencing customer behavior, and help predict sales. It provides advantages like enhanced business insights, trend identification, and predictive analysis. Choosing the right visual is key to effective data visualization.
The document discusses simplifying analytics by focusing on important data and how to use it to improve business outcomes, rather than complex analytics. It recommends building an environment to accelerate data processing for faster insights and decisions. Companies should leverage business intelligence, data visualization, and data discovery tools, as well as machine learning models, to automate analysis and gain insights from large data sets. Different problems may require hypothesis-based or discovery-based approaches. The key is to identify important data, delegate analysis to tools when possible, visualize data for better understanding, uncover hidden patterns, and customize the approach to the specific problem and data.
The document provides the results of an IQ test, indicating a score of 126 and identifying the test taker as a "Visual Mathematician". It explains that this means they have strong visual-spatial and mathematical processing abilities, along with strengths in logic. As a Visual Mathematician, the test taker is able to understand and manipulate patterns visually and numerically to solve problems and come up with ideas that simplify processes. They tend to excel in clearly defined work environments and strategic activities like chess that involve pattern recognition.
Big data and visual analytics can provide new insights. Dr. Tomasz Bednarz discusses using analytics methods, models, training and visualization on big data to gain insights. Challenges include developing fast, efficient algorithms to analyze large, complex datasets from different sources and types.
The Importance of Data Visualisation in AnalyticsAndy Kriebel
Data visualization is important for data analysis because it allows analysts to find unknown patterns in data and make an impact by communicating their findings to stakeholders in order to change the world. Specifically, visualizing data makes it easier to spot problems, opportunities, and insights that may have otherwise gone unnoticed. This allows organizations to use data to do good in their communities by showing what resources are needed where and how people can help. In short, data visualization has the potential to identify issues and solutions that could positively change the world.
Data visualization is the graphical presentation of data that enables decision makers to easily understand complex concepts and identify patterns. It is an effective way to convey large amounts of information because the human brain processes visual representations better than text. Data is like soil that can bloom into flowers - meaning interesting patterns and insights are revealed when data is explored and visualized creatively. For businesses, data visualization can help uncover emerging trends to gain a competitive advantage and spot issues to address before they become problems. It is also an important tool for communicating insights to others in an engaging way. In conclusion, data visualization is a wise investment for making sense of big data.
This document discusses data visualization in the context of big data. It notes that data visualization is important for making sense of large datasets and gaining insights. However, visualizing big data presents challenges related to scalability, heterogeneity, and speed. Effective visualization of big data requires tools that can handle its scale and complexity through techniques like cloud computing and advanced user interfaces. The document also provides examples of different visualization techniques like word clouds and line charts that can be used to display different data types.
Stop searching for that elusive data scientistYogita Bansal
Companies are increasingly seeking data scientists to drive data-based decision making, but there is a lack of qualified candidates. To address this, companies should build effective teams by coordinating existing resources, promoting a data-focused culture, and encouraging all members to contribute insights from available data. Even small groups can draw meaningful conclusions and make informed decisions by maximizing their current capabilities.
This presentation contains the key ideas from the article "Simplify your analytics strategy" by Narendra Mulani published in HBR. This presentation is a part of my internship under Prof. Sameer Mathur, IIM-L
Building your own skills is one step in strengthening how you use visualization in your work, but fostering organizational change can be hard. Here are a few quick considerations on how to nurture data visualization as a personal skill and as an organizational value, and tips for successful collaborations on data visualization activities.
Originally presented as part of the HC3 Innovation Webinar Series on March 8, 2017.
Data visualization is the graphical representation of information and data. It is used to communicate data or information clearly and effectively to readers by leveraging the human mind's receptiveness to visual information. Effective data visualization can improve transparency and communication, answer questions, discover trends, find patterns, see data in context, support calculations, and present or tell a story. Common tools for data visualization include charts, graphs, maps, and diagrams. Specialized roles involved in data visualization include data visualization experts, data analysts, business intelligence consultants, tool-specific consultants, business analysts, and data scientists.
This document discusses data visualization and provides examples related to Egyptian elections. It defines data visualization as visually communicating information clearly and effectively. It also outlines the elements of an effective infographic. Several examples are presented that visualize Egyptian political party maps, election results, and presidential election results. Tools for creating visualizations like Infogr.am, Visual.ly, and Gephi are also mentioned. Finally, it describes the data visualization lifecycle from data collection to analysis to creating visualizations.
