This document compares Python and R for use in data science. Both languages are popular among data scientists, though Python has broader usage among professional developers overall. Python is a general purpose language while R is specialized for statistical computing. Both have extensive libraries for data manipulation, analysis, and visualization. The best choice depends on factors like familiarity, project requirements, and team preferences as both are capable of most data science tasks.
This document discusses the programming language R and reasons for learning and using it. R is a statistical computing language that is open-source, cross-platform, and has powerful tools for data analysis, machine learning, and visualization. It has a large user community and is used by many top companies for tasks like advertising effectiveness analysis and data visualization. While R has a steep learning curve and requires more memory than some other languages, learning R provides access to cutting-edge algorithms and is valuable for mastering data science and working with large datasets. The document concludes that R offers immense benefits and tools to work with data at scale, making it a good choice for both technical fields and business applications.
This document discusses R programming and compares it to Python. R is an open-source programming language commonly used for statistical analysis and visualization. It has many libraries that enable data analysis and machine learning. The document compares key aspects of R and Python, such as their creators, release years, software environments, usability, and pros and cons. It concludes that R is easy to learn and offers powerful graphics and statistical techniques through libraries, making it well-suited for data analysis applications.
Big Data refers to a large amount of data both structured and unstructured. For managing and analyzing this amount of data we need technologies like Hadoop and language like R.
http://www.techsparks.co.in/thesis-in-big-data-with-r/
This document compares Python and R for use in data science. Python is a general purpose language while R is specifically designed for statistical analysis. Both have extensive libraries for data analysis and machine learning, though Python's libraries are more general purpose while R's focus on statistics. Python may have some performance limitations for statistical tasks compared to R, which is optimized for statistics. Both languages have large, active user communities providing support, though Python's community is larger. The best choice depends on the project's specific requirements and the user's preferences.
R was created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand to teach introductory statistics. It is an open source software environment excellent for data analysis and graphics using functions in an interpreter. R is used across many industries and can analyze both structured and unstructured data to explore datasets and build predictive models.
R is a popular programming language for statistical analysis and visualization. It allows users to import, clean, analyze, and visualize data, and is commonly used in fields like data science, machine learning, and research. The document provides an overview of R, including how to download and install it, basic usage like starting an R session and running commands, and examples of using R for tasks like data analysis, statistical computing, and machine learning. Key features of R highlighted are that it is open source, runs on various platforms, and has a large collection of packages for data handling and analysis.
Basic of R Programming Language,
Introduction, How to run R, R Sessions and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes
Basic of R Programming Language
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
Which programming language to learn R or Python - MeasureCamp XIIMaggie Petrova
This document compares the R and Python programming languages for data science and machine learning tasks. It discusses that R and Python are commonly used for artificial intelligence, machine learning, and data science. Python is currently more popular overall based on metrics like usage on Stack Overflow. The document outlines pros for each language, with R being good for statistical computing and analysis, while Python can be better integrated into web apps and production systems. It recommends starting with tools like RStudio for R and Jupyter Notebook for Python, and popular libraries for tasks like data manipulation, visualization, text analysis, time series, and machine learning. The top tips provided are to forget Excel, learn by doing projects, and leverage online communities.
Python – The Fastest Growing Programming LanguageIRJET Journal
1) Python is a widely used general-purpose programming language known for its simplicity and readability. It has seen rapid growth in recent years driven by its popularity for data science and machine learning tasks.
2) Key reasons for Python's growth include its use in academia and industries like software, manufacturing, and electronics. It is also popular due to its extensive libraries for tasks like data analysis and its job opportunities for data scientists.
3) Python supports multiple programming paradigms, has a large standard library, and can be used for web development, desktop GUIs, system scripting, and more. Its simplicity, readability, and extensive community make it a good choice for both learning and real-world programming
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document discusses using Python coding to store datasets in Hadoop databases for real-time applications. It begins by introducing big data and where Hadoop databases are used. The basic platform of Hadoop stores datasets using Java programs but Python is proposed as it is more user-friendly, efficient to code, debug and execute on all platforms. Examples are given comparing Python and Java programs. The major differences between the languages are outlined in a table. The document then discusses using Python for various real-world projects and platforms before concluding Python is better suited than Java for big data applications.
