This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
Data Science is the Sexiest job in 21st century. Big Data Concept is going to rule the 21st century. Here is the presentation to give complete information and overview of data science big data.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
Data Science is the Sexiest job in 21st century. Big Data Concept is going to rule the 21st century. Here is the presentation to give complete information and overview of data science big data.
In this presentation, let's have a look at What is Data Science and it's applications. We discussed most common use cases of Data Science.
I presented this at LSPE-IN meetup happened on 10th March 2018 at Walmart Global Technology Services.
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
Data Scientist has been regarded as the sexiest job of the twenty first century. As data in every industry keeps growing the need to organize, explore, analyze, predict and summarize is insatiable. Data Science is creating new paradigms in data driven business decisions. As the field is emerging out of its infancy a wide range of skill sets are becoming an integral part of being a Data Scientist. In this talk I will discuss the different driven roles and the expertise required to be successful in them. I will highlight some of the unique challenges and rewards of working in a young and dynamic field.
JavaZone 2018 - A Practical(ish) Introduction to Data ScienceMark West
Code: https://github.com/markwest1972/titanic
Video: https://vimeo.com/289705893
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all of this?
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
A Practical-ish Introduction to Data ScienceMark West
In this talk I will share insights and knowledge that I have gained from building up a Data Science department from scratch. This talk will be split into three sections:
1. I'll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organisation.
2. Next up well run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
GeeCon Prague 2018 - A Practical-ish Introduction to Data ScienceMark West
Data Science has been described as the sexiest job of the 21st Century. But what is Data Science? And what has Machine Learning got to do with all this? In this session I will share insights and knowledge that I have gained from building up a Data Science department from scratch. The talk will be split into three sections:
1. I’ll begin by defining what Data Science is, how it is related to Machine Learning and share some tips for introducing Data Science to your organization.
2. Next up we’ll run through some commonly used Machine Learning algorithms used by Data Scientists, along with examples for use cases where these algorithms can be applied.
3. The final third of the talk will be a demonstration of how you can quickly get started with Data Science and Machine Learning using Python and the Open Source scikit-learn Library.
Overview of a Machine Learning 11 week course I developed and trained software engineers at Dell on their way to become Data Scientists. Class is outline of Predictive Analytics methods using Python. I taught this class 8 separate occasions over 3 years.
Which institute is best for data science?DIGITALSAI1
EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
Join us for the Best Selenium certification course at Edux factor and enrich your carrier.
Dream for wonderful carrier we make to achieve your dreams come true Hurry up & enroll now.
<a href="https://eduxfactor.com/selenium-online-training">Best Selenium certification course</a>
Data Science Online Training In HA comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.hyderabad Data Science Online Training
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Data science training institute in hyderabadVamsiNihal
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
Eduxfactor is an online data science training institution based in Hyderabad. A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Data science online training in hyderabadVamsiNihal
Exploring the EduXfactor Data Science Training program, you will learn components of the Data Science lifecycle such as Big Data, Hadoop, Machine Learning, Deep Learning & R programming. Our professional experts will teach you how to adopt a blend of mathematics, statistics, business acumen, tools, algorithms & machine learning techniques. You will learn how to handle a large amount of data information & process it according to any firm business strategy.
Overview of Data Science Courses Online
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge.
What You'll Learn In Data Science Courses Online
Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
Comprehend the crucial steps required to solve real-world data problems and get familiar with the methodology to think and work like a Data Scientist.
Learn to collect, clean, and analyze big data with R. Understand how to employ appropriate modeling and methods of analytics to extract meaningful data for decision making.
Implement clustering methodology, an unsupervised learning method, and a deep neural network (a supervised learning method).
Build a data analysis pipeline, from collection to analysis to presenting data visually.
#datasciencecoursesonline
#datascience
#datasciencecourses
A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
Taught and developed by experienced and certified data professionals, the course goes right from collecting raw digital data to presenting it visually. Suitable for those with computer backgrounds, analytic mindset, and coding knowledge
EduXfactor is the top and best data science training institute in hyderabad offers data science training with 100% placement assistance with course certification.
L'approche de tests par propriétés est une alternative aux techniques de tests basés sur des exemples.
Cette approche est conçue pour tester les aspects d'une propriété qui doit toujours être vraie. Ceci permet de couvrir un grand éventail de valeurs d'entrées programmatiquement et de tester celles-ci en un seul test. Contrairement aux tests basés sur des exemples où l'on doit faire un test pour chaque exemple.
Nous couvrirons les outils requis pour débuter à utiliser les tests par propriété.
Nous construirons ensemble une suite de tests afin de démontrer la puissance de cette approche dans un cas réel.