Katya Vladislavleva - Tech Startup Day 2015StartUps.be
This document discusses building a data science company called DataStories. It provides an overview of the company's services in creating mathematical models to predict and optimize key performance indicators from customer data. The document introduces the team members and shows growth in the company's total turnover from 2011-2016. It provides advice on scaling up the company, such as embracing communication technology, trusting your passion, and seeking mentorship.
Data Visualization Resource Guide (September 2014)Amanda Makulec
A summary guide to data visualization design, including key design principles, great resources, and tools (listed by category with short explanations) that you can use to help design elegant, effective data visualizations that help share your message & promote the use of your information.
Note that the tools & resources highlighted are suggested, and inclusion should not be considered as an endorsement from JSI.
Data Visualization Design Best Practices WorkshopJSI
This document provides guidance on effective data visualization. It emphasizes starting with the audience and their needs, identifying the key story or message in the data, and using simple, clear design principles. Charts should be designed in 5-8 seconds to engage the audience. The document recommends several resources for choosing effective chart types and improving visualization skills. Overall, it stresses the importance of visualization in empowering stakeholders to make informed decisions.
Data Visualization Design Best Practices WorkshopAmanda Makulec
Presentation shared at the #MA4Health Data Visualization workshop cofacilitated with my colleague Tahmid Chowdhury. Our aim was to empower participants with simple principles they can apply to any graph or chart to improve its effectiveness in communicating information, and to share resources on viz design relevant to global health practitioners.
7 Key Benefits of Data Visualization Tools_BacklinkContent.pptxAlok Mishra
Mapsted's pattern visualization tool is the perfect choice. Mapsted pattern visualization is the expert-recommended choice for businesses looking to maximize space utilization and create a customer-centric in-store experience.
Data visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps. It provides an accessible way to see and understand trends, outliers, and patterns in data. Data visualization tools are essential for analyzing massive amounts of information and making data-driven decisions. The key benefits of data visualization are that it makes big data digestible, increases accessibility, and leads to greater efficiency and understanding. Good data visualization should communicate data clearly and effectively using graphics.
Tableau is a business intelligence tool that allows users to visually analyze data through interactive dashboards that depict trends, variations, and patterns in data through graphs and charts. It offers speed of analysis, self-reliant data discovery, the ability to blend diverse data sets, real-time collaboration, and centralized data. Tableau training from Sterling IT helps students learn the software and secure jobs in data visualization and business intelligence.
- The document discusses a session on data analysis and visualization for security professionals. It provides key learning points about using data and visualization to understand environments, thinking of solutions rather than just buying tools, and how visualization can quickly communicate complexity.
- Some tools mentioned for visualization include R, Python, Tableau, and MongoDB. Guidelines discussed include using simple, truthful visualizations and that data visualization is a skill that must be learned.
1) The document discusses a session on data visualization techniques for social change. It provides an agenda that covers measuring networked nonprofits, using data for assessment, learning and management, and communications and advocacy.
2) The session discusses how visualizing data through techniques like maps, placemats, dashboards and research findings can help nonprofits better understand and communicate information.
3) Effective data visualization follows design principles like maximizing data ink, using color and contrast effectively, allowing the purpose to guide the medium used, and incorporating classic graphic design elements. Visuals can help nonprofits evolve to more impactful communication.
Picturing Your Data is Better than 1,000 Numbers: Data Visualization Techniqu...NTEN
This document contains an agenda and slides from a presentation on data visualization for nonprofits. The agenda includes opening remarks and sessions on measuring the networked nonprofit, data assessment and learning, and communications and advocacy. The slides discuss creating a data-informed nonprofit culture, using data visualization principles like maximizing data-ink ratio and allowing purpose to select the medium. Examples are given of using maps, dashboards, and research findings visualization. The importance of visualizing data rather than just numbers is emphasized.
This presentation explores the power of data visualization and its impact on decision-making and storytelling. It discusses how effective data visualization can simplify complex datasets and enhance understanding, and how it can tell compelling stories by combining data analysis with visuals. Modern interactive visualization tools can help users uncover hidden insights and gain deeper understanding by exploring and analyzing data dynamically.
This presentation explores the power of data visualization and its impact on decision-making and storytelling. It discusses how effective data visualization can simplify complex datasets and enhance understanding, and how it can tell compelling stories by combining data analysis with visuals. Modern interactive visualization tools can help users uncover hidden insights and gain deeper understanding by exploring and analyzing data dynamically.
Understanding Data Science: Unveiling the Basics
What is Data Science?