The document compares Python and R for use in data science. Python is a general purpose language that emphasizes readable code, making it well-suited for deep learning, model deployment, and integration with other software. R was designed by statisticians for statistical modeling and analysis and has strong visualization capabilities. Both have large communities and ecosystems for data science work, though Python has advantages for deep learning while R performs better for statistical modeling and dashboards. Languages also borrow ideas from each other, like Python adopting plotting styles from R and R using web scraping techniques from Python.
Is r or python better for data journalism projects hari sandeep reddyconfidential
R and Python are both popular languages for data journalism projects, but Python may be better for beginners. While R excels at statistical analysis and visualization, Python has broader functionality for tasks like web scraping and is more of a general purpose language. In the long run, learning both R and Python is recommended for data journalism since the field requires both data analysis and reporting skills.
R can perform various data analysis and data science tasks for free through its extensive packages and community support. It is an open-source statistical programming language that is widely used for data manipulation, visualization, and machine learning. Some key features of R include its ability to perform interactive visualization, ensemble learning, text/social media mining, and integration with other languages and technologies like SQL, Python, and Tableau. While powerful, R does have some limitations like a steep learning curve and slower execution compared to other languages.
Python is widely used for data science and analysis tasks. Approximately 40% of data scientists use Python in their daily work to collect, analyze, and report data from various sources. Large organizations such as NASA, Google, and CERN also rely heavily on Python for programming tasks. Python has become popular for data science due to its many prebuilt libraries for tasks like data manipulation and visualization, its simplicity, extensive community support, growing popularity, and platform independence.
R vs SPSS: Which One is The Best Statistical LanguageStat Analytica
This document compares the statistical programming languages R and SPSS. It discusses that R is an open-source language developed for statistical analysis, while SPSS is a commercial product focused on social science data. Some key differences covered include that R has faster updates but a less user-friendly interface than SPSS. R also has better data visualization, lower costs, and larger community support than SPSS. Overall, the document provides an overview of the capabilities and differences between the two statistical languages.
This document provides an introduction to R, including:
- R is a software environment for data manipulation, statistical computing, and graphical data analysis. It is widely used in academia, healthcare, finance, and by large companies.
- R has two originators from New Zealand and Canada. It is developed by the R Core Team and has over 13,000 contributed packages.
- Examples of how companies like Google, Facebook, banks, John Deere, the New York Times, and Ford use R for tasks like data analysis, visualization, forecasting, and statistical modeling.
Introduction to Spark R with R studio - Mr. Pragith Sigmoid
R is a programming language and software environment for statistical computing and graphics.
The R language is widely used among statisticians and data miners for developing statistical
software and data analysis.
RStudio IDE is a powerful and productive user interface for R.
It’s free and open source, and available on Windows, Mac, and Linux.
R Vs Python – The most trending debate of aspiring Data Scientistsabhishekdf3
Now, it’s the time for a battle of two most demanding programming languages that is R vs Python. We will go deep in understanding the differences between the two languages. And, I assure you that you will not have any confusion left after completing this article i.e. R vs Python – the most trending debate of aspiring data scientists.
Learn more at :- https://data-flair.training/
Pink Apps for Everyone: Introducing LiveGridLetsConnect
LiveGrid is a Connections Pink component in development that will empower anyone in your organization to build applications. Not a programmer? No problem! With LiveGrid, application building can be done with no code at all! Build an application for yourself, or share it with your colleagues. Love to code? LiveGrid gives you brand new ways to define data, add logic, and UI. Plus, each application generates a series of APIs to extend your application further. Join this session to learn how LiveGrid brings the power of Pink to situational applications!
This document provides a summary of the history of Revolution Analytics from 2007 to 2014. It discusses key events and milestones such as:
- The founding of Revolution Analytics in 2007.
- The launch of their Revolutions Blog in 2008 and growth in popularity of R.
