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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

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1:1 Live Session

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Unlimited Chat with Mentor

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Task & Curated Resources

You will be certified for this mentorship program.

Regular Followups

Stay motivated with regular follow-ups.

Job Referrals

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Certified

You will be certified for this mentorship program.

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Sukashish Kaur | Working Professional

How can I improve my logic for solving questions ?

Logic can be developed only through more and more practice. After you have already done a certain amount of questions, you are able to identify some patterns. Not only practicing more questions, your goal while solving a question should be to identify all the possible approaches for the question and write them down along with thoroughly working on the most optimal solution. These quick notes will help in your revision as well.  

Ajitesh Chandra | Working Professional

How do I prepare for a data scientist interview?

Preparing for a data scientist interview requires a combination of technical knowledge, practical skills, and effective communication abilities. Here are some steps to help you prepare: 1. Review the job description: Understand the specific requirements and responsibilities of the data scientist role you are interviewing for. Identify the key skills and knowledge areas the company is seeking. 2. Brush up on core concepts: Refresh your understanding of fundamental concepts in data science, such as statistics, probability, linear algebra, and calculus. Familiarize yourself with common machine learning algorithms, data preprocessing techniques, and statistical methods. 3. Practice coding: Data scientists often need to write code to analyze and manipulate data. Make sure you are comfortable with programming languages commonly used in data science, such as Python or R. Practice coding exercises and solve data science-related problems using libraries like pandas, numpy, scikit-learn, or TensorFlow. 4. Dive into machine learning: Understand different machine learning algorithms, including supervised and unsupervised learning methods. Be prepared to explain how these algorithms work, their strengths and weaknesses, and when to apply them. Practice implementing and tuning machine learning models. 5. Work on real-world projects: Undertake practical data science projects to gain hands-on experience. This could involve working on datasets, conducting exploratory data analysis, applying machine learning algorithms, and evaluating model performance. Be ready to discuss these projects during your interview to showcase your practical skills. 6. Stay updated with industry trends: Follow the latest developments in the field of data science. Read blogs, research papers, and attend relevant conferences to stay abreast of current trends, emerging technologies, and best practices. 7. Prepare for technical questions: Expect technical questions on topics like data cleaning, feature selection, model evaluation, and regularization techniques. Practice answering questions related to statistical tests, experimental design, and A/B testing. Be comfortable discussing your approach to solving complex data science problems. 8. Enhance your communication skills: Data scientists need to effectively communicate their findings to both technical and non-technical audiences. Practice explaining complex concepts in a clear and concise manner. Be prepared to discuss your past projects and articulate your approach, methodology, and results. 9. Mock interviews and sample questions: Engage in mock interviews with friends, mentors, or other data scientists. Familiarize yourself with common interview questions and practice answering them. Some sample questions may cover data preprocessing, model selection, feature engineering, and deployment considerations. 10. Research the company: Gain a good understanding of the company's products, services, and data science initiatives. Research their data infrastructure, tools, and technologies they employ. This knowledge will help you tailor your responses to align with their specific requirements. Remember, interview preparation takes time and effort. Balance your technical knowledge with effective communication skills, problem-solving abilities, and a positive attitude. Good luck with your data scientist interview!

Bhavya Kalra | Working Professional

How do I build a strong resume?

List down all your competency and build your resume around it, it will help you to invision what is important and what is not. You have to carefully scan the jbo description for the role you are targeting.

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