Managing your inbox is easy, right?
Your employees know how to communicate, right?
Your contacts are responsive, never lost emails, don’t have excuses, right?
See the example of email management strategy and processes.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
A tremendous backlog of predictive modeling problems in the industry and short supply of trained data scientists have spiked interest in automation over the last few years. A new academic field, AutoML, has emerged. However, there is a significant gap between the topics that are academically interesting and automation capabilities that are necessary to solve real-world industrial problems end-to-end. An even greater challenge is enabling a non-expert to build a robust and trustworthy AI solution for their company. In this talk, we’ll discuss what an industry-grade AutoML system consists of and the scientific and engineering challenges of building it.
What is a Database?
Database creation steps
Benefits of using Database
Types of Table Relationships
What is a Database model
Database Management System
Users of Database
MS Access
Relational databases have pretty much ruled over the IT world for the last 30 years. However, Web 2.0 and the incipient Internet of Things (IoT) are some of the sources of a data explosion that has proved to exceed the limits of what modern relational databases can handle in a growing number of cases. As a result, new technologies had to be developed to handle these new use cases. We generally group these technologies under the umbrella of Big Data. In this two part presentation, we will start by understanding how relational databases have evolved to become the powerhouses they are today. In part 2 we will look at how non SQL databases are tackling the big data problem to scale beyond what relational databases can provide us today.
Migrate your Data Warehouse to Amazon Redshift - September Webinar SeriesAmazon Web Services
You can gain substantially more business insights and save costs by migrating your on-premise data warehouse to Amazon Redshift, a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. This webinar will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process.
Learning Objectives:
• Understand how Amazon Redshift can deliver a richer, faster analytics at much lower costs.
• Learn key factors to consider before migrating and how to put together a migration plan.
• Learn best practices and tools for migrating schema, data, ETL and SQL queries.
Slides from lecture style tutorial on data quality for ML delivered at SIGKDD 2021.
The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Data remains susceptible to errors or irregularities that may be introduced during collection, aggregation or annotation stage. This necessitates profiling and assessment of data to understand its suitability for machine learning tasks and failure to do so can result in inaccurate analytics and unreliable decisions. While researchers and practitioners have focused on improving the quality of models (such as neural architecture search and automated feature selection), there are limited efforts towards improving the data quality.
Assessing the quality of the data across intelligently designed metrics and developing corresponding transformation operations to address the quality gaps helps to reduce the effort of a data scientist for iterative debugging of the ML pipeline to improve model performance. This tutorial highlights the importance of analysing data quality in terms of its value for machine learning applications. Finding the data quality issues in data helps different personas like data stewards, data scientists, subject matter experts, or machine learning scientists to get relevant data insights and take remedial actions to rectify any issue. This tutorial surveys all the important data quality related approaches for structured, unstructured and spatio-temporal domains discussed in literature, focusing on the intuition behind them, highlighting their strengths and similarities, and illustrates their applicability to real-world problems.
** Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Machine Learning PPT on "Complete Machine Learning Course" will provide you with detailed and comprehensive knowledge of Machine Learning. It will provide you with the in-depth knowledge of the different types of Machine Learning with the different algorithms that lie under each category with a demo for each algorithm and the approach one should take to solve these problems. This PPT will be covering the following topics:
What is Data Science?
Data Science Peripherals
What is Machine learning?
Features of Machine Learning
How it works?
Applications of Machine Learning
Market Trend of Machine Learning
Machine Learning Life Cycle
Important Python Libraries
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Detailed Supervised Learning
Supervised Learning Algorithms
Linear Regression
Use Case(with Demo)
Model Fitting
Need for Logistic Regression
What is Logistic Regression?
What is Decision Tree?
What is Random Forest?
What is Naïve Bayes?
Detailed Unsupervised Learning
What is Clustering?
Types of Clustering
Market Basket Analysis
Association Rule Mining
Example
Apriori Algorithm
Detailed Reinforcement Learning
Reward Maximization
The Epsilon Greedy Algorithm
Markov Decision Process
Q-Learning
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This Edureka k-means clustering algorithm tutorial will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/3aseSs
Introducing GTD®
* “If my mind had a mind, I wouldn’t need
a system.” – David Allen
* GTD® is the popular shorthand for
Getting Things Done®
* “…a powerful method to manage
commitments, information, and
communication.”