- Releases of new versions of Revolution R Enterprise in 2009 and 2010, along with growth in the number of R user groups.
- Head to head performance comparisons of Revolution R against SAS in 2010 and 2011 that demonstrated Revolution R's reduced total cost of ownership.
- The release of RHadoop in 2011 to support analytics on Hadoop and databases.
- Recognition as a visionary in Gartner's Magic
GNU R in Clinical Research and Evidence-Based MedicineAdrian Olszewski
Is GNU R (an environment for statistical computing) suitable enough for Biostatisticians involved in Clinical Research? Can it replace or support SAS in this area? Well, I think this presentation may help to remove any doubts. If you are a Biostatistician (and probably a SAS user), you may find it useful.
The presentation is under constant improvement.
You can find it also on CRAN (contributed documentation) and at http://www.r-clinical-research.com
Data Science Amsterdam - Massively Parallel Processing with Procedural LanguagesIan Huston
The goal of in-database analytics is to bring the calculations to the data, reducing transport costs and I/O bottlenecks. With Procedural Languages such as PL/Python and PL/R data parallel queries can be run across terabytes of data using not only pure SQL but also familiar Python and R packages. The Pivotal Data Science team have used this technique to create fraud behaviour models for each individual user in a large corporate network, to understand interception rates at customs checkpoints by accelerating natural language processing of package descriptions and to reduce customer churn by building a sentiment model using customer call centre records.
http://www.meetup.com/Data-Science-Amsterdam/events/178974942/
What Is The Future of Data Science With Python?SofiaCarter4
Wondering what's the future of Data Science with Python language and why it is being used widely? Here is a complete blog. https://bit.ly/3Z6ARXj
6 Methods to Improve Your Manufacturing Process with Computer VisionGramener
Computer vision is a technology that enables computers to interpret and comprehend visual information from their surroundings, and it has the potential to transform the manufacturing industry. Manufacturers can improve their processes in a variety of ways by using computer vision, from ensuring quality control and optimizing production to inspecting and measuring products and monitoring machinery.
In this presentation you will find out 6 methods how you can improve your manufacturing process with computer vision.
Download our E-book
bit.ly/ebookcomputervision
Detecting Manufacturing Defects with Computer VisionGramener
Computer vision is the field of artificial intelligence that deals with the ability of computers to interpret and understand visual data from the world around them. In the manufacturing industry, computer vision can be used to detect defects in products as they are being produced. This can help to improve the quality of the final product and reduce the cost of rework or recalls.
In this presentation you will find out the use of computer vision for defect detection in manufacturing which aids in improving the efficiency and effectiveness of the production process, leading to higher quality products and lower costs.
Book a discovery call
https://reachus.gramener.com/damage-detection/
R is a popular programming language for statistical analysis and visualization. It allows users to import, clean, analyze, and visualize data, and is commonly used in fields like data science, machine learning, and research. The document provides an overview of R, including how to download and install it, basic usage like starting an R session and running commands, and examples of using R for tasks like data analysis, statistical computing, and machine learning. Key features of R highlighted are that it is open source, runs on various platforms, and has a large collection of packages for data handling and analysis.
Basic of R Programming Language,
Introduction, How to run R, R Sessions and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes
Basic of R Programming Language
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
Which programming language to learn R or Python - MeasureCamp XIIMaggie Petrova
This document compares the R and Python programming languages for data science and machine learning tasks. It discusses that R and Python are commonly used for artificial intelligence, machine learning, and data science. Python is currently more popular overall based on metrics like usage on Stack Overflow. The document outlines pros for each language, with R being good for statistical computing and analysis, while Python can be better integrated into web apps and production systems. It recommends starting with tools like RStudio for R and Jupyter Notebook for Python, and popular libraries for tasks like data manipulation, visualization, text analysis, time series, and machine learning. The top tips provided are to forget Excel, learn by doing projects, and leverage online communities.
Python – The Fastest Growing Programming LanguageIRJET Journal
1) Python is a widely used general-purpose programming language known for its simplicity and readability. It has seen rapid growth in recent years driven by its popularity for data science and machine learning tasks.
