673 Other courses in Durham

our Reiki Course BR1 Kent – Your Reiki Master Teacher Helped Write the National Occupational Standards For Reiki in the UK & Your Practitioner Training Is Approved By The Reiki Council -Contact me personally on +447533636939

This half-day workshop delivered face-to-face or online is designed for anyone in your organisation that wants to become a Neurodiversity Champion - someone who wants to educate and change the way that Neurodiversity is viewed in the workplace.

About this training course Business Impact: The main aim is to provide insight and understanding of data analytics and machine learning principles through applications. Field data is used to explain data-analysis workflows. Using easy to follow solution scripts, the participants will assess and extract value from the data sets. Hands-on solution approach will give them confidence to try out applicable techniques on data from their field assets. Data analysis means cleaning, inspecting, transforming, and modeling data with the goal of discovering new, useful information and supporting decision-making. In this hands-on 2-day training course, the participants learn some data analysis and data science techniques and workflows applied to petroleum production (specifically artificial lift) while reviewing code and practicing. The focus is on developing data-driven models while keeping our feet closer to the underlying oil and gas production principles. Unique Features: Eight business use cases covering their business impact, code walkthroughs for most all and solution approach. Industry data sets for participants to practice on and take home. No software or complicated Python frameworks required. Training Objectives After the completion of this training course, participants will be able to: Understand digital oil field transformation and its impact on business Examine machine learning methods Review workflows and code implementations After completing the course, participants will have a set of tools and some pathways to model and analyze their data in the cloud, find trends, and develop data-driven models Target Audience This training course is suitable and will greatly benefit the following specific groups: Artificial lift, production and facilities engineers and students to enhance their knowledge base, increase technology awareness, and improve the facility with different data analysis techniques applied on large data sets Course Level Intermediate Advanced Training Methods The course discusses several business use-cases that are amenable to data-driven workflows. For each use case, the instructor will show the solution using a data analysis technique with Python code deployed in the Google cloud. Trainees will solve a problem and tweak their solution. Course Duration: 2 days in total (14 hours). Training Schedule 0830 - Registration 0900 - Start of training 1030 - Morning Break 1045 - Training recommences 1230 - Lunch Break 1330 - Training recommences 1515 - Evening break 1530 - Training recommences 1700 - End of Training The maximum number of participants allowed for this training course is 20. This course is also available through our Virtual Instructor Led Training (VILT) format. Prerequisites: Understanding of petroleum production concepts Knowledge of Python is not a must but preferred to get the full benefit. The training will use the Google Collaboratory environment available in Google-Cloud for hands-on exercises Trainees will need to bring a computer with a Google Chrome browser and a Google email account (available for free) Trainer Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA. Highlighted Work Experience: At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe. Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements. Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics. Developed a workshop on digital oilfield approaches for production engineers. Patents: Principal inventor: 'Smarter Slug Flow Conditioning and Control' Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells' Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution' Worldwide Experience in Training / Seminar / Workshop Deliveries: Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&T, Louisiana State, U of Southern California, and U of Houston. He has conducted seminars, bespoke trainings / workshops globally for practicing professionals: Companies: Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others. Countries: USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela. Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

our Reiki Course BR1 Kent – Your Reiki Master Teacher Helped Write the National Occupational Standards For Reiki in the UK & Your Practitioner Training Is Approved By The Reiki Council -Contact me personally on +447533636939

Five half-day Leadership modules with mini work placed projects to bring the learning into action in the workplace.

Four half-day Leadership modules with mini work placed projects to bring the learning into action in the workplace.