- Accès aux données (CRUD) avec Entity Framework et quelques optimisations d eperformance grâce à des extensions pour EF Core. Des alternatives à EF Core telles que ORMLite nous aborderons également.
- Gestion de services multitenant avec L'injection de dépendance dynamique
- Api versioning
- Streaming
- la compression
- L'intégration avec Application Insights & live metrics
- Protéger les données secretes avec AzureKeyvault
- Healthcheck
- Policy retry et circuit breaker sur les HttpClient et call sql avec Polly
- Mise à jour de la partie 1 concernant TestServer: utilisation de la webapplicationfactory
Groupe Excel et Power BI - Rencontre du 25 septembre 2018MSDEVMTL
Voici la présentation d'introduction du groupe d'usagers Montreal Modern Excel and Power BI du 25 septembre 2018. Elle vous montre notamment comment aller récupérer les documents de nos meetings.
Authenticating, validating, caching, error handling, logging, documenting, testing and profiling are common features in web API, here are code samples to show how to implement them!
Stephane Lapointe: Governance in Azure, keep control of your environmentsMSDEVMTL
June 11th 2018
Azure Group
Subject: Governance in Azure, keep control of your environments.
Speaker: Stephane Lapointe, Azure MVP
It's very easy to lose control over what's happening in your Azure environments. In this talk, see solutions for managing security, costs, and governance. We'll talk about tools like tags, RBAC, policies, Azure Security Center & Azure Advisors to implement initiatives that will greatly help your management in Azure.
Eric Routhier: Garder le contrôle sur vos coûts AzureMSDEVMTL
11 juin 2018
Groupe Azure
Sujet: Garder le contrôle sur vos coûts Azure
Confériencier: Eric Routhier
Azure permet aux entreprises d'être plus agiles et plus innovantes en décentralisant la gestion de l'infrastructure. En contrepartie, les coûts augmentent souvent rapidement et les administrateurs se demandent parfois s'ils l’exploitent vraiment à son plein potentiel. Apprenez comment gagner une meilleure visibilité sur votre consommation Azure et surtout apprenez quelques trucs pour réduire votre facture considérablement!
Open id connect, azure ad, angular 5, web api coreMSDEVMTL
Dans cette se session nous allons démystifier la configuration et l'utilisation d'Open Id Connect avec Azure AD.
L'application cliente sera un SPA Angular 5, implémentant la librairie ADAL.JS, enfin nous allons voir comment proétéger une WebAPI avec un JSON Web Token (JWT)
Dan Edwards : Data visualization best practices with Power BIMSDEVMTL
30 octobre 2017
Groupe Excel et Power BI
Sujet: La visualisation de données (en anglais)
Conférencier: Dan Edwards
Voici la présentation du conférencier Dan Edwards sur les meilleures pratiques d'affaires à adopter en visualisation de données, avec Power BI (en anglais)
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.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
6. Why the Hype Around Data Science?
● IBM predicts that demand for data scientists will soar by 28% by 2020
● Data scientist roles have grown over 650% since 2012, but currently, 35,000 people in
the US have data science skills, while hundreds of companies are hiring for those roles.
● Software engineering is a common starting point for professionals who are in the
top five fasting growing jobs today. The career path to Machine Learning Engineer
and Big Data Developer begins with a solid software engineering background.
● Data Science gives you career flexibility
7. Who Are Data Scientists?
https://www.datascience.com/blog/data-scientist-skills
8. Who Are Data Scientists?
https://www.datascience.com/blog/data-scientist-skills
9. Who Are Data Scientists?
https://www.datascience.com/blog/data-scientist-skills
13. Application – Microsoft (Skype Product)
● The first is with a product feature called Skype Translator. As its implied,
Skype uses machine learning to translate a conversation between two people
speaking different languages through the use of a third party bot that joins
your call.
Skype Translator – How it Works | Skype Blogs
● The second is to detect fraudulent Skype Users, examples range from
users who send spammy messages, to credit card and online payment fraud.
This is an important application of machine learning as you can imagine, a
platform that’s riddled with spammers and fraudsters is not one that will likely
retain many users.
Detecting Fraudulent Skype Users via Machine Learning
14. Learning Data Science With Python - Libraries
NumPy is a library for the Python
programming language, adding
support for large, multi-
dimensional arrays and matrices,
along with a large collection of
high-level mathematical functions
to operate on these arrays.
Pandas is a software library
written for the Python
programming language for
data manipulation and
analysis. In particular, it offers
data structures and operations
for manipulating numerical
tables and time series.