2) Key reasons for Python's growth include its use in academia and industries like software, manufacturing, and electronics. It is also popular due to its extensive libraries for tasks like data analysis and its job opportunities for data scientists.
3) Python supports multiple programming paradigms, has a large standard library, and can be used for web development, desktop GUIs, system scripting, and more. Its simplicity, readability, and extensive community make it a good choice for both learning and real-world programming
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document discusses using Python coding to store datasets in Hadoop databases for real-time applications. It begins by introducing big data and where Hadoop databases are used. The basic platform of Hadoop stores datasets using Java programs but Python is proposed as it is more user-friendly, efficient to code, debug and execute on all platforms. Examples are given comparing Python and Java programs. The major differences between the languages are outlined in a table. The document then discusses using Python for various real-world projects and platforms before concluding Python is better suited than Java for big data applications.
The document compares Python and R for use in data science. Python is a general purpose language that emphasizes readable code, making it well-suited for deep learning, model deployment, and integration with other software. R was designed by statisticians for statistical modeling and analysis and has strong visualization capabilities. Both have large communities and ecosystems for data science work, though Python has advantages for deep learning while R performs better for statistical modeling and dashboards. Languages also borrow ideas from each other, like Python adopting plotting styles from R and R using web scraping techniques from Python.
Is r or python better for data journalism projects hari sandeep reddyconfidential
R and Python are both popular languages for data journalism projects, but Python may be better for beginners. While R excels at statistical analysis and visualization, Python has broader functionality for tasks like web scraping and is more of a general purpose language. In the long run, learning both R and Python is recommended for data journalism since the field requires both data analysis and reporting skills.
R can perform various data analysis and data science tasks for free through its extensive packages and community support. It is an open-source statistical programming language that is widely used for data manipulation, visualization, and machine learning. Some key features of R include its ability to perform interactive visualization, ensemble learning, text/social media mining, and integration with other languages and technologies like SQL, Python, and Tableau. While powerful, R does have some limitations like a steep learning curve and slower execution compared to other languages.
Python is widely used for data science and analysis tasks. Approximately 40% of data scientists use Python in their daily work to collect, analyze, and report data from various sources. Large organizations such as NASA, Google, and CERN also rely heavily on Python for programming tasks. Python has become popular for data science due to its many prebuilt libraries for tasks like data manipulation and visualization, its simplicity, extensive community support, growing popularity, and platform independence.
R vs SPSS: Which One is The Best Statistical LanguageStat Analytica
This document compares the statistical programming languages R and SPSS. It discusses that R is an open-source language developed for statistical analysis, while SPSS is a commercial product focused on social science data. Some key differences covered include that R has faster updates but a less user-friendly interface than SPSS. R also has better data visualization, lower costs, and larger community support than SPSS. Overall, the document provides an overview of the capabilities and differences between the two statistical languages.
This document provides an introduction to R, including:
- R is a software environment for data manipulation, statistical computing, and graphical data analysis. It is widely used in academia, healthcare, finance, and by large companies.
- R has two originators from New Zealand and Canada. It is developed by the R Core Team and has over 13,000 contributed packages.
- Examples of how companies like Google, Facebook, banks, John Deere, the New York Times, and Ford use R for tasks like data analysis, visualization, forecasting, and statistical modeling.
Introduction to Spark R with R studio - Mr. Pragith Sigmoid
R is a programming language and software environment for statistical computing and graphics.
The R language is widely used among statisticians and data miners for developing statistical
software and data analysis.
RStudio IDE is a powerful and productive user interface for R.
It’s free and open source, and available on Windows, Mac, and Linux.
R Vs Python – The most trending debate of aspiring Data Scientistsabhishekdf3
Now, it’s the time for a battle of two most demanding programming languages that is R vs Python. We will go deep in understanding the differences between the two languages. And, I assure you that you will not have any confusion left after completing this article i.e. R vs Python – the most trending debate of aspiring data scientists.