A tremendous backlog of predictive modeling problems in the industry and short supply of trained data scientists have spiked interest in automation over the last few years. A new academic field, AutoML, has emerged. However, there is a significant gap between the topics that are academically interesting and automation capabilities that are necessary to solve real-world industrial problems end-to-end. An even greater challenge is enabling a non-expert to build a robust and trustworthy AI solution for their company. In this talk, we’ll discuss what an industry-grade AutoML system consists of and the scientific and engineering challenges of building it.
What is a Database?
Database creation steps
Benefits of using Database
Types of Table Relationships
What is a Database model
Database Management System
Users of Database
MS Access
Relational databases have pretty much ruled over the IT world for the last 30 years. However, Web 2.0 and the incipient Internet of Things (IoT) are some of the sources of a data explosion that has proved to exceed the limits of what modern relational databases can handle in a growing number of cases. As a result, new technologies had to be developed to handle these new use cases. We generally group these technologies under the umbrella of Big Data. In this two part presentation, we will start by understanding how relational databases have evolved to become the powerhouses they are today. In part 2 we will look at how non SQL databases are tackling the big data problem to scale beyond what relational databases can provide us today.
Migrate your Data Warehouse to Amazon Redshift - September Webinar SeriesAmazon Web Services
You can gain substantially more business insights and save costs by migrating your on-premise data warehouse to Amazon Redshift, a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. This webinar will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process.
Learning Objectives:
• Understand how Amazon Redshift can deliver a richer, faster analytics at much lower costs.
• Learn key factors to consider before migrating and how to put together a migration plan.
• Learn best practices and tools for migrating schema, data, ETL and SQL queries.
Slides from lecture style tutorial on data quality for ML delivered at SIGKDD 2021.
The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Data remains susceptible to errors or irregularities that may be introduced during collection, aggregation or annotation stage. This necessitates profiling and assessment of data to understand its suitability for machine learning tasks and failure to do so can result in inaccurate analytics and unreliable decisions. While researchers and practitioners have focused on improving the quality of models (such as neural architecture search and automated feature selection), there are limited efforts towards improving the data quality.
Assessing the quality of the data across intelligently designed metrics and developing corresponding transformation operations to address the quality gaps helps to reduce the effort of a data scientist for iterative debugging of the ML pipeline to improve model performance. This tutorial highlights the importance of analysing data quality in terms of its value for machine learning applications. Finding the data quality issues in data helps different personas like data stewards, data scientists, subject matter experts, or machine learning scientists to get relevant data insights and take remedial actions to rectify any issue. This tutorial surveys all the important data quality related approaches for structured, unstructured and spatio-temporal domains discussed in literature, focusing on the intuition behind them, highlighting their strengths and similarities, and illustrates their applicability to real-world problems.
** Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Machine Learning PPT on "Complete Machine Learning Course" will provide you with detailed and comprehensive knowledge of Machine Learning. It will provide you with the in-depth knowledge of the different types of Machine Learning with the different algorithms that lie under each category with a demo for each algorithm and the approach one should take to solve these problems. This PPT will be covering the following topics:
What is Data Science?
Data Science Peripherals
What is Machine learning?
Features of Machine Learning
How it works?
Applications of Machine Learning
Market Trend of Machine Learning
Machine Learning Life Cycle
Important Python Libraries
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Detailed Supervised Learning
Supervised Learning Algorithms
Linear Regression
Use Case(with Demo)
Model Fitting
Need for Logistic Regression
What is Logistic Regression?
What is Decision Tree?
What is Random Forest?
What is Naïve Bayes?
Detailed Unsupervised Learning
What is Clustering?
Types of Clustering
Market Basket Analysis
Association Rule Mining
Example
Apriori Algorithm
Detailed Reinforcement Learning
Reward Maximization
The Epsilon Greedy Algorithm
Markov Decision Process
Q-Learning
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This Edureka k-means clustering algorithm tutorial will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/3aseSs
Introducing GTD®
* “If my mind had a mind, I wouldn’t need
a system.” – David Allen
* GTD® is the popular shorthand for
Getting Things Done®
* “…a powerful method to manage
commitments, information, and
communication.”
Cities are fonts of ideas, opportunity, art and political movements. But urban enclaves can also generate inequality, epidemics and pollution. The rapid pace of urbanization in the coming decades brings these and other unprecedented opportunities and challenges to the fore. Will cities lose their vibrant potential if the challenges they face spiral out of control?