Data science is an interdisciplinary field that combines techniques from statistics, mathematics, computer science, and domain knowledge to extract insights and knowledge from data. It involves collecting, processing, analyzing, and interpreting large and complex datasets to solve real-world problems.
Importance of Data Science
In today's data-driven world, organizations are inundated with data from various sources. Data science allows them to convert this raw data into actionable insights, enabling informed decision-making, improved efficiency, and innovation.
Intersection of Data Science, Statistics, and Computer Science
Data science borrows heavily from statistics and computer science. Statistical methods help in understanding data patterns, while computer science provides the tools to process and analyze large datasets efficiently.
Key Components of Data Science
Data Collection and Storage
The first step in data science is gathering relevant data from various sources. This data is then stored in databases or data warehouses for further processing.
Data Cleaning and Preprocessing
Raw data is often messy and inconsistent. Data cleaning involves removing errors, duplicates, and irrelevant information. Preprocessing includes transforming data into a usable format.
Exploratory Data Analysis (EDA)
EDA involves visualizing and summarizing data to uncover patterns, trends, and anomalies. It helps in forming hypotheses and guiding further analysis.
Machine Learning and Predictive Modeling
Machine learning algorithms are used to build predictive models from data. These models can make predictions and decisions based on new, unseen data.
Data Visualization
Visual representations of data, such as graphs and charts, help in understanding complex information quickly. Data visualization aids in conveying insights effectively.
The Data Science Process
Problem Definition
The data science process begins with understanding the problem you want to solve and defining clear objectives.
Data Collection and Understanding
Collect relevant data and understand its context. This step is crucial as the quality of the analysis depends on the quality of the data.
Data Preparation
Clean, preprocess, and transform the data into a suitable format for analysis. This step ensures that the data is accurate and ready for modeling.
Model Building
Select appropriate algorithms and build predictive models using machine learning techniques. This step involves training and fine-tuning the models.
Model Evaluation and Deployment
Evaluate the model's performance using metrics and test datasets. If the model performs well, deploy it for making predictions on new data.
Technologies Driving Data Science
Programming Languages
Languages like Python and R are widely used in data science due to their extensive libraries and versatility.
Machine Learning Libraries
Libraries like Scikit-Learn and TensorFlow prov
This document contains an agenda for a workshop on data, information visualization, communications, and advocacy for nonprofits. The agenda includes opening remarks and then three presentations: Beth Kanter will discuss measuring the impact of networks in nonprofits; Johanna Morariu will cover data collection, assessment, learning and management; and Brian Kennedy will talk about communications and advocacy. The document provides brief summaries of each presentation and includes slides from Beth Kanter's presentation on creating a data-informed culture in nonprofits.
This document discusses data visualization and provides best practices for visualizing data. It defines data visualization as translating information into visual formats like charts and graphs to make insights and trends easier for people to understand. The document recommends finding the story in the data, cleaning and sorting it, selecting appropriate visual elements to represent it, avoiding exaggeration, and citing sources. It highlights how visuals help illustrate data creatively, uncover new insights, engage audiences, represent big data, and drive decision making. The importance of using word clouds to reveal audience thoughts in an exciting, emotional, and engaging way is also covered, along with ten examples of word cloud generating tools.
Data literacy is now a sought-after ability for many workers. To begin, leaders must be aware of data literacy and develop a common language for learning.
How to foil the three villains of data visualization - Tableau Software EditionLee Feinberg
See how the Tragedy of Tables™, the Tyranny of Pie Charts™, and the Treachery of Averages™ cause confusion and mayhem. Learn practical tactics to defeat them and become a visualization hero. Excelsior!
5 errores de la visualización de datos en Qlik ViewBSolutions Group
This document discusses 5 common pitfalls of data visualization and provides tips to avoid each one. The pitfalls are: 1) Color Abuse - using too much or inappropriate color that can confuse viewers. 2) Misuse of Pie Charts - including too much data in pie charts which obscures the big picture. 3) Visual Clutter - including unnecessary elements or too many KPIs which obscures meaning. 4) Poor Design - prioritizing aesthetics over effective communication of data. 5) Bad Data - visualizations that reveal issues with the underlying data sources. The document provides tips for each pitfall such as using color purposefully, limiting pie charts' data, keeping visuals simple, involving designers, and addressing data
The document discusses how companies can better utilize data and analytics to support decision making rather than focusing primarily on acquiring more data. It argues that most companies do not effectively use the data they already have. To leverage data, companies need to adopt evidence-based decision making as a cultural shift. This involves establishing single data sources, providing real-time feedback to decision makers, explicitly defining and updating business rules based on facts, and coaching employees who make regular decisions. Empowering employees to make decisions based on data analysis, like at Seven-Eleven Japan, can provide competitive advantages if companies learn to effectively capture, analyze, and act on data.