Learn more at :- https://data-flair.training/
Pink Apps for Everyone: Introducing LiveGridLetsConnect
LiveGrid is a Connections Pink component in development that will empower anyone in your organization to build applications. Not a programmer? No problem! With LiveGrid, application building can be done with no code at all! Build an application for yourself, or share it with your colleagues. Love to code? LiveGrid gives you brand new ways to define data, add logic, and UI. Plus, each application generates a series of APIs to extend your application further. Join this session to learn how LiveGrid brings the power of Pink to situational applications!
This document provides a summary of the history of Revolution Analytics from 2007 to 2014. It discusses key events and milestones such as:
- The founding of Revolution Analytics in 2007.
- The launch of their Revolutions Blog in 2008 and growth in popularity of R.
- Releases of new versions of Revolution R Enterprise in 2009 and 2010, along with growth in the number of R user groups.
- Head to head performance comparisons of Revolution R against SAS in 2010 and 2011 that demonstrated Revolution R's reduced total cost of ownership.
- The release of RHadoop in 2011 to support analytics on Hadoop and databases.
- Recognition as a visionary in Gartner's Magic
GNU R in Clinical Research and Evidence-Based MedicineAdrian Olszewski
Is GNU R (an environment for statistical computing) suitable enough for Biostatisticians involved in Clinical Research? Can it replace or support SAS in this area? Well, I think this presentation may help to remove any doubts. If you are a Biostatistician (and probably a SAS user), you may find it useful.
The presentation is under constant improvement.
You can find it also on CRAN (contributed documentation) and at http://www.r-clinical-research.com
Data Science Amsterdam - Massively Parallel Processing with Procedural LanguagesIan Huston
The goal of in-database analytics is to bring the calculations to the data, reducing transport costs and I/O bottlenecks. With Procedural Languages such as PL/Python and PL/R data parallel queries can be run across terabytes of data using not only pure SQL but also familiar Python and R packages. The Pivotal Data Science team have used this technique to create fraud behaviour models for each individual user in a large corporate network, to understand interception rates at customs checkpoints by accelerating natural language processing of package descriptions and to reduce customer churn by building a sentiment model using customer call centre records.
http://www.meetup.com/Data-Science-Amsterdam/events/178974942/
What Is The Future of Data Science With Python?SofiaCarter4
Wondering what's the future of Data Science with Python language and why it is being used widely? Here is a complete blog. https://bit.ly/3Z6ARXj
6 Methods to Improve Your Manufacturing Process with Computer VisionGramener
Computer vision is a technology that enables computers to interpret and comprehend visual information from their surroundings, and it has the potential to transform the manufacturing industry. Manufacturers can improve their processes in a variety of ways by using computer vision, from ensuring quality control and optimizing production to inspecting and measuring products and monitoring machinery.
In this presentation you will find out 6 methods how you can improve your manufacturing process with computer vision.
Download our E-book
bit.ly/ebookcomputervision
Detecting Manufacturing Defects with Computer VisionGramener
Computer vision is the field of artificial intelligence that deals with the ability of computers to interpret and understand visual data from the world around them. In the manufacturing industry, computer vision can be used to detect defects in products as they are being produced. This can help to improve the quality of the final product and reduce the cost of rework or recalls.
In this presentation you will find out the use of computer vision for defect detection in manufacturing which aids in improving the efficiency and effectiveness of the production process, leading to higher quality products and lower costs.
Book a discovery call
https://reachus.gramener.com/damage-detection/
How to Identify the Right Key Opinion Leaders (KOLs) in Pharma & HealthcareGramener
Find out the importance of KOLs (Key Opinion leaders) in the Pharma industry and everything you need to know about them.
In the presentation, we will show you who is a KOL in the Pharmaceutical Industry, what role they play and how to identify the right KOLs.
Book a free demo
https://gramener.com/demorequest/
Automated Barcode Generation System in ManufacturingGramener
The document discusses how a leading semiconductor company was facing issues with validating product labels from multiple suppliers due to different labeling standards. They solved this by using a low-code barcode labeling solution called BarGen, which enables centralized standards and reduces validation time by 67%. BarGen allows for smart conversion of user inputs to barcodes via APIs and can generate barcodes in common formats for web, Excel, and bulk printing across operating systems and languages.
