Technical data is a good skill to learn if you want to become a flight information expediter, aircraft inspector, or engine repair supervisor. Here are the top courses to learn technical data:
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1. Data Engineer Technical Interview Preparation
While some companies consider the Data Engineer role to be a subcategory of the Software Development Engineer role, topics covered in Data Engineer technical interviews differ from those covered in Software Development Engineer interviews. This course focuses on those you will most likely encounter during your Data Engineer interview. Data Engineering interviews grew by 40% in 2020 and Data Engineering in general is one of the fastest growing job role. These numbers will likely continue to grow as companies invest in data-driven solutions. What you'll learnIn this course, you will learn how to prepare for the Data Engineer technical interview at FAANG companies. The lectures will guide you through the concepts you should focus on as a Data Engineer and provide you with practice problems after each topic to test your understanding. The course will cover the following topics: Problem Sense, Data Modeling, and Coding. The coding practice problem solutions will be written in SQL and Python. You will practice classic coding problems as well as how to handle batch and streaming data for ETL related interview rounds/questions. The course will conclude with general tips to remember throughout your interview process. Are there any course requirements or prerequisites?There is no prerequisites for this course, however any work experience as a Data Engineer will be helpful. Who this course is for: Students and professionals striving to land a Data Engineer position at a FAANG company...
2. Essential Non Technical Skills of Effective Data Scientists
Most data science training focuses only on key technologies like Python, R, ML etc. But real-world data science jobs require more than just technical acumen. As IT professionals rush to upgrade their current skill-set and become career ready in the field of data science, most of them forget the other part of skill development - Non-technical skills. These skills won't require as much technical training or formal certification, but they're foundational to the rigorous application of data science to business problems. Even the most technically skilled data scientist needs to have these soft skills to thrive today. This easy to follow course is created for recent Data Science graduates, beginners, new hires, Working Data Professionals, or any employee looking to boost their skills at the office and in the global workplace. I am sure these will help you to develop effective work habits that will help you succeed at your job, create a healthy work/life balance, and have a better understanding of your own personal strengths and how you work best. These nontechnical skills can help you convert your first data science job into a successful, lifelong career. By the end of this course you will be able to: Develop some crucial Non Technical skills, professional presence, and confidence in the workplace. Become a more effective communicator in the work environment. Ask better questions and increase your ability to come up with more and better ideas around how to effectively use data in any project. Evaluate your personal strengths (and weakness), and understand how those are best used at the individual and team levels when working in any Data Science Project. Create clear, specific, and actionable goals to improve your confidence at workplace. These Non Technical skills are those skills that get you hired, keep you focused, and help you survive and integrate into your global workplace community. Practicing and developing these skills will help separate you from the crowd of job applicants and scientists as the field grows. A Verifiable Certificate of Completion is presented to all students who undertake this course...
