Nearly 500 people have been killed in an Israeli air attack on the Al-Ahli Arab Hospital in the besieged Gaza Strip, Palestinian officials have said. The health ministry in Gaza said the blast at ...

What we know so far about the deadly strike on a Gaza hospital

In Gaza, hospitals were on the verge of losing electricity, threatening the lives of thousands of patients, and hundreds of thousands of Palestinians displaced from their homes searched for bread.

As Israeli forces gather, humanitarian aid can't get through to Gaza

Horizon Health Network says it's bringing back masking requirements in anticipation of an increase in COVID-19 cases and other respiratory viruses in its hospitals and wider community.

Horizon brings back mask rules for hospitals ahead of expected COVID increase

(Reuters) - A blast at a Gaza hospital killed hundreds of Palestinians just before U.S. President Joe Biden visited Israel as the conflict between the Israeli military and the Hamas militant group raged. WHO BOMBED THE HOSPITAL IN GAZA? Israelis and ...

Factbox-Gaza hospital blast: what we know about the explosion

Hundreds of people were killed in a devastating explosion at the al Ahli hospital in an attack that Palestinian officials have blamed on Israel. It is the deadliest single event of the Israel ...

Israel-Hamas war: Israel releases evidence which it claims shows an Islamic Jihad rocket was responsible for Gaza hospital blast

Hospitals around Ontario have faced staffing shortages, with many forced to implement temporary emergency room closures as a result — an ER in Minden had to permanently close, and urgent care ...

Muskoka hospital says patient volumes surge to 150% capacity

By The New York Times Hundreds of people were feared dead after an explosion at a hospital in the Gaza Strip on Oct. 17, a little over a week after the Palestinian group Hamas staged a terrorist ...

What We Know About the Explosion at the Hospital in Gaza

A Hamas-run hospital that was treating wounded Palestinians and sheltering many others in the Gaza Strip was hit by an airstrike Tuesday, killing hundreds of people, though Israel Defense Forces ...

Hundreds killed in blast at Gaza Strip hospital; Israel says Islamic Jihad's 'failed rocket launch' to blame

By Raja Abdulrahim, Hiba Yazbek and Yousur Al-Hlou Reporting from Jerusalem and Cairo Sameh al-Jaroosha was sitting on the edge of the grassy courtyard of the Ahli Arab Hospital in Gaza City on ...

A Sudden Blast, Then Carnage in a Hospital Courtyard

With the Medicare open enrollment period under way, the Nebraska Hospital Association is urging Nebraska seniors to make sure they fully understand what they’re signing up for when they choose a ...

Hospitals say patients denied care under Medicare Advantage

Get the most recent info and news about GNRC Hospitals on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