The Role of Technology to Save BiodiversityGramener
Find out what are the major challenges biodiversity is facing such as deforestation, species endangerment, and poaching.
In the presentation, we will show you how some of the major technology and nature conservation organizations are building innovative solutions to protect our biodiversity.
Download this E-book to know how geospatial AI is impacting biodiversity conservation and sustainable development.
https://info.gramener.com/geospatial-analytics-ai-solutions-esg-sector-ebook
Enable Storytelling with Power BI & Comicgen PluginGramener
The document summarizes a webinar about Comicgen, a Power BI plug-in that generates comic strips from data insights. It introduces Comicgen's features like controlling character emotions and poses based on two KPIs. The webinar agenda covers an introduction to data comics, what Comicgen is, how to generate comics, different use cases, and data storytelling. Future enhancements are also discussed, such as adding conversation between two characters, new Sherlock Holmes and Watson characters, improved performance, and customized comics with client CEO/CFO faces.
The Most Effective Method For Selecting Data Science ProjectsGramener
Ganes Kesari, Gramener's Head of Analytics & Co-Founder gives his insights on how to craft a data science roadmap that maximizes ROI.
The biggest reason why 80% of analytics projects fail is that they don’t solve the right problem. Asking analytics or data-related question is the worst way to initiate a data analytics project.
This webinar will walk you through how to get started in the most efficient way possible. You'll discover a straightforward step-by-step strategy to unlocking corporate value through industry examples.
Things you will learn from this webinar:
-The most common reasons for the failure of data science initiatives
-Identifying projects and prioritizing them
-Building a data science strategy in three easy steps
-Real-life examples are used to explain the approach
Watch this full webinar on: https://info.gramener.com/data-science-roadmap
To know more from our industry experts book a free demo at: https://gramener.com/demorequest/
Low Code Platform To Build Data & AI ProductsGramener
Gramener's CEO, Anand S conducted this webinar where he explained how to build Data and AI products using a low-code platform in less than two weeks.
Few takeaways:
-How low-code approaches can be tailored to your data/digital needs?
-Decisions on Building vs. Buying
-Production-ready use cases to stimulate your thinking
Who should watch?
You will find this webinar to be valuable if you're a CPO, VP IT, handling product development, or building analytical solutions for your company.
Watch this full webinar on: https://info.gramener.com/low-code-platform-to-build-process-optimization-solutions?
Want to know more about our low-code platform, Gramex?
Visit: https://gramener.com/gramex/
5 Key Foundations To Build An Effective CX ProgramGramener
Gramener's VP of Analytics Amit Garg hosted this webinar and talked about what are the principles of a good customer experience program, and why is it important.
This webinar will be beneficial to leaders in the CMO, CCO, Customer Service, and any other customer-facing departments within a firm.
Pain points discussed:
-You'll be able to assess the level of CX maturity in your company.
-You'll learn the high-level steps to creating a successful CX program.
-You'll figure out what tools you'll need to improve your talents.
To watch the full webinar visit: https://info.gramener.com/5-key-foundations-effective-cx-program
Learn more about CX Analytics: https://gramener.com/customer-experience-analytics/
Using Power BI To Improve Media Buying & Ad PerformanceGramener
This document discusses using Power BI to optimize media buying and ad performance. It introduces Power BI and its capabilities to provide a centralized campaign reporting platform. Media buying involves complex decisions around strategy, budget, objectives, and target markets. An ideal solution would provide a single product with user access control, an overview of spends and campaigns, detailed views of campaigns, and comparisons across campaigns. The demo then shows Power BI's flexibility, visual analytics, and data storytelling capabilities to evaluate campaign performance through live operational dashboards.
This webinar was hosted by Gramener's CEO/Co-Founder, Anand S, and Ganes Kesari, Head of Analytics/Co-Founder on how data can help firms recover quickly throughout the recession and recovery period.