The document discusses analyzing data from over 1.3 million words of TEDTalk transcripts and millions of user ratings to determine characteristics of the best and worst TED talks. It finds that choosing an interesting topic is important, and that the best talks tend to be about 50% longer than the worst talks. It also notes the importance of visuals and delivery style. The document concludes by advising managers to choose topics of interest to the audience and give the best possible delivery when presenting.
Big data provides an unprecedented opportunity to predict consumer behavior through the longitudinal and cross-sectional analysis of vast time series data. However, the inherent randomness of human behavior poses a limiting factor, and while marginal gains can be made through big data, breakthroughs may remain elusive as long as human behavior stays inconsistent, impulsive, and dynamic. The biggest impact of big data will be creating new areas like personalized medicine, improved customer service, and powering artificial intelligence through vast data analysis to understand and anticipate human behavior.
Business decisions are not based on data only but an individual's risk taking ability. Sebastian Wernicke explains this characteristic with examples of Netflix, Amazon and Google.
Stop searching for that elusive data scientistAbhi Rana
Look beyond the charm of data scientist in the organisation and find easy, quick and low-cost solutions. Team collaboration and peer learning is one the best way for small firms.
This document discusses the importance of managers having a working knowledge of analytics according to Florian Zettelmeyer. It argues that managers should view analytics as integral to business processes rather than something separate and technical. Managers need to understand how data is collected and ensure it is aligned with business goals. A working knowledge allows managers to identify faulty assumptions, avoid misinterpreting results, and use analytics to generate genuine insights rather than bad decisions. Managers' domain knowledge is crucial for validating data-driven findings.
This document discusses predictive analytics and its importance for managers. It explains that predictive analysis uses customer data collected through digital marketing to predict future customer wants and needs. This allows companies to determine customer lifetime value, recommend the best products, and accurately forecast demand. The document outlines that while data scientists build predictive models, managers must communicate the meaning and implications of the analysis. It stresses that both making assumptions and checking their validity are essential parts of creating an effective predictive model.
Data is worthless if you don;t communicateAbhi Rana
This document discusses the importance of data communication for managers. It notes that while data scientists analyze data, managers must communicate insights to key stakeholders to enable effective decision making. Managers can now make decisions based on data rather than intuition. The document provides examples showing that communicating research findings can lead to adoption of ideas or creation of new businesses. It outlines a framework for communicating data that involves defining a business problem, measuring relevant variables, collecting and analyzing available data, developing initial and refined solutions, and communicating the business impact. The overall message is that data is only valuable if insights are effectively communicated.
The document discusses traits of being data-driven. It lists traits such as making decisions at lower levels, bringing in diverse data, having a deeper understanding, dealing with uncertainty, and learning from mistakes. It also discusses how data-driven companies work to drive decision making to the lowest possible level which frees up senior time for important decisions. It notes the importance of having the right organizational capabilities and taking care of data variations by focusing on the simplest process in the most controlled situation. It emphasizes recognizing variations and understanding them easily through high quality data and execution as well as having a different approach to problem solving.
Jer Thorp uses software-based art and data analysis to discover relationships between people on the internet and build historical narratives. He examines how shared content spreads from person to person in a process he calls "structuring a cascade." Thorp believes data science can honor victims of tragedy like 9/11 by showing the meaningful connections between victims. The document also discusses the value of human mobility data for understanding customer preferences and encounters with brands, but notes data should be approached from an ethical perspective that respects people.
1) The document discusses how to think like a data scientist by walking through collecting and analyzing data to answer a question about meeting start times.
2) It recommends starting with an interesting or bothersome work question, defining relevant data to collect, and then gathering that data over a period of time while modifying definitions as needed.
3) The example analyzes meeting start times over two weeks, finds that on average meetings started 12 minutes late with 10% starting on time, and discusses what else the data might reveal and whether others would believe the results.
Big data is useful in many ways. But what are they and how they impact our lives. Is it the the ultimate thing that will drive our goals in future? Let's find out. What do we do with all this big data.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
5. Data visualization is a general term that describes any
effort to help people understand the significance
of data by placing it in a visual context.
Patterns, trends and correlations that might go
undetected in text-based data can be exposed and
recognized easier with data visualization software.
13. Visuals are more effective than numbers.
Our mind comprehends
more data in form of
graphics. It is language of
eyes and enhances
experience of data reading