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Long Term Mentorship

1-on-1 long-term mentorship with your chosen mentor to guide you to the career you deserve

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1:1 Long Term Mentorship

30%

Cheaper

Compared to any 6 month course

4x

Results

As compared to any online courses

50%

Faster

Get a results within 6 months instead of years

600+ mentors are just a Free Trial Session away!

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No need to Struggle Alone Anymore

Long term mentorship gets fully covered

1:1 Live Session

Boost your progress with frequent 1:1 sessions.

Unlimited Chat with Mentor

Get the right advice from your mentor via Chat.

Task & Curated Resources

You will be certified for this mentorship program.

Regular Followups

Stay motivated with regular follow-ups.

Job Referrals

Get referrals to companies by mentor community.

Certified

You will be certified for this mentorship program.

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Connect with our star mentors, distinguished leaders in their fields, to receive personalized mentorship.

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Niyati Kapoor | Working Professional

How can I switch from SDE to Data Science role?

Switching from a Software Development Engineer (SDE) role to a Data Science role requires a combination of acquiring new skills and leveraging your existing technical expertise. Here's a roadmap to help you make the transition: 1. Assess your current skills and knowledge: Evaluate your existing programming skills, especially in languages commonly used in data science like Python or R. Identify the areas where you have a strong foundation, such as algorithms, data structures, and software development practices. These skills will be valuable in your transition. 2. Understand the field of data science: Gain a solid understanding of the key concepts, techniques, and tools used in data science. Familiarize yourself with statistics, machine learning algorithms, data preprocessing, data visualization, and other relevant topics. Online courses, tutorials, and textbooks can be valuable resources for learning. 3. Learn programming languages and libraries: Develop proficiency in Python or R, as these languages are widely used in data science. Familiarize yourself with data science libraries and frameworks like pandas, numpy, scikit-learn, TensorFlow, or PyTorch. Practice coding exercises and work on data science projects to apply your knowledge. 4. Gain knowledge in statistics and mathematics: Data science heavily relies on statistical analysis and mathematical modeling. Strengthen your understanding of statistical concepts such as hypothesis testing, regression analysis, and probability. Brush up on linear algebra and calculus, as they are foundational for certain machine learning algorithms. 5. Take data science courses and certifications: Enroll in online or in-person courses specifically tailored to data science. Platforms like Coursera, edX, and Udacity offer a wide range of courses on statistics, machine learning, data analysis, and data visualization. Completing reputable certifications can also boost your credibility. 6. Work on data projects: Start working on personal data science projects to gain hands-on experience. Seek out datasets from open data sources or Kaggle, and apply your knowledge to analyze and derive insights from the data. These projects will help you build a portfolio and demonstrate your practical skills to potential employers. 7. Network with data scientists: Engage with professionals in the data science field to expand your knowledge and make connections. Attend data science meetups, conferences, and online forums. Networking can provide valuable insights, mentoring opportunities, and potential job leads. 8. Customize your resume and cover letter: Tailor your resume and cover letter to highlight relevant skills, projects, and experiences that align with data science. Emphasize any data-related work or projects you have done during your SDE role. Highlight transferable skills such as problem-solving, analytical thinking, and teamwork. 9. Apply for data science internships or junior positions: Consider applying for internships or entry-level data science positions to gain practical experience. These roles can help you bridge the gap between your SDE background and a full-fledged data science role. Be open to starting at a junior level and gradually progressing as you gain more experience. 10. Prepare for interviews: Familiarize yourself with common data science interview questions and practice answering them. Be ready to discuss your projects, your approach to solving data problems, and how you have used your programming and problem-solving skills in a data-driven context. Leverage your software development experience to showcase your ability to implement solutions efficiently. Remember, transitioning to a data science role may take time and effort. Continuously build your skills, stay updated with industry trends, and be persistent in your job search. With the right combination of technical knowledge and practical experience, you can successfully switch from an SDE role to a data science role.

Ansh mishra | Fresher

How to find mentors in the software industry ?

First you need to understand in what areas you want a mentor and what are your expectations. You may be a engineer, you might want to grow in your domain, or switch to a different role like product or manager. So you need to understand in what areas you want to be mentored and as a the next step you need you figure out whom to reach out to. Filter out the people you have been inspired by, it can be in your workplace or outside in forums like linkedin or someone whom you feel comfortable with, you need not have to right away choose them. If you many options you can try to set 1:1's with each of them and understand who would suit you best. Keep in mind you can change your mentors after some time, when you feel like you have learnt their skills and you right away know what would be their approach or answer before you reach out to them.

Mohammad Owaiz Shaik | Working Professional

How do I become a cloud engineer?

To become a cloud engineer, you can follow these steps: Obtain a relevant degree or certification: Consider pursuing a degree in computer science, information technology, or a related field. Alternatively, you can acquire certifications like AWS Certified Solutions Architect or Microsoft Certified: Azure Solutions Architect. Gain experience with cloud technologies: Familiarize yourself with popular cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). Learn about cloud computing concepts, virtualization, networking, and storage. Develop programming and scripting skills: Learn programming languages commonly used in cloud environments, such as Python, Java, or PowerShell. Acquire scripting skills to automate tasks and manage cloud resources efficiently. Learn infrastructure as code (IaC) tools: Gain proficiency in tools like Terraform or AWS CloudFormation, which enable you to define and manage infrastructure using code. Gain hands-on experience: Practice deploying and managing cloud resources. Set up virtual machines, containers, databases, and networking configurations in a cloud environment. Explore different services offered by cloud providers. Expand your knowledge: Stay updated with the latest developments in cloud computing, attend webinars, join communities, and engage in online forums. Continuously learn about new services, best practices, and emerging trends. Showcase your skills: Build a portfolio of projects or contribute to open-source projects related to cloud computing. Demonstrate your ability to design, implement, and manage cloud infrastructure effectively. Networking and collaboration: Connect with professionals in the field through networking events, conferences, or online communities. Collaborate with others on cloud-related projects to gain insights and expand your knowledge. Keep learning and adapting: Cloud technologies evolve rapidly, so it's crucial to stay curious and embrace continuous learning. Seek opportunities to acquire new skills, explore different cloud services, and adapt to changing industry demands. Seek cloud engineer positions: Once you feel confident in your skills and experience, start applying for cloud engineer roles. Tailor your resume to highlight your cloud expertise and showcase your projects. Prepare for interviews by studying common cloud-related questions and practicing your responses. Remember, becoming a cloud engineer is an ongoing journey of learning and practical application. Stay dedicated, keep building your skills, and embrace the opportunities that come your way.

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