Who should watch this webinar :
Analytics Leaders, Business Leaders, CDOs, CTOs, etc.
Few takeaways :
-Which aspects of your company could benefit the most from a data-driven response?
-A strategy for identifying use cases that will provide the most value for the money.
How to use data in creative ways to uncover new market opportunities and customers.
Objectives :
-Data's utility in COVID situation
-How data science may assist you in navigating the recession
-Gramener's industry case studies to assist businesses in responding to COVID-19
Full Webinar: https://info.gramener.com/recession-proofing-your-business-with-data
To know more from industry leaders visit our official website: https://gramener.com/
Engage Your Audience With PowerPoint Decks: WebinarGramener
Gramener's CEO and Co-Founder Anand S hosted a webinar on how interactive PowerPoint decks can engage your audiences.
Pain points discussed in this webinar :
-How to utilize interactive slides to answer business questions like "Where is the problem?" and "What created this problem?"
-What forms of interactivity does PowerPoint offer, and when should you utilize each?
-What tools and plug-ins can aid in the creation of interactive presentations?
Watch the full webinar on: https://info.gramener.com/interactive-powerpoint-for-operations
Book a free demo to know more about Gramener's solutions: https://gramener.com/demorequest/
Structure Your Data Science Teams For Best OutcomesGramener
Gramener's Head of Analytics, Ganes Kesari conducted this webinar and discussed the following points :
-Why do data analytics and visualization initiatives require teams to work in silos?
-What are the best organizational structures for data science?
-As your data journey progresses, how should the organizational structure evolve?
-Best methods for encouraging team collaboration in data projects
This is a unique webinar designed for Executives, Chief Analytics Officers, Heads of Analytics, Directors, Technology Leaders, and Managers that work with data science teams on a daily basis.
To check out the full webinar visit: https://info.gramener.com/data-science-teams-structure-for-best-outcomes
To contact us & book a free demo visit: https://gramener.com/demorequest/
Gramener's Lead Data Scientist Soumya Ranjan and Senior Data Science Engineer Sumedh Ghatage conducted a webinar on Geospatial AI.
In this webinar, they discussed the technical know-how to get started, as well as some strategies for navigating this fascinating realm of Geospatial Analytics.
Pain points covered :
-How to begin with Geospatial Analytics in Python
-How can large-scale geospatial datasets be cleaned and analyzed?
-What is the best way to design geospatial workflows?
-How to use Geospatial Datasets for Deep Learning?
No matter whatever industry you're in, Geospatial Analytics will provide you with a wealth of unique solutions.
To watch the full webinar visit: https://info.gramener.com/geospatial-ai-technical-sneak-peek
To know more about Gramener's Geospatial AI solutions book a free demo on: https://gramener.com/demorequest/
5 Steps To Become A Data-Driven Organization : WebinarGramener
Gramener's Chief Data Scientist and Co-founder Ganes Kesari conducted an interesting webinar that will give you an idea of how to analyze your data maturity and plan the five steps to transforming your business using data.
Who should watch this webinar?
Executives, Chief Data/Analytics Officers, Technology leaders, Business heads, Directors, and Managers.
Important points discussed on the webinar:
-The majority of businesses reach a halt in the middle of their data journey.
-According to Gartner, approximately 87% of companies in the business have a poor degree of data maturity (levels 1 and 2 on a scale of 5).
-Adding more data science projects to your portfolio will not boost your talents or results. The truth is that CDOs' primary issues are divided into five categories.
Learnings from this webinar:
-Data Science Maturity. What is it and why is it important?
-How can you determine the maturity of data science and its limitations?
-How does data science maturity (described with an example) assist your business in progressing?
Watch the full webinar on:
https://info.gramener.com/5-steps-to-transform-into-data-driven-organization
To know more about Data Maturity visit:
https://gramener.com/data-maturity/#
5 Steps To Measure ROI On Your Data Science Initiatives - WebinarGramener
1. Measuring ROI from data science initiatives is challenging for many organizations as the outcomes are often not clearly defined, quantified, or attributed to the initiatives. Breaking the chain from data to insights to actions to outcomes is common.