GNRC Hospitals

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! Toshima Altplus Guangzhou, China came out as the Startup of the Year in ! Leyaoyao Guangzhou Hong Kong, Hong Kong In , is declared the Startup of the Year! Hong Kong Episode Six Kunming In , is the winner! Kunming Green A Sapporo In , is declared the Startup of the Year! Sapporo Polar Star Space Shirokanedai came out as the Startup of the Year in ! Nupp1 Shirokanedai Wuhan In , is the winner! Wuhan CSICEE Baoan, China In , is declared the Startup of the Year! Baoan Jumper Baoshan, China In , is declared the Startup of the Year! Baoshan Renren Games Chengdu, China came out as the Startup of the Year in ! Ju You Xi Chengdu Fujisawa, Japan In , is the winner! Fujisawa Scohia Pharma Fukuoka, Japan In , is declared the Startup of the Year! Fukuoka Kyulux Haidian, China came out as the Startup of the Year in ! 51vv Haidian Hongkou, China In , is the winner! Hongkou Kidstone Jiading, China In , is declared the Startup of the Year! Jiading IMRobotic Kawasaki, Japan came out as the Startup of the Year in ! QD Laser, Inc. Kawasaki Kyoto, Japan In , is declared the Startup of the Year! Kyoto Hacarus Minato, Japan In , is the winner! Minato Visionary Fun Nagoya, Japan came out as the Startup of the Year in ! Prodrone Nagoya Ningbo, China The winner in is . Ningbo OlymTech Corporation Aomori In , is declared the Startup of the Year! Aomori Forte Japan Bandai-cho In , is the winner. Bandai-cho Delta-Fly Pharm Changchun In , the Startup of the Year is . Changchun Kaidi Animation Fuchu In , is declared the Startup of the Year! Fuchu TMS Fukuoka-shi The winner in is . Fukuoka-shi SkyDisc Fukushima In , the Startup of the Year is . Fukushima Matsuri Technologies Gotanda In , is the winner. Gotanda Basset Hachioji In , is declared the Startup of the Year! Hachioji Adriakaim Harbin The winner in is J . Harbin oining Technology Iwaki In , is the winner. Iwaki Tao Engineering Izumo In , the Startup of the Year is . Izumo PuREC Kagoshima In , is declared the Startup of the Year! Kagoshima Farmers Support Kamakura In , the Startup of the Year is . Kamakura KAYAC Kami-meguro The winner in is . Kami-meguro Buzzreach Kanagawa In , is the winner. Kanagawa LoiLo Kashiwa In , the Startup of the Year is . Kashiwa Intelligent Surfaces Kita In , is declared the Startup of the Year! Kita Ax Robotix Kobe The winner in is . Kobe Coconomi Koriyama In , the Startup of the Year is . Koriyama Tocoton Kumamoto In , is the winner. Kumamoto Bloom Technology Kunitachi In , is declared the Startup of the Year! Kunitachi Trys Liaocheng The winner in is . Liaocheng Jinzheng Animation Minami In , is declared the Startup of the Year! Minami ATLAB Miyazaki In , is the winner. Miyazaki Caster Nagano In , is declared the Startup of the Year! Nagano Precision Forestry Nagaoka The winner in is . Nagaoka Plant Form Nihombashi-hamacho In , the Startup of the Year is . Nihombashi-hamacho Shizen Energy Niigata In , is declared the Startup of the Year! Niigata UniBio Japan Nishi-shinagawa In , is the winner. Nishi-shinagawa Play Heart Okayama In , is the winner! Okayama Arch Farm Okinawa In , is declared the Startup of the Year! Okinawa BlogNinja Otemachi In , the Startup of the Year is . Otemachi beBit, Inc Qingdao Shi In , is the winner! Qingdao Shi Manyoubang Quanzhou In , is declared the Startup of the Year! Quanzhou KungFu Animation Group Roppongi The winner in is . Roppongi Holo-X Saitama In , the Startup of the Year is . Saitama GameTector Sendai In , is the winner! Sendai Sendai Smart Machines Setagaya In , is declared the Startup of the Year! Setagaya Skyestate Shenzhen In , the Startup of the Year is . Shenzhen Aiator Shinjuku In , is declared the Startup of the Year! Shinjuku Scalar Shizuoka In , is the winner! Shizuoka PiPhotonics Sumida The winner in is . Sumida Holotch Tachikawa In , the Startup of the Year is . Tachikawa PlusOne Takamatsu In , is declared the Startup of the Year! Takamatsu Ken Robotec Tokushima In , is the winner! Tokushima DennoKotsu Toyonaka In , the Startup of the Year is . Toyonaka FISH BIOTECH Tsukuba In , is declared the Startup of the Year! Tsukuba WARPSPACE Wakayama In , the Startup of the Year is . Wakayama 4D Sensor Xiangzhou In , is the winner! Xiangzhou ALLiN Edutech Group Xianyang The winner in is . Xianyang TONLY Xiaoshan In , is declared the Startup of the Year! Xiaoshan GONGHUI Yamaguchi In , the Startup of the Year is . Yamaguchi Archis Yangpu The winner in is . Yangpu Tmxmall Yau Tsim Mong In , is the winner! Yau Tsim Mong Airnugget Yeoksamdong In , the Startup of the Year is . Yeoksamdong WizSchool Yichang In , is declared the Startup of the Year! Yichang Hubei Kento Electronic Yokohama In , is the winner! Yokohama Next Innovation Yubei In , the Startup of the Year is . Yubei Ophiux Network Yueqing The winner in is . Yueqing Yueqing Easythink Media Zhuhai In , is the winner! Zhuhai Aijuba Hongo In , the Startup of the Year is . Hongo Smile Robotics, Inc Admiralty In , is declared the Startup of the Year! Admiralty BUNCH Southern In , the Startup of the Year is . Southern Retykle Yongdungpo In , is the winner! Yongdungpo Push News Taichung In , is declared the Startup of the Year! Taichung Wondercise Cyberport, Hong Kong In , the Startup of the Year is . Cyberport Animoca Brands Hefei, China In , is the winner! Hefei Tsinglink New Territories, Hong Kong The winner in is . New Territories, Hong Kong Zeek Dezhou In , the Startup of the Year is . Dezhou Dezhou Baji Kitakyushu In , is declared the Startup of the Year! Kitakyushu Reif Seongnam In , is the winner! Seongnam EOFlow Yanshi In , the Startup of the Year is . Yanshi CASI Vision Top Startup Cities in South Asia Hyderabad, India The Startup of the Year in is with 962 votes Hyderabad Quixy Lahore, Pakistan is the Startup of the Year! It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

