Those working in the field of technology hear the terms ‘Data Science’ and ‘Data Analytics’ probably all the time. These two words are often used interchangeably. Big data is a major component in the tech world today due to the actionable insights and results it offers for businesses. In order to study the data that your organization is producing, it is important to use the proper tools needed to comprehend big data to uncover the right information. To help you optimize your analytics, it is important for you to examine both the similarities and differences of data science and data analytics.
Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
When Big Data and Predictive Analytics Collide: Visual Magic HappensChase McMichael
Big data is useless data unless you have a way to handle and perform meaningful analysis that drives a business outcome. Data visualization has transformed complex data sets into patterns now being used to constructed predictive models. In the massive exploding world of social data and content engagement the need for intelligent data mining and pattern prediction is required to realize data driving marketing. In this presentation, we will explore techniques, key takeaways and examples behind this fast growing market of predictive https://svforum.org/Business-Intelligence/Business-Intelligence-SIG-When-Big-Data-and-Predictive-Analytics-Collide SEE Dreamforce Content Hub in ACTION here http://blog.infinigraph.com/example-of-visual-content-trends-powered-by-hypercuration/
Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
When Big Data and Predictive Analytics Collide: Visual Magic HappensChase McMichael
Big data is useless data unless you have a way to handle and perform meaningful analysis that drives a business outcome. Data visualization has transformed complex data sets into patterns now being used to constructed predictive models. In the massive exploding world of social data and content engagement the need for intelligent data mining and pattern prediction is required to realize data driving marketing. In this presentation, we will explore techniques, key takeaways and examples behind this fast growing market of predictive https://svforum.org/Business-Intelligence/Business-Intelligence-SIG-When-Big-Data-and-Predictive-Analytics-Collide SEE Dreamforce Content Hub in ACTION here http://blog.infinigraph.com/example-of-visual-content-trends-powered-by-hypercuration/
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This presentation briefly discusses the following topics:
Classification of Data
What is Structured Data?
What is Unstructured Data?
What is Semistructured Data?
Structured vs Unstructured Data: 5 Key Differences
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Data Science has become one of the most demanded jobs of the 21st century. It has become a buzzword that almost everyone talks about these days. But what is Data Science? In this article, we will demystify Data Science, the role of a Data Scientist and have a look at the tools required to master Data Science.
Mental Model for Exploratory Data Analysis Applications for Structured Proble...Jukka-Matti Turtiainen
The ability to solve complex problems has been identified as one of the most important skills for 2020s. Exploratory Data Analysis (EDA) can be used as part of structured problem-solving process. EDA has been described more as an art than a science.
The research objective of the master’s thesis was to develop a mental model of EDA that better describes the way it can be applied to initiate the investigation of a performance issue. The thesis presents an overview of EDA history in a literature review, from which a preliminary mental model was developed. That mental model was presented for review and critique to a team of Lean Six Sigma Master Black Belt content matter experts, who offered suggestions in face-to-face, individual interviews for further improvement of the model.
The updated mental model was presented individually to a different group of content matter experts for critique. The thesis demonstrates a practical application of the proposed EDA model through a case study, where it was applied to define the problem in a Lean Six Sigma Black Belt project. The proposed EDA mental model was well accepted by these Master Black Belt experts. Additional research topics were suggested to make the mental model more comprehensive. The proposed mental model integrates the foundational scientific thinking logic in the process of conducting Exploratory Data Analysis which brings EDA closer to science.
phd admission. phd in computer science phd thesisresearch paper formatresearch paper how to writepaper publicationpaper publication journalpaper publication journal paper publication journal paper publication in journalsresearch topics research paper topics research questions
Turning Big Data Analytics To Knowledge PowerPoint Presentation SlidesSlideTeam
This complete deck covers various topics and highlights important concepts. It has PPT slides which cater to your business needs. This complete deck presentation emphasizes Turning Big Data Analytics To Knowledge PowerPoint Presentation Slides and has templates with professional background images and relevant content. This deck consists of total of twenty two slides. Our designers have created customizable templates, keeping your convenience in mind. You can edit the colour, text and font size with ease. Not just this, you can also add or delete the content if needed. Get access to this fully editable complete presentation by clicking the download button below. http://bit.ly/2HHUsqf
How relevant is Predictive Analytics relevant today?Steven Mugerwa
This is my view on how relevant is Predictive Analytics relevant today. Although its a high level view, it gives great insights to a person who is looking for somewhere to begin. This was an essay for the
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
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Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
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
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This presentation briefly discusses the following topics:
Classification of Data
What is Structured Data?
What is Unstructured Data?
What is Semistructured Data?
Structured vs Unstructured Data: 5 Key Differences
every business needs a data analytics to get a detailed value of cost and profits. we will study the importance in detail in this particular presentation.
Data Science has become one of the most demanded jobs of the 21st century. It has become a buzzword that almost everyone talks about these days. But what is Data Science? In this article, we will demystify Data Science, the role of a Data Scientist and have a look at the tools required to master Data Science.
Mental Model for Exploratory Data Analysis Applications for Structured Proble...Jukka-Matti Turtiainen
The ability to solve complex problems has been identified as one of the most important skills for 2020s. Exploratory Data Analysis (EDA) can be used as part of structured problem-solving process. EDA has been described more as an art than a science.
The research objective of the master’s thesis was to develop a mental model of EDA that better describes the way it can be applied to initiate the investigation of a performance issue. The thesis presents an overview of EDA history in a literature review, from which a preliminary mental model was developed. That mental model was presented for review and critique to a team of Lean Six Sigma Master Black Belt content matter experts, who offered suggestions in face-to-face, individual interviews for further improvement of the model.
The updated mental model was presented individually to a different group of content matter experts for critique. The thesis demonstrates a practical application of the proposed EDA model through a case study, where it was applied to define the problem in a Lean Six Sigma Black Belt project. The proposed EDA mental model was well accepted by these Master Black Belt experts. Additional research topics were suggested to make the mental model more comprehensive. The proposed mental model integrates the foundational scientific thinking logic in the process of conducting Exploratory Data Analysis which brings EDA closer to science.
phd admission. phd in computer science phd thesisresearch paper formatresearch paper how to writepaper publicationpaper publication journalpaper publication journal paper publication journal paper publication in journalsresearch topics research paper topics research questions
Turning Big Data Analytics To Knowledge PowerPoint Presentation SlidesSlideTeam
This complete deck covers various topics and highlights important concepts. It has PPT slides which cater to your business needs. This complete deck presentation emphasizes Turning Big Data Analytics To Knowledge PowerPoint Presentation Slides and has templates with professional background images and relevant content. This deck consists of total of twenty two slides. Our designers have created customizable templates, keeping your convenience in mind. You can edit the colour, text and font size with ease. Not just this, you can also add or delete the content if needed. Get access to this fully editable complete presentation by clicking the download button below. http://bit.ly/2HHUsqf
How relevant is Predictive Analytics relevant today?Steven Mugerwa
This is my view on how relevant is Predictive Analytics relevant today. Although its a high level view, it gives great insights to a person who is looking for somewhere to begin. This was an essay for the
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
Want Free Career Counseling?
Just fill in your details, and one of our experts will call you!
Call us: +918308103366
WhatsApp Us: https://wa.me/+918308103366
Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
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
Data Science is in high demand, the melting pot
of complex skills requires a qualified data scientist have made them the unicorns in today's data-driven landscape.
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfKajal Digital
Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
Data sciences is the topnotch in our world now as it enables us to predict the future and behaviors of people and systems alike.
Hence, this course focuses on introducing the processing involved in data sciences.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
What are Entry Level Data Analyst Jobs?: A Guide Skills optnation1
Paid internships and employment training programmes that are directly related to their field of study are permitted for international students holding F-1 student visas, provided that the courses fall under the category of Optional Practical Training to their major subjects of study. You can search for remote data analyst jobs and other OPT positions in the USA with similar specialisations.
Paradigm4 Research Report: Leaving Data on the tableParadigm4
While Big Data enjoys widespread media coverage, not enough attention has been paid to what practitioners think — data scientists who manage and analyze massive volumes of data. We wanted to know, so Paradigm4 teamed up with Innovation Enterprise to ask over 100 data scientists for their help separating Big Data hype from reality. What we learned is that data scientists face multiple challenges achieving their company’s analytical aspirations. The upshot is that businesses are leaving data — and money — on the table.
what is ..how to process types and methods involved in data analysisData analysis ireland
Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
The Data Science Institute A One-stop Solution for All Your Data Science Need...The Interface™
CloudLearn ERP is one stop solutions for Data Science and SAP training, Big Data, Data Analytics Training. Cloud learn ERP is one of best Data Science training in Mumbai.
We are represented world-class Data Science training in Mumbai, Best SAP training in Mumbai, Data Science course in Mumbai, Data Analytics preparing in Mumbai, Big Data training in Mumbai, Hadoop training in Mumbai, Python training in Mumbai, Cloud Computing Training in Mumbai, AWS training in Mumbai,
Azure preparing in Mumbai, Salesforce preparing in Mumbai, SAP training in Mumbai. You can here legitimately contact for preparing in Mumbai or any data. So continue visiting our sites to get update.
Huge amount of data is being collected everywhere - when we browse the web, go to the doctor's clinic, visit the supermarket, tweet or watch a movie. This plethora of data is dealt under a new realm called Data Science. Data Science is now recognized as a highly-critical growing area with impact across many sectors including science, government, finance, health care, social networks, manufacturing, advertising, retail,
and others. This colloquium will try to provide an overview as well as clarify bits and bats about this emerging field.
Advanced Business Analytics for Actuaries - Canadian Institute of Actuaries J...Kevin Pledge
Presentation given at the Canadian Institute of Actuaries Annual Meeting in June 2013. Covers the direction business intelligence is moving in for insurance.
Similar to Data science and data analytics major similarities and distinctions (1) (20)
The 7 Key Steps To Build Your Machine Learning ModelRobert Smith
A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data.
Environmental Monitoring System using IoT, AI and MLRobert Smith
AI, ML, IoT, and Emerging Tech. Cognitive technologies like machine learning and AI (artificial intelligence) certainly have proven to be an important part of the IoT (Internet of Things) sector because they can help make products and services smarter and, therefore, more valuable.
The Key Differences Between Rule-Based AI And Machine LearningRobert Smith
While a rules-based system could be considered as having “fixed” intelligence, in contrast, a machine learning system is adaptive and attempts to simulate human intelligence. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.
Top 10 Skills You Need For A High-Paying Machine Learning CareerRobert Smith
There are vast applications of Machine Learning in computer science including different types of learning such as supervised learning, unsupervised learning, and reinforcement learning. Machine Learning can be a rewarding career for students who are good in mathematics and statistics and have sharp programming skills.
How Cyber Security Courses Opens Up Amazing Career Opportunities?Robert Smith
To become a security consultant, you might follow a career path similar to this: Earn a bachelor's degree in computer science, information technology, cyber security, or a related field. Or, gain equivalent experience with relevant industry certifications. Pursue an entry-level position in general IT or security.
5 Key Trends in Virtual Reality and Augmented Reality Careers in 2020?Robert Smith
The AR and VR market had sold 8.9 million units by the end of 2018, which is expected to grow to 65.9 million by the end of 2022. AR and VR headset sales are expected to grow to $9.7 billion in 2020. The majority of augmented reality users fall into the 16 to 34 age bracket.
How Will Chatbots Affect Customer Service?Robert Smith
AI chatbots use your existing information and resources, like FAQs or knowledge base articles, to help answer and resolve your customers' questions. They can recognize and answer multiple forms of the same question and can be trained to give instant responses using your preferred voice and tone.
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This article will teach you many of the core concepts behind neural networks and deep learning.
Learn Where Artificial Intelligence Is Used NowadaysRobert Smith
This topic explains about AI Technology. Here you how AI use in different industries and they enhance your daily revenue and change your career opportunity.
How Is IoT Technology Transforming The Agricultural Sector?Robert Smith
Basically, Smart Agricultural Technology enables farmers to have better control over the process of growing crops and rearing livestock. This way it brings massive efficiencies of scale, cuts costs, and helps in saving scarce resources, like water.
Significance Of Hadoop For Data ScienceRobert Smith
Hadoop is an important tool for data science when the volume of data exceeds the system memory or when the business case requires data to be distributed across multiple servers.
How Python Is Used In Machine LearningRobert Smith
A python is a great tool for the development of programs that perform data manipulation whereas R is statistical software that works on a particular format of the dataset. Python provides the various development tools which can be used to work with machine learning & other systems. R has a learning curve to it.
Few Chatbots Expert Interview Questions & Answer For FreshersRobert Smith
Chatbots — automated conversation systems — have become increasingly sophisticated. Should you design and deploy one that can interact with your customers? If you’re an executive making that decision right now, you may feel caught between A.I. hype on the one hand and the fear that machines might not treat your customers right on the other.
How ai transforms the marketing domain for the better Robert Smith
AI solutions provide marketers with a deeper knowledge of consumers and prospective clients, enabling them to deliver the right message, to the right person, at the right time. Marketers can use AI solutions to take these profiles a step further, refine marketing campaigns, and create highly personalized content.
How machine learning & artificial intelligence implement in e commerce Robert Smith
In this presentation, we will discuss AI and ML with specific reference to the e-commerce domain and how it helps e-commerce companies in driving their sales. AI and ML, no doubt, are adding valuable elements to e-commerce platforms to help them stay in the market.
How artificial intelligence certification help you in future to grow your selfRobert Smith
We are living in an era where machines have influenced our lives. We have seen some huge transformations in this field and one such development is Artificial Intelligence which is the simulation of human intelligence by the machines. There have been some great developments in this field which has impacted our lives.
How to become an expert in augmented reality Robert Smith
Wouldn't it be great to have a world that can superimpose an image or create a real-world experience? Well, this is not a fantasy anymore. The world is now witnessing a paradigm shift where we are witnessing the surge of new technological developments that are changing the pace of learning and growth.
How virtual reality help the students to change the way of learning Robert Smith
Technology has transformed different fields; it has brought forth new ways that have kick-started a new era of learning and development. One such transformation is Virtual Reality.
How is ai important to the future of cyber security Robert Smith
Today’s era is driven by technology in every aspect of our lives, so much that we’ve now increased our dependence on technology on a daily basis. With an increase in the dependency, we’re now very vulnerable and exposed to the intermittent threat posed as cyber-attacks. Cyber-attack threats have plagued businesses, corporates, governments, and institutions.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
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After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.