TEDx video - http://bit.ly/TEDxp - Persuasive Cities for Sustainable Wellbein...Agnis Stibe
Watch TEDx Talk: http://bit.ly/TEDxp
Watch TEDx Interview: http://bit.ly/TEDxi
Can you imagine a city that feels, understands, and cares about your wellbeing? Many of us live and work in an urban environment, however we often are not aware of how hugely our behavior is influenced by the environment.
Future cities will alter human behavior in countless ways and Socially Influencing Systems (SIS) will play an important role in making urban spaces more livable and resource-efficient by addressing current environmental problems and enabling healthier routines.
This talk will focus on discussing ways for reshaping our current environments and designing future Persuasive Cities to help people become healthier and to acquire sustainable lifestyles.
I have presented the following topics during the conference.
Next Generation of Organizational Training and Succession Strategies
Organizational Training and Leaders Succession Planning Strategies Development
Who’s Preparing For the Leadership Gap
What are the Differences: Organizational Training and Empowerment vs. Succession Planning Strategies
Next Generation of Human Capital Training and Succession Strategies
Private Sectors National Succession Planning Strategies
Empowering the leaders towards the Next Era of Globalization leadership
Keeping the Nationals Aware of Latest Standards and Practices in the Private Sector
Creating Comprehensive Training Modules
Inculcating Young Learners With Practical Knowledge
Upgrading the Nationals with the Global Market Trends
Young Generation of Leaders Succession Planning and Strategic leadership development
How Nationals can be part of 4.00 Generation Organizations
Communication is the lifeblood of social as well as corporate aspects. It is communication where we effectively deliver our messages, give suggestions, improve or present condition and acquire learning. In this presentation, we look into the basics of communication but in the public relations context. Thank you. Enjoy the presentation.
How to run an outbound email campaigns with $0 investmentVenkat Ramakrishnan
I started doing outbound email campaigns recently and realised that its an ocean in itself . I have put together my learnings for the benefit of some one starting newly.
The presentation covers the following:
1. How to create a target list?
2. What content to send in an email?
3. How to handle a campaign
4. Further reading material to learn more
The presentation details the tools required to run a with $0 investment. The presentation also provides details on tools which will improve efficiency and can be used when your campaign starts to scale.
Email Marketing Solutions Built for AssociationsInformz
Offering email marketing solutions built with the needs of Associations in mind, coupled with expert emarketing advisors, Informz is your partner for email marketing success. Starting with campaign creation and targeting, to ongoing training, reporting, and support, you’ll have the tools and expertise you need to send the right message to the right person, at the right time.
Presented at the Healthcare IT Marketing and PR Conference held in 2016, the presentation highlights the realities of buying a marketing automation solution (such as Marketo, Pardot, Eloqua or Hubspot) and some cool tricks to get a lift out of your marketing efforts.
DMIEXPO - Keith Kouzmanoff - The Essential Strategies for Effective Inbox Del...Morning Dough
When a person subscribes to your email list if they don’t receive the email in their inbox? The likely hood of them finding it in the spam folder is about 3 percent. You should have a solid plan to send emails focused on inboxing and continuously maintain/monitor these priorities for successful email campaigns. These session will explain fundamental as well as advance critical steps that will help you continue to build your email list and let you focus on the creative side of emails. The focus of this session will touch the following; Confirmation Email, The Welcome Email, and then a variety follow up emails in which most use encompassing; promotional , triggers, funnels, seasonal, post-purchase, delivery tracking, newsletters, and the abandoned cart email. Mastering the foundation of inbox strategies is the quintessential key to any successful email delivery program.
High-speed internet may have opened up global opportunities for sales, but those opportunities come with challenges. When you have billions of potential customers, it can be overwhelming knowing where to focus your efforts.
https://belkins.io/how-to-organize-your-sales-process
Avoid these 10 mistakes in your internal communications strategyVing
An effective internal communications strategy is crucial to your financial bottom line. Effective communicators and increased profits are directly related. Here are 10 mistakes you should avoid so you can stop sabotaging the way you communicate.
At Microsoft I experienced how A/B testing grew from being occasionally used by a few teams in Bing and MSN several years ago to becoming widely used by many Microsoft products including Office, Windows, xBox, Skype, Visual Studio, and others. In some products it is already a standard required part of the software release process, helping ensure software quality, understand customer value, and make better data driven decisions. In others products it is growing steadily. At Microsoft, A/B testing is winning and will soon be part of everyone's daily job. However, when I left Microsoft to join Outreach, a startup that makes sales automation software, I got exposed to a different world. Even though Outreach provided A/B testing functionality, it was rarely used and the usage was often incorrect. While the need for trustworthy decision making through A/B testing in sales was clear, it was also clear that simply giving sales teams an A/B testing system like the one we had at Microsoft will not be enough. I learned that there is a big difference between a Microsoft engineer and a sales representative, with respect to their needs for successfully using A/B testing. In this talk I will discuss the gaps. What are our experimentation platforms, tools and processes, which were built for highly trained engineers, missing to make A/B testing truly available to everyone? I will also discuss ongoing work and future research directions to fill these gaps. While required to make A/B testing a success in sales, I believe that solving these problem will also help to increase adoption and successful usage of A/B testing in the software industry.