Security and data breaches are a growing concern globally. Believe it or not, in the 10 biggest data breaches in the first half of 2020. The data could be anything ranging from Social Security numbers to your credit card details to something as personal as a medical record. more than 3.2 million records were compromised That’s why cybersecurity is top of mind for every company around the world. With the increasing number of data breaches  — — the has become vital. In fact, the need for cybersecurity specialists has become so paramount, that according to the US Bureau of Labor Statistics, the occupation is projected to grow at a much faster rate — more than 30% between 2018 and 2028 —  than the average of all other occupations. up 54% in 2019 role of a cybersecurity professional As demand for cybersecurity specialists reaches a fever pitch, Udacity is excited to announce the release of the all-new — created in collaboration with SecurityScorecard — a great start for anyone interested in this critical field. Introduction to Cybersecurity Nanodegree program Prep for Future Jobs with our Introduction to Cybersecurity Nanodegree program This Nanodegree program prepares students with a strong foundation in cybersecurity skills and knowledge. The program is a great start in the Cybersecurity field or a great supplement for IT professionals looking to build stronger Cybersecurity skills. The Nanodegree program fills in the gap in hands-on skills-based training in the field of cybersecurity. Currently, there are many university or college-level programs focused on the theory of cybersecurity without addressing the practical application of that theory; boot camps could provide practical training, but it comes with a very high price tag. Udacity’s Nanodegree program fills that gap by offering the right balance of structured content and practitioner-level skills for existing cybersecurity professionals. Our project-based learning approach prepares students for the real-world problems faced by organizations globally. Data breaches and security threats are a growing concern and with this program, you will not only learn the fundamentals of cybersecurity but also get to implement and test them in a real-world context. Here’s what you’ll learn in the Introduction to Cybersecurity Nanodegree program: COURSE 1: Cybersecurity Foundations This foundations course explains security fundamentals including core principles, critical security controls, and the best practices. PROJECT 1: Securing a Business Network Investigate and fix security issues on a Windows 10 client system as a way of demonstrating fundamental cybersecurity knowledge, skills, and abilities. COURSE 2: Defending and Securing Systems Get exposed to a diverse group of technologies that will provide or enhance the skills needed to enter the field. : PROJECT 2 Monitoring and Securing Douglas Financials Inc. Analyze Windows and Linux servers and report recommendations on OS hardening, compliance issues, encryption, and network security for Douglas Financials Inc. COURSE 3: Threats, Vulnerabilities, and Incident Response Learn how to identify security threats and gaps, fix issues, and respond to inevitable attacks. : PROJECT 3 Navigating a Cybersecurity Incident Apply the skills you have acquired to navigate a potential cyber incident threatening a hospital. : COURSE 4 Governance, Risk, and Compliance Learn about the functions of Governance, Risk, and Compliance and how each function operates alongside operational controls to strengthen an organization’s security. : PROJECT 4 Create the SwiftTech GRC Program As a brand new GRC analyst for SwiftTech, you’ll need to understand the business quickly and improve their GRC documentation to help support the organization’s goals. This program is great for IT professionals, risk analysts, and QA engineers who need to build foundational Cybersecurity knowledge, though any individual with basic familiarity of network connectivity and operating systems would also benefit from these critical skills. Partnering with Industry-leader SecurityScorecard Udacity has partnered with SecurityScorecard to bring industry-relevant cybersecurity skills training to students and businesses globally, starting with the Introduction to Cybersecurity Nanodegree program. “Our goal at Udacity is to train the world’s workforce in the in-demand careers of the future, and cybersecurity is a critical industry that’s experiencing a significant gap between demand for and supply of qualified professionals,” said Alper Tekin, Chief Product Officer at Udacity. “We’re excited and proud to partner with SecurityScorecard to bring practical cybersecurity skills training to students and companies across all industries, to answer the massive demand for cybersecurity professionals.” is the global leader in cybersecurity ratings and the only service with over a million companies continuously rated. Founded in 2013 by security and risk experts Dr.Aleksandr Yampolskiy and Sam Kassoumeh, SecurityScorecard’s patented rating technology is used by over 1,000 organizations for enterprise risk management, third-party risk management, board reporting, due diligence, and cyber insurance underwriting. SecurityScorecard “We are thrilled to partner with Udacity to bring easily accessible cybersecurity education to the public,” said Aleksandr Yampolskiy, CEO and co-founder of SecurityScorecard, the global leader in security ratings. “Our company mission is to make the world a safer place. By partnering with Udacity we are making additional progress on this journey and helping to facilitate the development of future and current cybersecurity professionals. The world needs skilled security practitioners and we’re happy to join Udacity in this endeavour.” Learn About Cybersecurity with the Best Instructors This program has been created in collaboration with some of the top professionals and academics in the industry. , CEO, Cyber-AAA Ron Woerner , Information Security Engineer, University of Alabama, Birmingham Jerry Smith , CEO, Cyber Pop-Up Christine Izuakor , Senior Director, Security and GRC, Business Wire Sean Pike Industry-wide Demand for Cybersecurity Specialists on the Rise According to Microsoft’s , there are upwards of 744,000 cybersecurity professionals today, but cybersecurity roles are expected to grow 750% in the next five years, amounting to almost 6.4 million jobs and not nearly enough qualified cybersecurity professionals to fill them. research of qualified applicants, that typically translates to big advantages for those job-seekers. For example, the median salary for cybersecurity professionals as of May 2019 is When demand for a role is over double the supply $99,730 per year and $47.95 per hour . Beyond compensation, cybersecurity professionals also benefit from job opportunities across the full spectrum of industries, given that every business needs to maintain adequate security practices and infrastructure to avoid data breaches. Particularly high Information Technology & Services, Defense & Space, Computer Network & Security, Management Consulting, and Financial Services. demand for cybersecurity professionals comes from sectors like Enroll Today! Excited to get your start in this in-demand field? Enroll today in Udacity’s new ! Introduction to Cybersecurity Nanodegree program