Network virtualization training course description This course covers network virtualization. It has been designed to enable network engineers to recognise and handle the requirements of networking Virtual Machines. Both internal and external network virtualization is covered along with the technologies used to map overlay networks on to the physical infrastructure. Hands on sessions are used to reinforce the theory rather than teach specific manufacturer implementations. What will you learn Evaluate network virtualization implementations and technologies. Connect Virtual Machines with virtual switches. Explain how overlay networks operate. Describe the technologies in overlay networks. Network virtualization training course details Who will benefit: Engineers networking virtual machines. Prerequisites: Introduction to virtualization. Duration 2 days Network virtualization training course contents Virtualization review Hypervisors, VMs, containers, migration issues, Data Centre network design. TOR and spine switches. VM IP addressing and MAC addresses. Hands on VM network configuration Network virtualization What is network virtualization, internal virtual networks, external virtual networks. Wireless network virtualization: spectrum, infrastructure, air interface. Implementations: Open vSwitch, NSX, Cisco, others. Hands on VM communication over the network. Single host network virtualization NICs, vNICs, resource allocation, vSwitches, tables, packet walks. vRouters. Hands on vSwitch configuration, MAC and ARP tables. Container networks Single host, network modes: Bridge, host, container, none. Hands on Docker networking. Multi host network virtualization Access control, path isolation, controllers, overlay networks. L2 extensions. NSX manager. OpenStack neutron. Packet walks. Distributed logical firewalls. Load balancing. Hands on Creating, configuring and using a distributed vSwitch. Mapping virtual to physical networks VXLAN, VTEP, VXLAN encapsulation, controllers, multicasts and VXLAN. VRF lite, GRE, MPLS VPN, 802.1x. Hands on VXLAN configuration. Orchestration vCenter, vagrant, OpenStack, Kubernetes, scheduling, service discovery, load balancing, plugins, CNI, Kubernetes architecture. Hands on Kubernetes networking. Summary Performance, NFV, automation. Monitoring in virtual networks.

SIP security training course description A hands-on course covering SIP security. It is assumed that delegates already know SIP as this course focuses purely on the security issues in SIP IP telephony networks. Hands-on practicals follow each major theory session and include use of various SIP security tools such as vomit, sipp, sipsak and sivus amongst others. What will you learn Secure SIP networks Use various SIP security tools SIP security training course details Who will benefit: Technical staff working with SIP. Technical security staff. Prerequisites: SIP for engineers Duration 2 days SIP security training course contents SIP review SIP infrastructure and entities, example SIP session. Hands on Simple SIP network with and without authentication. SIP security attacks DOS attacks, infrastructure attacks, eavesdropping, spoofing, replay, message integrity. Hands on Basic SIP packet capture, infrastructure attacks. SIP tools SIP packet creation: Sivus, SIPsak, PROTOS, SFTF, SIP bomber, SIPp, Seagull, Nastysip. SIP packet generators: SIPNess, NetDude. Monitoring: Wireshark, Cain & Abel, Vomit, Oreka, VoiPong. Scripts and tools: SIP-Fun, Skora.net, kphone-ddos, sip-scan, sip-kill, sip-redirectrtp. Health of different tools. Hands on Generating SIP packets, rebuilding conversations from captured packets, password cracking. VPNs and SIP IPSec, AH, ESP, transport mode, tunnel mode, Pre Shared Keys, Public keys. Hands on SIP calls over IPSec. Secure SIP signaling SIP relationship with HTTP, Deprecated HTTP 1.0 basic authentication, HTTP 1.1 Digest authentication, S/MIME, SIPS, SIPS URI, TLS, DTLS, PKI infrastructures. Hands on SIP with TLS. Secure media streams SRTP, features, packet format, default encryption, default authentication, key distribution. S/MIME, MIKEY, SDP security descriptions. SIP security agreements. Hands on Analysing SRTP packets. Firewalls NAT traversal. Impact of firewall on infrastructure attacks. TLS and firewalls. SIP specific firewalls. Hands on SIP calls through a firewall.