From a Sales Class Syllabus to a Sales Class Compensation PlanSalesforce.org
Presentation from Salesforce.org Higher Ed Summit 2017 by: Dr. Joel Le Bon of University of Houston
In this presentation, learn about the University of Houston Sales CRM class, where the adoption of Salesforce helps sell and manage the PES (Program for Excellence in Selling) Open - the world's largest charity golf tournament sold by sales students with over 1,100 players per year. Given their 100% turnover every semester (the entire sales force of sales students turns over), they use Sales Cloud to manage all customer-related data, and help students make their quota of $800 ($500 for a foursome + $300 of golf sponsorships) by managing activities, opportunities, their sales funnel, and a territory of about 300 accounts. Since 2010 and through this project, 873 sales students have learned to use and leverage Salesforce to raise $1,660,000 of sales revenue, and serve 7,297 golf players. This presentation will describe how they have accomplished this.
Predstavitev (keynote) na konferenci Zlati kamen 2019 - Občine prihodnosti, 7. 3. 2019:
Pametna mesta, tehnologije, standardi, primeri študentskih projektov, študijski program Menedžment pametnih mest, DOBA fakulteta, igrifikacija učenja
Sustainability, social innovations and information technologyTomislav Rozman
Is a bitcoin a social innovation? Is it sustainable? It depends on the point of view. Who is a sustainable leader? Can you learn about it to become one?
A result of TeachSus project, presented on 15. Feb. 2019 in Ljubljana, Slovenia (Multiplier Event).
Growth hacking and gamification - presentation Tomislav Rozman
3 examples of gamification and the results of the analysis among the incubators. PBL, journey, mission, quest, online learning course.
Presented at the conference in Zadar, Sept. 2017
By enGaging project.
Pregledno gradivo za predmet "Poslovna informatika"
6. del: Podatkovna baza, E-R modeliranje, tabele, entitete, povezave, poročila, vnosne maske, OpenOffice Base
Dodiplomski študij, DOBA Fakulteta za uporabne poslovne in družbene študije Maribor
Pregledno gradivo za predmet "Poslovna informatika"
5. del: informacijska varnost, neprekinjeno delovanje, gesla, arhiviranje, zaunost, dostop, socialno inženirstvo, e-pošta, virusi, piratstvo, avtorske pravice
Dodiplomski študij, DOBA Fakulteta za uporabne poslovne in družbene študije Maribor
Poslovna informatika 4: Razvoj in management informatikeTomislav Rozman
Pregledno gradivo za predmet "Poslovna informatika"
4. del: razvoj informacijskih sistemov, specifikacija zahtev, analiza, načrtovanje, management informatike, informacijska arhitektura, licenciranje
Dodiplomski študij, DOBA Fakulteta za uporabne poslovne in družbene študije Maribor
Poslovna informatika 3: e-poslovanje in digitalizacijaTomislav Rozman
Pregledno gradivo za predmet "Poslovna informatika"
3. del: e-poslovanje, B2B, B2C, B2A, e-plačevanje, informacijski razvoj podjetja
Dodiplomski študij, DOBA Fakulteta za uporabne poslovne in družbene študije Maribor
Poslovna informatika 2: Podpora upravljanju in infomacijska analizaTomislav Rozman
Pregledno gradivo za predmet "Poslovna informatika"
2. del: podpora upravljanju, podatkovno modeliranje, podatkovne kocke, informacijska analiza, OLAP
Dodiplomski študij, DOBA Fakulteta za uporabne poslovne in družbene študije Maribor
Pregledno gradivo za predmet "Poslovna informatika"
1. del: uvod v poslovno informatiko, poslovni informacijski sistemi, terminologija, podpora upravljanju
Dodiplomski študij, DOBA Fakulteta za uporabne poslovne in družbene študije Maribor
Growth hacking / Gamification - Case study (7)Tomislav Rozman
Growth hacking - Case study
enGaging project, slides from Gamification and Growth hacking academy training in Ancona (Italy)
www.engaging-project.eu
Slide set: 7.
Growth hacking - Content marketing
enGaging project, slides from Gamification and Growth hacking academy training in Ancona (Italy)
www.engaging-project.eu
Slide set: 3.
5 tips - how to become irreplaceable member of any project teamTomislav Rozman
I'm participating in several international projects (for more than 15 years) and I can assure you if you ignore these 5 tips, you will make someone's life ... very unpleasant.
The slideshow includes overview of EU project proposal preparation process. It is specific to Erasmus+ Key Action 2 - Strategic partnerships. Proposal preparation process for other calls may vary.
Achieving sustainable development by integrating it into the business proces...Tomislav Rozman
The purpose of the article is to present an approach how to integrate sustainability related topics into an organization’s management system. As a starting point and connecting tissue, we use a process-oriented approach (BPM) for managing companies and then apply sustainability dimensions (economic, ecologic, and social) to it. Using this approach we do not change existing or already established management systems of companies, but we adapt it by modifying company vision, strategy and most importantly, management and core processes.
Integrating sustainability related processes into organization management system prevents “fire-fighting” and ad-hoc activities, which are performed by companies to comply with the increasing number of sustainability related standards.
In addition, we present two managerial trainings (business process management and sustainability management), which when combined, will enable managers to adapt to today’s highly competitive business environment.
The concept presented here is a novel approach under the ECQA (European Certification and Qualification Organization), which will allow on-demand clustering of managerial skills and trainings (BPM and sustainability management).
The results presented are particularly useful for process analysts, quality managers, sustainability managers, social responsibility managers and similar professional profiles in order to improve their companies’ activities and processes with respect to the sustainable development values.
Specific ServPoints should be tailored for restaurants in all food service segments. Your ServPoints should be the centerpiece of brand delivery training (guest service) and align with your brand position and marketing initiatives, especially in high-labor-cost conditions.
408-784-7371
Foodservice Consulting + Design
Artificial intelligence (AI) offers new opportunities to radically reinvent the way we do business. This study explores how CEOs and top decision makers around the world are responding to the transformative potential of AI.
The Team Member and Guest Experience - Lead and Take Care of your restaurant team. They are the people closest to and delivering Hospitality to your paying Guests!
Make the call, and we can assist you.
408-784-7371
Foodservice Consulting + Design
The case study discusses the potential of drone delivery and the challenges that need to be addressed before it becomes widespread.
Key takeaways:
Drone delivery is in its early stages: Amazon's trial in the UK demonstrates the potential for faster deliveries, but it's still limited by regulations and technology.
Regulations are a major hurdle: Safety concerns around drone collisions with airplanes and people have led to restrictions on flight height and location.
Other challenges exist: Who will use drone delivery the most? Is it cost-effective compared to traditional delivery trucks?
Discussion questions:
Managerial challenges: Integrating drones requires planning for new infrastructure, training staff, and navigating regulations. There are also marketing and recruitment considerations specific to this technology.
External forces vary by country: Regulations, consumer acceptance, and infrastructure all differ between countries.
Demographics matter: Younger generations might be more receptive to drone delivery, while older populations might have concerns.
Stakeholders for Amazon: Customers, regulators, aviation authorities, and competitors are all stakeholders. Regulators likely hold the greatest influence as they determine the feasibility of drone delivery.
Comparing Stability and Sustainability in Agile SystemsRob Healy
Copy of the presentation given at XP2024 based on a research paper.
In this paper we explain wat overwork is and the physical and mental health risks associated with it.
We then explore how overwork relates to system stability and inventory.
Finally there is a call to action for Team Leads / Scrum Masters / Managers to measure and monitor excess work for individual teams.
Senior Project and Engineering Leader Jim Smith.pdfJim Smith
I am a Project and Engineering Leader with extensive experience as a Business Operations Leader, Technical Project Manager, Engineering Manager and Operations Experience for Domestic and International companies such as Electrolux, Carrier, and Deutz. I have developed new products using Stage Gate development/MS Project/JIRA, for the pro-duction of Medical Equipment, Large Commercial Refrigeration Systems, Appliances, HVAC, and Diesel engines.
My experience includes:
Managed customized engineered refrigeration system projects with high voltage power panels from quote to ship, coordinating actions between electrical engineering, mechanical design and application engineering, purchasing, production, test, quality assurance and field installation. Managed projects $25k to $1M per project; 4-8 per month. (Hussmann refrigeration)
Successfully developed the $15-20M yearly corporate capital strategy for manufacturing, with the Executive Team and key stakeholders. Created project scope and specifications, business case, ROI, managed project plans with key personnel for nine consumer product manufacturing and distribution sites; to support the company’s strategic sales plan.
Over 15 years of experience managing and developing cost improvement projects with key Stakeholders, site Manufacturing Engineers, Mechanical Engineers, Maintenance, and facility support personnel to optimize pro-duction operations, safety, EHS, and new product development. (BioLab, Deutz, Caire)
Experience working as a Technical Manager developing new products with chemical engineers and packaging engineers to enhance and reduce the cost of retail products. I have led the activities of multiple engineering groups with diverse backgrounds.
Great experience managing the product development of products which utilize complex electrical controls, high voltage power panels, product testing, and commissioning.
Created project scope, business case, ROI for multiple capital projects to support electrotechnical assembly and CPG goods. Identified project cost, risk, success criteria, and performed equipment qualifications. (Carrier, Electrolux, Biolab, Price, Hussmann)
Created detailed projects plans using MS Project, Gant charts in excel, and updated new product development in Jira for stakeholders and project team members including critical path.
Great knowledge of ISO9001, NFPA, OSHA regulations.
User level knowledge of MRP/SAP, MS Project, Powerpoint, Visio, Mastercontrol, JIRA, Power BI and Tableau.
I appreciate your consideration, and look forward to discussing this role with you, and how I can lead your company’s growth and profitability. I can be contacted via LinkedIn via phone or E Mail.
Jim Smith
678-993-7195
jimsmith30024@gmail.com
Public Speaking Tips to Help You Be A Strong Leader.pdfPinta Partners
In the realm of effective leadership, a multitude of skills come into play, but one stands out as both crucial and challenging: public speaking.
Public speaking transcends mere eloquence; it serves as the medium through which leaders articulate their vision, inspire action, and foster engagement. For leaders, refining public speaking skills is essential, elevating their ability to influence, persuade, and lead with resolute conviction. Here are some key tips to consider: https://joellandau.com/the-public-speaking-tips-to-help-you-be-a-stronger-leader/
1. Email management process …
… because we all know how to handle email (NOT)
dr. Tomislav Rozman
Slideshow by Tomislav Rozman is licensed under a Creative Commons Attribution 4.0 International License.
2. Motivation
Managing your inbox is easy, right?
Wrong!
Your employees know how to communicate, right?
Wrong!
Your contacts are responsive, never lost emails, don’t have excuses, right?
Wrong!
2
3. Context and challenge
● The challenge we are trying to solve with the “Email management
process”:
○ ensuring sender / customer satisfaction
○ ensuring organization's reputation
○ ensuring proper routing of emails between employees
○ ensuring standardized response of employees to most common situations and
types of incoming email
○ improving communication culture and netiquette
○ improving self-management
● Context
○ Typical organization, which communicates with customers and partners (= nearly all
organizations)
○ Can be also applied to personal email management
○ When the organization employs somebody it has to teach him/her the strategy of
professional email management
3
9. 1.3 Email process -Handle complex
9
Critical tasks to
maintain your
integrity and
good relations!
One level up
10. 1.4 Email process -Handle other
10
What about
‘delete all and wait
for important
email to come
back’?
One level up
11. After applying this process to your organization
Managing your inbox is easy, right?
A little bit better now!
Your employees know how to communicate, right?
A little bit better now!
Your contacts are responsive, never lost emails, don’t have excuses, right?
It’s improving!
11
12. Disclaimer
12
Process models / examples are partial mappings of real situations and
were designed using BPMN.IO tool, a derivate of Camunda.
If you need .bpmn versions of the process models, please subscribe on the
next page.
After downloading you can open process models in: BPMN.io, Camunda,
Yaoquiang, Bonita BPM and possibly other tools.
13. Last but not least
Like it, share it, comment it!
And of course, don’t forget to reuse it (with contribution) in your projects.
If you want to learn how to identify, model, optimize processes, you can
attend our live or on-line learning courses:
○ ECQA Certified Business Process Manager - Foundation and Advanced level
○ ECQA Certified Business Process Manager in Higher Education Institutions
○ i-VBPM (BPM on interactive board for VET)
Subscribe: here
Website: BICERO ltd.
E-mail: tomislav.rozman@bicero.com
LinkedIn: Tomislav Rozman 13