The Lowdown on Udacity's Intro to Cybersecurity Nanodegree 

What I Would Have Missed in April 2018

Introduction Sentiment analysis has been predominantly used in data science for analysis of customer feedbacks on products and reviews. They are used to understand user ratings on different kinds of products, hospitality services like travel, hotel bookings. It has also become popular to analyse user tweets — positive, negative or neutral by crawling twitter through APIs. In this article, we talk about sentiment analysis of the upcoming Lokshobha Elections for Congress and BJP by crawling tweets from different hashtags of either parties, party leaders, as well as news hashtags like NDTV. The sentiments analysed covers different user-reactions not only restricted to positive or negative sentiments but covers an in-depth analysis of various positive and negative moods along with the results of different ML models. We categorise the analytics and machine learning into 3 sections: Crawling, cleaning data and labelling un-structured data by using/mapping known English words from various sourcesApplying Natural Language based classifiers used for text processing to train tweets and predict moodsApplying standard machine learning algorithms and deep learning to do multi-class mood classification for 2 prominent parties in the election The objective of this blog is to highlight mechanisms for labelling tweets, and classifying and summarising them from different viewpoints. Crawl Weekly tweets and Merge: We crawl tweets on a weekly basis and merge them with previous weeks to have an overall prediction over a period of few months. The system is designed to learn from tweets every week and consolidates results by eliminating duplicate tweets. It preserves the retweet counts to understand the impact of higher number of retweets. list_BJP = []
list_Cong = [] ( file_[i]):
    df_BJP = pd.read_csv(file_[i],index_col= , header= ) ( file_[i]):
    df_Cong = pd.read_csv(file_[i], index_col= , header= )
list_BJP.append(df_BJP)
list_Cong.append(df_Cong)
df_BJP = pd.concat(list_BJP, axis = , ignore_index = )
df_BJP = df_BJP.drop_duplicates(subset=[ , full_text created_at full_text full_text if 'BJP' in None 0 if 'Cong' in None 0 0 True 'created_at' 'full_text’])#dropping retweets with same text posted at same time
df_BJP = df_BJP[df_BJP.full_text != ' ']

df_Cong = pd.concat(list_Cong, axis = 0, ignore_index = True)
df_Cong = df_Cong.drop_duplicates(subset=[' ', ' '])
df_Cong = df_Cong[df_Cong.full_text != ' '] Crawl Mood Words and labelling unstructured data: The mood vocabulary is built using english word repository available in the internet. The following mood labels Joy, Sadness, Arousal, Dominance, Neutral, Anger, Fear, Faith(Support) were assigned to tweets by taking the strongest mood in the sentence, by taking each word from the sentence into account, along with the emoji in consideration. For example, the overall mood of the sentence is Dominance when each word in the sentence have the following moods. [‘dominance’, ‘dominance’, ‘dominance’, ‘dominance’, ‘dominance’, ‘joy’, ‘arousal’, ‘dominance’] max_mood_item = max(mood_freq_dist.items(), key=operator.itemgetter( ))[ ] 1 0 The sentiment of each word is derived by assigning an affectual score to it . The lexicon dictionary for 25,000 words are dowloaded from NRC Word -Emotion Association Lexicon (Reference 2). If certain words in a sentence are missing from Vader or the specific mood type is missing, TextBlob is used to determine positive/negative sentiment of the word. For example, for the following tweet “I request all fellow Indians to get rid of this clown coming elections. Please vote wisely”, the word “wisely” encounters a Valence score of 0.878, but it does not differentiate between positive (Joy)/negative (Sadness/Anger) mood, which necessitates a further lookup of word polarity through TextBlob. Finally with a positive score of 0.7 its labelled as sentiment of “Joy”. While affectual score and TextBlob determines mood of each word, SentimentIntensityAnalyzer is used to calculate the overall polarity of a sentence. It uses Vader’s lexicon (Reference 2) which rates individual words (present in the lexicon) in a sentence on a scale of highly negative to highly positive. For example, for a tweet, “We stand rock solid behind you @narendramodi Our party has performed well under all odds, we will do better in” has few words in the lexicon with score as “solid”: 0.6, “party” : 1.7, “well” :1.1 and “better” 1.9 These word ratings help to derive four sentiment metrics to represent the proportion the tweet falls under it. ‘compound’: 0.8074, ‘neg’: 0.0, ‘neu’: 0.632, ‘pos’: 0.368 This explains the tweet is how much positive, negative or neutral. The compound score have been standardised to range between -1 and 1 and is calculated by calculating the normalized sum (normalize(sum_s)) of all of individual word ratings (0.6, 1.7, 1.1, 1.9) present in the lexicon. max_mood_item = max(mood_freq_dist.items(), key=operator.itemgetter( ))[ ] 1 0 All tweets vary in intensity from -1 to +1. As the below figures shows strong positive sentiments like “Joy” and “Faith” incline more 0 to +1 for both BJP and Congress, while negative sentiments like “Anger” and “Sadness” incline more between -1 to 0. “Neutral” sentiment is centred around zero. Sentiments like “Arousal” and “Dominance” are more or less distributed equally between -1 to +1 which signify they can be either tweeted in a positive or negative mind. For example, the tweet “In 2014 when Modi elected PM candidate, people eected change will happen” records “Dominance” with positive sentiment . While the tweet “2019 elections will be fought on completely different lines’, says @amitmalviya, National Spokesperson, BJP in conversation” records a negative sentiment because of the word “fought”. Sentiment IntensityAnalyzer calculates the compound metric of the tweet as -0.3182, while positive, negative and neutral scores are 0.0, 0.247 and 0.753 respectively. Further rating the word “fought” in terms of “Valence” — (Joy/Sadness/Anger/Fear/Faith), “Arousal” or “Dominance”, the measurements are “Valence” : 0.531, “Arousal” : 0.809, “Dominance” : 0.868, justifying the predominance of “Dominance” mood. Similarly, both positive and negative sentiments can be observed with “Arousal” mood. “Arousal” incorporates any feeling that causes state change or prompts to rise and undertake any activity. The tweet “Govt today introduced a bill in to make provisions regarding recognition of, drawing opposition from the as well as the CPI(M) which staged a walkout calling it a ‘draconian and unconstitutional’ legislation” records a compound score of -0.3182 showing a negative “Arousal” sentiment. While the tweet “God bless you all. Now do the job well, Dems, it’s been way too long since it was done properly. Show them how it’s done” is a positive “Arousal” sentiment with a compound score of 0.95 . The upper and bottom figures demonstrate how sentiments differ for Congress and BJP. The most visually distinguishing aspects are seen in the 2 sentiments “Faith” and “Arousal”. BJP records a higher recording in “Faith” while Congress shows higher predominance of “Arousal”. The following figure illustrates tweet that belongs to both BJP and Congress. “Dominance” is still seen as the predominant mood. A sample tweet involving both parties : “Very close fight in . The difference between BJP and Congress not too many seats” — — clearly shows close and stiff competition between the two. Tweet Analytics This section, we structurize the blog into different areas of analytics and provide visual representations for comparisons. Frequency of different MoodsSentiment representation by Word CloudN-gram modelLocation-wise tweet distributionRetweet frequency distribution Frequency of different Moods sns.set(font_scale= )
df_BJP =  pd.read_csv(plot_path + files[i+ ])
df_Cong = pd.read_csv(plot_path + files[i+ ])
fields = [ , ] fig = mplt.figure()
ax1 = fig.add_subplot( )
ax2 = fig.add_subplot( )

size = fig.get_size_inches() * fig.dpi ax1.set_title( + labels[i] + , fontsize = , loc = )
ax2.set_title( + labels[i+ ] + , fontsize = , loc = ) g = sns.countplot(x= , data=df_BJP,  palette= ,  ax=ax1, order = df_BJP[ ].value_counts().index)
g = sns.countplot(x= , data=df_Cong,  palette= ,  ax=ax2, order = df_BJP[ ].value_counts().index)
mplt.show() 0.8 1 2 'tweet' 'mood' # Create a figure instance, and the two subplots 211 212 # get fig size in pixels "LokShobha Elections 2019 " " Sentiments" 8.5 'right' "LokShobha Elections 2019 " 1 " Sentiments" 8.5 'right' # Tell countplot to plot on ax1 with one party and ax2 with another party "mood" "PuBuGn_d" 'mood' "mood" "PuBuGn_d" 'mood' The different mood frequencies show public reactions towards both the parties before elections. “Dominance” mood dominates in case of both the parties followed by “Joy” mood. SNS countplot provides functionality to plot total frequency distribution of each individual mood which helps to compare within party different moods as well compare a specific mood for both the parties. For instance, for the following graphs of BJP and Congress shows the total number of tweets received for BJP is more than Congress and consequently each corresponding mood gets a higher percentage of tweets for BJP than Congress. Sentiment Representation by WordCloud The different kinds of tweet sentiments are represented by means of different WordClouds. WordClouds are ideal representatives of labelled sentiments as the most common words specific to a mood appear bigger and bolded than other less frequent words. WordClouds are fast and easy mechanism of representing the most relevant words for a theme or context. Its one of the most convenient ways to convey information visually appealing and engaging manner. Here 2 different sentiments of BJP “Faith/Support” and “Fear” are represented by 2 different WordClouds. The below code snippet represents all tweets specific to “Faith” sentiment through a WordCloud. df_faith = df[df[ ] == ]
wordcloud = WordCloud(width= , height= , max_font_size= ).generate(str(df_faith.tweet.values))
mplt.figure(figsize=( , ))
mplt.imshow(wordcloud, interpolation= )
mplt.title(labels[i] + , fontsize = )
mplt.xlabel( )
mplt.axis( )
mplt.show() 'mood' 'faith' 1600 800 200 12 10 "bilinear" "  Faith" 10 'Support/Faith' "off" From the figure below, you can see certain words of BJP like “modi”, “pm” are more frequent and the tweets exibit a tendency to “support”, “congratulate” , “thank” Prime Minister Narendra Modi for country’s development. Words like “vikas”, “development”, “honest team” , “agree”, “sath” , point out positive sentiment towards Modi government. Futher tweets that honour Prime Minister, are visible though words like “hon pm”, “dearest”, “fan”. One kind of negative sentiment like “Fear” for the BJP government is analysed and represented through a separate WordCloud. The “Fear” WordCloud shows a kind of negative feeling, fear/threat in people’s mind from opposition parties. df_fear = df[df[ ] == ]
wordcloud = WordCloud(width= , height= , max_font_size= ).generate(str(df_fear.tweet.values))
mplt.figure(figsize=( , ))
mplt.imshow(wordcloud, interpolation= )
mplt.title(labels[i] + , fontsize = )
mplt.xlabel( )
mplt.axis( )
mplt.show() 'mood' 'fear' 1600 800 200 12 10 "bilinear" "  Fear" 10 'Fear' "off" The “Fear” WordCloud has prominent bolded words like “worry”, “failure”, “mistrust”, “fighting” “worried” , “unexpected” , “wounded” that raises questions about doubts and uncertainities in people’s minds. Similarly, doing the sentiment analysis for Congress, 2 different moods one Positive — Joy and another negative -Sadness are represented by means of WordCloud. The “Sadness” WordCloud of Congress have clearly distinguishable words like “lost”, “refused”, “defeat”, “destroy”, “crying”, “missed”, “loot”, “slaps” that remark a sense of negative disheartened feeling in the tweets. Further the occurrence of most frequent words “Gandhi” , “Rahul” shows Rahul Gandhi as one of the foremost leaders of Congress. The positive tweet sentiments for Congress are represented by means of “Joy” WordCloud. Similar to the previous WordCloud “Rahul Gandhi”, “Congress” dominates the word cloud. df_joy = df[df[ ] == ]
wordcloud = WordCloud(width= , height= , max_font_size= ).generate(str(df_joy.tweet.values))
mplt.figure(figsize=( , ))
mplt.imshow(wordcloud, interpolation= )
mplt.xlabel( )
mplt.title(labels[i] + , fontsize = )
mplt.axis( )
mplt.show() 'mood' 'joy' 1600 800 200 12 10 "bilinear" 'Joy' "  Joy" 10 "off" Words like “win”, “good”, “congratulation”, “great”, “truth”, “happy”, “love”, “victory”, “dancing” , “grand” , “cheer” , “laugh” exhibits a strong “Happy” and “Joyous” public sentiment for Congress. N-gram Model The most popular bag-of words in NLP has n-gram models comprising of 1 -word text (Unigram) , 2-word text (Bi-gram) , 3-gram text (Tri-gram), where the number of occurrences of single word, side-by-side 2 words, side-by-side 3 words are counted and fed as feature-vectors to Text Classifiers (Naive Bayes, Maximum Entropy and Support Vector Machines). Word occurrences are counted after cleaning the tweets from hashtags, urls, emojis stopwords and character repetitions. This helps to extract most popular 1-word, 2-words, 3-words from tweet and construct feature vectors to determine the overall sentiment score of the text. ngrams = []
    num_words = len(tweet_words) i range(num_words -(n )):
        lookUpTweets = [] j range(i, i+n):
            lookUpTweets.append(tweet_words[j])