2. A framework is presented for quantifying the value of data science initiatives using 5 steps - define success metrics, measure the metrics, attribute outcomes to causal factors, calculate net costs and benefits to determine breakeven, and benchmark results.
3. The framework is applied to a case study of a beverage manufacturer that used analytics to optimize plant costs. Key metrics like cost savings, employee productivity, and process efficiency were defined and attribution methods like A/B testing were used
Saving Lives with Geospatial AI - Pycon Indonesia 2020Gramener
This document discusses how geospatial AI can help save lives by more precisely identifying locations to release Wolbachia-infected mosquitoes. Wolbachia bacteria can suppress mosquito-borne diseases like dengue and chikungunya by infecting mosquitoes. However, identifying exact release locations at a micro-scale (50-100m radius) is challenging. The author's company helped the World Mosquito Program address this by using building footprint data to more accurately distribute population data at a 100m grid level, reducing identification time from 3 weeks to 2 hours with higher accuracy. This approach is now being implemented in 10 countries to more efficiently roll out Wolbachia-infected mosquito releases.
Driving Transformation in Industries with Artificial Intelligence (AI)Gramener
This document discusses artificial intelligence (AI) and its impact across industries. It covers why AI is important, how it is affecting industry landscapes and shaping the global economy. It examines where we are today with AI and related technologies like the Internet of Things, big data, cloud computing and robotics. It also explores what AI is, the different elements and types of AI, and how machine learning and deep learning work. Finally, it discusses the impact of AI on various industries and some of the ethical challenges of AI.
The Art of Storytelling Using Data ScienceGramener
Gramener's VP - Sales, APAC Region, Vijayam Sirikonda interacted with the students of IIM Raipur and talked about the importance of data storytelling for business users.
Storyfying your Data: How to go from Data to Insights to StoriesGramener
Gramener's Director - Client success, Shravan Kumar A, delivered an online session to the students of Praxis Business School.
In his session he talked about how converting data into stories can benefit businesses and enable quick decision making. Furthermore, he shared approaches to create data stories along with some use cases and case studies we solved at Gramener to benefit our clients.
Check out our initiative to teach data storytelling to data scientists and analysts so that they can think out of the box and create wonderful data stories for their stakeholders: https://gramener.com/data-storytelling-workshop
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AgentExchange is Salesforce’s latest innovation, expanding upon the foundation of AppExchange by offering a centralized marketplace for AI-powered digital labor. Designed for Agentblazers, developers, and Salesforce admins, this platform enables the rapid development and deployment of AI agents across industries.
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As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
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eady to harness the power of Grafana for your HackUPC project? This session provides a rapid introduction to the core concepts you need to get started. We'll cover Grafana fundamentals and guide you through the initial steps of building both compelling dashboards and your very first Grafana app. Equip yourself with the essential tools to visualize your data and bring your innovative ideas to life!
Who Watches the Watchmen (SciFiDevCon 2025)Allon Mureinik
Tests, especially unit tests, are the developers’ superheroes. They allow us to mess around with our code and keep us safe.
We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved?
Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security.
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6. 6
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SUPPORT
LINK
7. 7
THIS HAS THE BENEFITS OF BOTH R & PYTHON
But the biggest benefit is…
If someone has created an R
script, integrate it.
Don’t argue with them about
changing languages.
Other libraries
rpy2, PypeR, pyRserve
R for Statistical Analysis Python for Deep Learning
R for exploratory analysis Python for deploying analytics
R for reproducing scientific papers Python for solving problems roughly
12. 12
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13. USE AVAILABLE SKILLS & LIBRARIES
INTEGRATE, DON’T RE-WRITE
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EXPLORE AT GITHUB.COM / GRAMENER/GRAMEX
IDEATE AS A GROUP. TWEET: #RPY / #JSPY
S.ANAND @ GRAMENER.COM
TALK TO ANYONE FROM GRAMENER