3. CCIE Data Center (v3.0) - Technical Classes
Exam Description: The Cisco CCIE Data Center (v3.0) PracticalExam is an eight-hour, hands-on exam that requires a candidate to plan, design, deploy, operate, and optimize complex Data Center networks. Candidates are expected to program and automate the network within their exam, as per exam topics below. The following topics are general guidelines for the content likely to be included on the exam. Your knowledge, skills and abilities on these topics will be tested throughout the entire network lifecycle, unless explicitly specified otherwise within this document. The exam is closed book and no outside reference materials are allowed.1. Data Center L2/L3 Connectivity (20%)1.1 Layer 2 technologies1.1. a Link Aggregation 1.1. a i vPC 1.1. a ii PortChannel1.1. b Tagging/Trunking1.1. c Static Path binding1.1. d Spanning Tree Protocol1.1. d i PVST1.1. d ii MST1.2 Routing Protocols and features1.2. a OSPF (v2 and v3)1.2. a i Authentication1.2. a ii Adjacencies1.2. a iii Network types and Area Types1.2. a iv LSA Types1.2. a v Route Aggregation/Summarization1.2. a vi Route Redistribution1.2. b ISIS1.2. b i Adjacencies1.2. b. i.1. Single area, single topology1.2. b ii Network types, Levels and Router types1.2. b. ii.1. NSAP addressing1.2. b. ii.2. Point-to-point, broadcast1.2. c BGP1.2. c i Path Selection1.2. c ii External and Internal Peering1.2. c iii Route reflectors and Route Server1.2. c iv Peer Templates 1.2. c v Multi-Hop EBGP1.2. c vi Route Aggregation/Summarization1.2. c vii Route Redistribution1.2. d BFD1.2. e FHRP1.3 Multicast protocols1.3. a PIM1.3. a i Sparse Mode1.3. a ii BiDir1.3. a iii Static RP, BSR, AutoRP, PhantomRP1.3. a iv IPv4 PIM Anycast1.3. a v IPv4 Anycast RP using MSDP1.3. b IGMP1.3. b i IGMPv2, IGMPv31.3. b ii IGMP Snooping1.3. b iii IGMP Querier2. Data Center Fabric Infrastructure (15%)2.1 Physical fabric components2.1. a Fabric Discovery2.1. b Controllers/Network Managers2.1. c Switches2.2 Fabric policies2.2. a Access Policies2.2. b Multi Tenancy2.2. c Monitoring Policies2.3 Tenant Policies2.3. a Application profiles and EPGs2.3. b Networking2.3. c Security2.4 Fabric Monitoring2.4. a Faults2.4. b Events2.4. c Health indicators2.4. d Audit Logs2.5 Virtual Networking2.5. a vSphere VDS3. Data Center Fabric Connectivity (15%)3.1 VRF lite3.2 L3Out 3.2. a OSPF 3.2. a i Authentication3.2. a ii Adjacencies3.2. a iii Network types and Area Types3.2. a iv Route Redistribution3.2. b BGP3.2. b i AS manipulation3.2. b ii External and Internal Peering3.2. b iii Route reflectors3.2. b iv Route Redistribution3.2. c Transit Routing3.3 Inter Fabric connectivity3.3. a Multi-Pod3.3. b Multi-Site3.3. c Virtual POD3.3. d remote Leaf3.4 Overlays3.4. a VXLAN EVPN4. Data Center Compute (15%)4.1 Compute Resources4.1. a UCSM Policies, Profiles and Templates4.1. b Hyperflex4.2 Compute Connectivity4.2. a SAN/LAN uplinks4.2. b Rack server integration4.2. c Port Modes5. Data Center Storage Protocols and Features (10%)5.1 FC and FCoE5.1. a Zoning5.1. b NPV/NPIV5.1. c Trunking5.1. d Portchannel5.1. e Load Balancing5.2 iSCSI5.2. a Authentication5.2. b Multipathing5.3 RoCE v2 over IP Networks6. Data Center Security and Network Services (10%)6.1 Security features6.1. a ACL's6.1. b First Hop Security6.1. c Port security6.1. d Private VLANs6.1. e Contracts6.2 RBAC6.2. a Radius6.2. b TACACS+6.2. c LDAP6.2. d AAA6.3 Network Services Insertion and Redirection 6.3. a Policy Based Routing6.3. b Policy Based Redirection6.3. c Inter VRF communication6.3. d Route Targets6.3. e Prefix Lists6.4 Services6.4. a Flow/Telemetry Export6.4. b SPAN6.4. c SNMP6.4. d Syslog 6.4. e DHCP6.4. f NTP/PTP6.5 Traffic management6.5. a Queueing6.5. b Policing6.5. c Classification/Marking6.5. d Scheduling6.5. e CoPP7. Data Center Automation and Orchestration (15%)7.1 Data center tasks using scripts (Python and Ansible)7.1. a Create, Read, Update, Delete using RESTful APIs7.1. b Deploy and modify configurations 7.1. c Statistics, Data Collection7.2 Data Center Automation and Orchestration using tools7.2. a DCNM 7.2. b UCSD7.2. b i Tasks...
Jobs that use Technical Data
Updated December 8, 2023