HTTP streaming training course description This course looks at the delivery of video streams using HTTP adaptive streaming. Both MPEG DASH and HLS are investigated. Hands on sessions primarily involve using Wireshark to analyse streams. What will you learn Use Wireshark to analyse and troubleshoot HTTP video streams. Explain HTTP adaptive streaming works. Evaluate and compare MPEG DASH and HLS. Use tools to create HTTP adaptive streams. HTTP streaming training course details Who will benefit: Anyone working in the broadcast industry. Prerequisites: TCP/IP foundation for engineers Duration 2 days HTTP streaming training course contents What is HTTP streaming? The old way. Progressive downloads versus streaming. Why not UDP and RTP for delivery? Adaptive bit rate streaming. Standards. Hands on Base network setup. Using WireShark for HTTP streams. HTTP protocol stack IP, TCP, IPv6. HTTP. HTTP 1.0, HTTP 1.1, HTTP 2.0, HTTP header fields. HTML 5. Hands on Analysing HTTP. Adaptive bitrate streaming principles Chunks, fragments, segments. Manifest files. Encoding, resolution, bitrates. Addressing, relative and absolute URLs, redirection. When does the client switch streams? Switch points. Hands on Walk through of client behaviours on a stream. HTTP streaming architecture Server components, distribution components, client software. CDN, caching, multiple servers. Hands on Analysing CDN and Internet delivery. TCP and HTTP streaming interactions TCP ACK, TCP connections, unicast only. TCP flow control, TCP and performance. Hands on TCP window sizes. MPEG DASH Stakeholders, DASH architecture and model, codec agnostic, XML, Media Presentation Description, Media Presentation, segment formats. Hands on MPEG DASH analysis. HTTP Live Streaming and others Stakeholders. Media segments, media playlists, master playlists. Adobe HTTP dynamic streaming, Microsoft smooth streaming. Hands on Analysing HLS. Tools mp4dash, mp4fragment, libdash. Apple developer tools for HLS. Hands on Creating segmented content. Security HTTPS, encryption, content protection. Hands on Encryption analysis. Summary Choosing a streaming method. Impact of live versus VoD. Web sockets.

OTT TV for engineers course description This course covers OTT TV by primarily looking at the delivery of video streams using HTTP adaptive streaming. Both MPEG DASH and HLS are investigated. Hands on sessions involve using Wireshark to analyse streams as well as crafting segmented content. What will you learn Explain what OTT TV is, and how it works. Describe the OTT TV architecture. Use Wireshark to analyse and troubleshoot OTT video streams. Explain how HTTP adaptive streaming works. Evaluate and compare MPEG DASH and HLS. Use tools to create OTT TV adaptive streams. OTT TV for engineers course details Who will benefit: Anyone working in the broadcast industry. Prerequisites: TCP/IP foundation for engineers. Duration 2 days OTT TV for engineers course contents What is OTT TV? Brodeo providers vs ISPs. Progressive downloads versus streaming. Why not UDP and RTP for delivery? Adaptive bit rate streaming. Standards. Hands on: Base network setup. Using WireShark for HTTP streams. HTTP protocol stack IP, TCP, IPv6. HTTP. HTTP 1.0, HTTP 1.1, HTTP 2.0, HTTP header fields. HTML 5. Hands on: Analysing HTTP. Adaptive bitrate streaming principles Chunks, fragments, segments. Manifest files. Encoding, resolution, bitrates. Addressing, relative and absolute URLs, redirection. When does the client switch streams? Switch points. Hands on: Walk through of client behaviours on a stream. OTT TV streaming architecture Server components, distribution components, client software. CDN, caching, multiple servers. Hands on: Analysing CDN and Internet delivery. TCP and HTTP streaming interactions TCP ACK, TCP connections, unicast only. TCP flow control, TCP and performance. Hands on: TCP window sizes. MPEG DASH Stakeholders, DASH architecture and model, codec agnostic, XML, Media Presentation Description, Media Presentation, segment formats. Hands on: MPEG DASH analysis. HTTP Live Streaming and others Stakeholders. Media segments, media playlists, master playlists. Adobe HTTP dynamic streaming, Microsoft smooth streaming. Hands on: Analysing HLS. Tools mp4dash, mp4fragment, libdash. Apple developer tools for HLS. Hands on: Creating segmented content. Security HTTPS, encryption, content protection. Hands on: Encryption analysis. Summary Choosing a streaming method. Impact of live versus VoD. Web sockets.