        ngrams.append(tuple(lookUpTweets)) ngrams freq_dict = {} ngram ngrams: (ngram freq_dict):
            freq_dict[ngram] += :
            freq_dict[ngram] = counter = Counter(freq_dict) counter word_counter_df = pd.read_csv(word_disb_path + uni_gram_files[i])
word_popular_df = word_counter_df.nlargest( , columns=[ ])
word_popular_df[ ] = word_popular_df.W1
fig = sns.barplot(x=word_popular_df[ ], y=word_popular_df[ ])
sns.set(font_scale= )
mplt.xlabel( , fontsize= )
mplt.ylabel( , fontsize= )
mplt.title( +  labels[i], fontsize= ) 
mplt.show(fig) sns.set(font_scale= )
word_popular_df[ ] = word_popular_df.W1 + + word_popular_df.W2
fig = sns.barplot(x=word_popular_df[ ], y=word_popular_df[ ])
sns.set(font_scale= )
mplt.xlabel( , fontsize = )
mplt.ylabel( , fontsize = )
mplt.title( + labels[i], fontsize = )  
mplt.show(fig) #splits up a sentence to 1-word, 2-word,3-words depending on input n : def get_ngrams (tweet_words, n) for in -1 for in return #calculates the frequency distribution of 1-word, 2-word,3-words : def get_ngram_freqdist (ngrams) for in if in 1 else 1 return #Unigram Frequency Distribution 25 'F' 'unigram_word' "unigram_word" "F" .3 "Unigram Words" 10 "Frequency" 10 "LokShobha Elections 2019 " 10 #Bigram Frequency Distribution 0.5 'bigram_word' "  " "bigram_word" "F" .5 "Bigram Words" 10 "Frequency" 10 "LokShobha Elections 2019 " 10 Unigram Frequency Distribution for Congress and BJP shows the most dominant 1-word occurring in the respective tweets. Similarly, Bigram Frequency Distribution for Congress and BJP shows the most dominant 2-word occurring in the respective tweets. Location wise tweet distribution A pie-chart is constructed for each of Congress and BJP by taking into account percentages of tweets from some of the known states of India. While both of them have larger percentages of tweets from unknown location and unknown states of India, New Delhi, Mumbai and Bangalore still dominates the percentages of tweets from India. location_df = combined_df[ ].value_counts()
filter_loc = location_df[location_df> ]
mplt.rcParams[ ] = mplt.title(labels[i])