A free software machine learning
library that features various
classification, regression and
clustering algorithms including
support vector machines,
random forests, gradient
boosting, and k-means and is
designed to interoperate with the
Python numerical and scientific
libraries NumPy and SciPy.
15. Learning Data Science With Python - Libraries
A plotting library for the Python
programming language and its
numerical mathematics extension
NumPy
Keras is an open source
neural network library written
in Python. It is capable of
running on top of TensorFlow,
Microsoft Cognitive Toolkit,
Theano, or MXNet. It was
developed with a focus on
enabling fast experimentation
TensorFlow is an open-source
software library for dataflow
programming across a range of
tasks. It is a symbolic math
library, and is also used for
machine learning applications
such as neural networks.
16. Learning Data Science With Python - Tools
Open-source web application that
allows you to create and share
documents that contain live code,
equations, visualizations and
narrative text
http://jupyter.org/
Crestle is your GPU-enabled
Jupyter environment in the
cloud.
https://www.crestle.com/
Similar to Jupyter Notebook, but
with the added benefit of “google
doc” type sharing and
collaboration
https://colab.research.google.com
17. Data Science Steps
● Data Gathering
Unless you’re at a company with great data governance you’re likely going to have some trouble
accessing the data you want. Whether that's because your company has neglected to put the
necessary systems in place to gather data, or the data that they are collecting is fragmented and
scattered across the organization, you’ll have to first spend some time gathering whatever data you’ll
need to do your job. That means having discussions with relevant stakeholders, and getting the
necessary credentials to access databases within your organization.
● Data Preparation
Once you have access to data, you’ll need to spend some time cleaning and formatting it. This is
where Data Science can often become more of an art, then a science. Unlike datasets you’ll find in
competitions, the real world has very messy data sets. Missing values, error in data collection, data
formatting, normalization, outliers - these are all issues that you’ll have to learn to deal with.
18. Data Science Steps
● Exploration
Before diving into building any models, you’ll want to explore the data to try to glean
some insights. Clustering algorithms, scatterplots, bar graphs, Chernoff faces are all
interesting ways of visualizing data that will lead to a better understanding of the
structure of your data and aid you in your model building step.
● Model Building
With your data cleaned and formatted, you’ll have an opportunity to explore a variety of
models to see which one works best. Random Forests, SVM’s, Bayesian Predictors
Neural Networks, Deep Learning, K-Nearest Neighbours - all models you should
familiarize yourself with. There is no one model fits all, and so you again will need to
develop intuition on which model suits your particular problem.
19. Data Science Steps
● Model Validation
Prediction accuracy is a common benchmark for whether your model is performing well, however
often times there are other evaluation metrics to consider. False positives and false negatives are
important to think about from the perspective of the problem you’re working on. If you’re predicting
disease, you’ll care more about minimizing false negative, since it may result in a persons death -
whereas a false positive will only lead to additional testing.
● Model Deployment
Finally you’ll deploy your model into the wild, as you gather more data and feedback on how its
doing you’ll be able to tweak and improve it as time goes on.
*This is by no means a comprehensive list of steps, and there are certainly other things you’ll need
to do to be able to do well in your job - however this is a good high level overview of the steps
involved in tackling data science problems.
24. Resources
Podcasts Websites/Blogs Communities
Data Skeptic Dataquest.io
experfylabs.slack.com/m
essages/C0L736X36/
Data Stories Kaggle.com
opendatacommunity.slac
k.com
Learning Machines 101 Quora.com dcommunity.slack.com
Linear Digressions analyticsvidhya.com kagglenoobs.slack.com
O’Reilly Data Show Coursera.org pythondev.slack.com
Talking Machine
https://developers.google.com/
machine-learning/crash-
course/
This week in Machine Learning and AI https://portal.azure.com/
Siraj Raval (Youtube) https://www.luis.ai/
25. Resources
Books Tv Shows/documentaries
Hands-On Machine Learning with Scikit-Learn and TensorFlow Humans (2015-)
Python Machine Learning, 1st Edition Persons of interest
Everybody Lies: Big Data, New Data Intelligence
Weapons of Math Destruction Minority report
Big Data: A Revolution That Will Transform How We Live, Work, and Think Almost human
Turing: Pioneer of the Information Age Robot and frank
Avogadro Corp Her
Code: The Hidden Language of Computer Hardware and Software Black Mirror
Superintelligence: Paths, Dangers, Strategies iRobot
Visual Explanations: Images and Quantities, Evidence and Narrative Ex Machina
Pattern Recognition and Machine Learning (Information Science and Statistics)
The Secret Rules of Modern Living:
Algorithms
Storytelling with Data: A Data Visualization Guide for Business Professionals
An Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani