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I am Shubham Dokania, Ex-Research Scientist at Mercedes-Benz R&D. My main area of work includes, but not limited to, deep learning for computer vision and artificial intelligence.
https://shubham1810.github.io
More than 3 years experience as Data Scientist including strong foundation with python,big data, hadoop, aws, natural language processing and visualization tools. More than 5 years experience as software engineer. Always ready for the next challenge.
I've done tons of work on systems and backend development. Highlights of my work:
- Automated bare-metal deployment and management of Linux and Solaris systems
- Built a cloud compute platform with a Flask API, Python services, RabbitMQ for messing, and MongoDB for persistence, powered by VMWare vSphere
- Managed thousands of systems with Salt and integrated Salt into said cloud platform
- Operated distributed applications on AWS
- Tested/built/deployed all of the above with CI/CD
Solid background in the Information and Communication Technology field; experience acquired with top market Italian and European players (Telco and Enterprise Market). Specific competence on deployment and placement of value added ICT services and security systems.
Specific technical knowledge in System Integration for OSS (Operational Support Systems); deep knowledge in System and Network infrastructure Design and Planning (for both Physical and Virtualized environments)
Consolidated many-years project management and consulting approach to the customers.
Product marketing and IT architectural design, technical and business development pre-sales
Bid management
Project management on mission critical tasks
Operational procedures
Infrastructure design and planning
Hardware and Software selection and comparison
Solid knowledge of TELCO/ISP environment
O.S.: Linux, RHEL, SUN Solaris, HP-UX, AIX, MP/RAS
HA/Clustering: Linux-HA, OpenAIS/Corosync, Pacemaker, RHCS, DRBD, SUN Cluster, HP MC/SG
Net/Sys MGMT: HP OpenView, IBM Tivoli Netcool, ARUBA Networks AMP (formerly AirWave MP), Cisco CUOM, Zoho ManageEngine Suite, Zenoss, OpenNMS, ZABBIX, Nagios, Bigbrother, Hobbit, Net-SNMP
Performance MGMT: CA Concord eHealth, Cisco Management Software, Cisco NetFlow, Cisco NBAR, MRTG, Cacti
SLA MGMT: Grandsla, CA Business Service Insight (formerly Oblicore Guarantee)
Networking/Security: Ciso IOS, Alteon OS and SLB, pfSense, FreeRADIUS, OpenVPN, IPTables, BSD PF, IPF
SAN: FC, NFS, iSCSI, Brocade SAN Switch, IBM System Storage, Nexsan
Server Virtualization: VMWare vSphere, ESX, ESXi, Parallels Virtuozzo, Oracle Solaris Containers
Desktop Virtualization: VMWare View, Citrix VDI-in-a-Box (formerly Kaviza), VirtualBridges VERDE, Virtualcomputer NxTop (now part of Citrix)
Specialties: Able to find and develop innovative and ingenious solutions
Able to find good alternatives to market leader products
Integration of heterogeneous products
I am a passionate software engineer with a strong will to learn and improve myself continuously across a wide range of different areas.
My focus and favourite work would be in R&D on information retrieval, information extraction, natural language processing and machine learning with a big emphasis on data structures, algorithms and probability theory.
Experience with a great variety of clients has taught me to be a proficient, professional consultant and allowed me to develop excellent interpersonal skills.
I have a strong inclination for teaching, a passion which grew during my academic career where I tutored students and taught Summer preparation courses at the high school level. This interest continued to grow after University as I taught specific technical topics (mostly search related) to customers.
And last but not least I'm a Beach Volleyball player, Sport Event organizer in my home town and a crazy snowboarder.
Specialties: Semantic Search Engines, Architectural & Design patterns, Java designer & developer, Data Extraction from web sites, Natural Language Processing, XML
Conference sessions :
- "Content Discovery Through Entity Driven Search" - ECIR, Apr 2014 @Amsterdam
- "Multi Language Content Discovery Through Entity Driven Search" - Lucene/Solr Revolution, Nov 2014 @Washington DC
See the power of our Cluster Analysis tutors through glowing user reviews that showcase their successful Cluster Analysis learning journeys. Don't miss out on top-notch Cluster Analysis training.
“I am thrilled to write a review for my React mentor, Vardan. Working with Vardan has been an incredible experience as I learn and develop features on a relatively large React, Vite, and TypeScript codebase.
Vardan is an excellent communicator and a very smart engineer. From our first interaction, he quickly understood the problem context and immediately added value. His ability to break down complex concepts into easily understandable components has been instrumental in accelerating my learning curve.
Beyond his technical prowess, Vardan’s approachability and willingness to help have made a significant difference in my development journey.
I highly recommend Vardan to anyone looking for a knowledgeable, patient, and effective mentor in the React ecosystem. His expertise and dedication are truly remarkable.“
Daniel Meas / Jun 2024
Vardan Hakobyan
Cluster Analysis tutor
“Tyler helped me with the following points:
- understanding rust more and how to develop in a rust idiomatic way
- how to set up the correct project structure in rust
- he gave me some useful pointers on what to look into next and that the usage of dyn might not be necessary in some places
Thanks for the session and taking the time to explain things!“
Josef Seibl / Jun 2024
Tyler Green
Cluster Analysis tutor
“Tested mic and headphone before session and it worked.
Unfortunately during session mic and headphone is connected but the volume cannot be louder.
Didn't manage to solve the request due to technicality issue, however the mentor is nice enough to refund the money back to me.“
Prefrontal Learning Center / Jun 2024
Emmanuel Gbenga
Cluster Analysis tutor
“Angel is a good mentor that taught me step by step in my first lesson with Java, he gave the necessary tools for starting and setting my workplace, he also gave some useful advice for my first program in Java, most probably I will get other session with him.“
Nelson Garcia / Jun 2024
Ángel quintanilla
Cluster Analysis tutor
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We'll help connect you with a Cluster Analysis tutor that suits your needs.
Find the most suitable Cluster Analysis tutor by chatting with Cluster Analysis experts.
Arrange regular session times with Cluster Analysis tutors for one-on-one instruction.
Frequently asked questions
Learning Cluster Analysis effectively takes a structured approach, whether you're starting as a beginner or aiming to improve your existing skills. Here are key steps to guide you through the learning process:
Understand the basics: Start with the fundamentals of Cluster Analysis. You can find free courses and tutorials online that cater specifically to beginners. These resources make it easy for you to grasp the core concepts and basic syntax of Cluster Analysis, laying a solid foundation for further growth.
Practice regularly: Hands-on practice is crucial. Work on small projects or coding exercises that challenge you to apply what you've learned. This practical experience strengthens your knowledge and builds your coding skills.
Seek expert guidance: Connect with experienced Cluster Analysis tutors on Codementor for one-on-one mentorship. Our mentors offer personalized support, helping you troubleshoot problems, review your code, and navigate more complex topics as your skills develop.
Join online communities: Engage with other learners and professionals in Cluster Analysis through forums and online communities. This engagement offers support, new learning resources, and insights into industry practices.
Build real-world projects: Apply your Cluster Analysis skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using Cluster Analysis in practical applications not only boosts your learning but also builds your portfolio, which is crucial for career advancement.
Stay updated: Since Cluster Analysis is continually evolving, staying informed about the latest developments and advanced features is essential. Follow relevant blogs, subscribe to newsletters, and participate in workshops to keep your skills up-to-date and relevant.
The time it takes to learn Cluster Analysis depends greatly on several factors, including your prior experience, the complexity of the language or tech stack, and how much time you dedicate to learning. Here’s a general framework to help you set realistic expectations:
Beginner level: If you are starting from scratch, getting comfortable with the basics of Cluster Analysis typically takes about 3 to 6 months. During this period, you'll learn the fundamental concepts and begin applying them in simple projects.
Intermediate level: Advancing to an intermediate level can take an additional 6 to 12 months. At this stage, you should be working on more complex projects and deepening your understanding of Cluster Analysis’s more advanced features and best practices.
Advanced level: Achieving proficiency or an advanced level of skill in Cluster Analysis generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of Cluster Analysis, contributing to major projects, and possibly specializing in specific areas within Cluster Analysis.
Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in Cluster Analysis. Engaging with new developments, tools, and methodologies in Cluster Analysis is a continuous process throughout your career.
Setting personal learning goals and maintaining a regular learning schedule are crucial. Consider leveraging resources like Codementor to access personalized mentorship and expert guidance, which can accelerate your learning process and help you tackle specific challenges more efficiently.
The cost of finding a Cluster Analysis tutor on Codementor depends on several factors, including the tutor's experience level, the complexity of the topic, and the length of the mentoring session. Here is a breakdown to help you understand the pricing structure:
Tutor experience: Tutors with extensive experience or high demand skills in Cluster Analysis typically charge higher rates. Conversely, emerging professionals might offer more affordable pricing.
Pro plans: Codementor also offers subscription plans that provide full access to all mentors and include features like automated mentor matching, which can be a cost-effective option for regular, ongoing support.
Project-based pricing: If you have a specific project, mentors may offer a flat rate for the complete task instead of an hourly charge. This range can vary widely depending on the project's scope and complexity.
To find the best rate, browse through our Cluster Analysis tutors’ profiles on Codementor, where you can view their rates and read reviews from other learners. This will help you choose a tutor who fits your budget and learning needs.
Learning Cluster Analysis with a dedicated tutor from Codementor offers several significant benefits that can accelerate your understanding and proficiency:
Personalized learning: A dedicated tutor adapts the learning experience to your specific needs, skills, and goals. This personalization ensures that you are not just learning Cluster Analysis, but exceling in a way that directly aligns with your objectives.
Immediate feedback and assistance: Unlike self-paced online courses, a dedicated tutor provides instant feedback on your code, concepts, and practices. This immediate response helps eliminate misunderstandings and sharpens your skills in real-time, making the learning process more efficient.
Motivation and accountability: Regular sessions with a tutor keep you motivated and accountable. Learning Cluster Analysis can be challenging, and having a dedicated mentor ensures you stay on track and continue making progress towards your learning goals.
Access to expert insights: Dedicated tutors often bring years of experience and industry knowledge. They can provide insights into best practices, current trends, and professional advice that are invaluable for both learning and career development.
Career guidance: Tutors can also offer guidance on how to apply Cluster Analysis in professional settings, assist in building a relevant portfolio, and advise on career opportunities, which is particularly beneficial if you plan to transition into a new role or industry.
By leveraging these benefits, you can significantly improve your competency in Cluster Analysis in a structured, supportive, and effective environment.
Personalized Cluster Analysis mentoring through Codementor offers a unique and effective learning approach compared to traditional classroom learning, particularly in these key aspects:
Customized content: Personalized mentoring adapts the learning material and pace specifically to your needs and skill level. This means the sessions can focus on areas where you need the most help or interest, unlike classroom settings which follow a fixed curriculum for all students.
One-on-one attention: With personalized mentoring, you receive the undivided attention of the tutor. This allows for immediate feedback and detailed explanations, ensuring that no questions are left unanswered, and concepts are fully understood.
Flexible scheduling: Personalized mentoring is arranged around your schedule, providing the flexibility to learn at times that are most convenient for you. This is often not possible in traditional classroom settings, which operate on a fixed schedule.
Pace of learning: In personalized mentoring, the pace can be adjusted according to how quickly or slowly you grasp new concepts. This custom pacing can significantly enhance the learning experience, as opposed to a classroom environment where the pace is set and may not align with every student’s learning speed.
Practical, hands-on learning: Mentors can provide more practical, hands-on learning experiences tailored to real-world applications. This direct application of skills is often more limited in classroom settings due to the general nature of the curriculum and the number of students involved.
Personalized mentoring thus provides a more tailored, flexible, and intensive learning experience, making it ideal for those who seek a focused and practical approach to mastering Cluster Analysis.