patches, texts, autotexts = mplt.pie(
    filter_loc,
    labels=filter_loc.index.values,
    shadow= ,
    startangle= ,
    pctdistance= , labeldistance= , autopct= ,
)
mplt.axis( )
mplt.tight_layout()
mplt.show() 'location' 35 'font.size' 5.0 False 90 0.7 1.15 # with the percent listed as a fraction '%1.1f%%' 'equal' Retweet Frequency Distribution df_raw = pd.read_csv(full_statspath + stats_files[i]).dropna()
df_raw.drop_duplicates(subset= ,
                     keep= , inplace= )

df_raw_retweets = df_raw.nlargest( , columns=[ ])

x = df_raw_retweets[ ].values
y = df_raw_retweets[ ].values

colors = [ , , , , , , ]
fig, ax = mplt.subplots()

offset = k range(len(x)):
   ax.text(offset, k, x[k], color= , fontweight= , fontsize = )
   offset = offset+ width = ind = np.arange(len(y)) ax.barh(ind, y, width, color = colors)
mplt.title(labels[i])
mplt.xlabel( , fontsize = )
mplt.ylabel( , fontsize = )
mplt.show() "full_text" 'first' True 25 'retweet_count' "full_text" "retweet_count" 'b' 'g' 'r' 'c' 'm' 'y' 'k' 0.75 for in 'blue' 'bold' 7 1 0.75 # the width of the bars # the x locations for the groups 'Retweet Frequency' 7 'Tweets' 7 The popularity of tweets have been represented with the retweet count . Only first 25 unique retweets are selected. It's seen, that BJP tweets are much more frequent than Congress and ranges between 100–250 while average retweet frequency for Congress is 20–30. The retweet frequency along with the tweet text have been graphically displayed below. Conclusion This post mainly discusses about labelling tweets from known word dictionaries and rating them between -1 and 1 . It further compares BJP and Congress side by side considering tweet sentiments, frequency of different tweet sentiments, commonly used words in tweets (anar-grams 1–2 words), location of users who tweeted as well as the most popular tweets obtained from the retweet count. The following posts will cover on different ML techniques used for NLP, comparing them side by side with different metrics of accuracy like Precision, Recall and F1 Score as well as processing time to train the models. The election results for 2019 is still few months to go and the study hopes to find more interesting results through more weekly tweet crawls. References: Norms of valence, arousal, and dominance for 13,915 English lemmas. Warriner AB1, Kuperman V, Brysbaert M. https://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm Disclaimer Statement: The work analyses tweet of 2 prominent parties for the upcoming election. The author has no intention to create controversy in people’s mind or hurt anybody’s feelings or incite feelings of anger or hatred. Its purely done for academic, research and information purposes and somebody else might get different results on application of other techniques of analysis. Its an unbiased and impartial summary and does not discriminate/differentiate any individual or group.

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