Quais são as dicas mais importantes de entrevista de ciência de dados?

I believe the key to excelling in data science interviews is a blend of technical proficiency and practical application. Demonstrate your mastery of statistical methods, coding skills, and algorithmic understanding. Equally important is showcasing how you apply these skills to solve real-world problems, emphasizing critical thinking and creativity. Articulate your thought process clearly and engage with the problem-solving aspect of the questions. Also, displaying a keen interest in continuous learning and adaptability is essential in this rapidly evolving field.

Putting the record straight: “Data science interviews absolutely just not about modelling and algorithms”. Having said that, that also doesn’t mean one has to stop putting effort in that area. Idea of saying, data science is a vast field, which includes technical aspects like data pulling, Exploratory data analysis, modelling, visualisation, cloud based model deployment, model monitoring. On other hand one can surely not overlook behavioural aspects like collaboration, innovation, creativity etc. One also has to have a strong grip over business. In nutshell, data science is not a cakewalk,one has to have many angles approach and confidence on work done.

One thing that can help you stand out in a Data Science interview is to Contextualise your answers according to your audience. Know the company and train yourself to answer in the context. For a tech focused organisation emphasise on how you utilised your skills across projects on things like 1. to create a scalable and reusable model resources/frameworks , 2. reduced latencies in output , 3. Early warning systems for model deterioration For a Business focused organisation your emphasis should be on what you did to -1. refine the problem from a business challenge to a Data Science problem statement . 2. Build an explainable model 3.Potential Impact and Tests Designs to validate claim. 4.Translate Outcomes without Jargons

Preparing for a Data Science interview would be crucial if you sit for a long hour with a book. the best way to prepare for your interview is to Teach someone as a Mentor to enhance your confidence and master that skill. I come from Non-IT background and Transformed my career in Data Science through a mentor and mastered skills like Project presentation, Concept Exploration, and ATS Resume building. Here is the impact difference : Preparing for any Interview as playing the role of mentor will lead you to master the skills but by just normal preparation you will learn the things, while Learning and mastering are two things. 2. Keep things before an Interview: a. ATS Resume b. Project Ready c. Basic Foundation Clear d. Stats & Probablity

Best way for preparing for a Data Science interview is to be good at basics like Python, ML Algorithms. Focus and commitement are the keys for getting success.

What always gives me confidence to hire a candidate for a Data Scientist role is the ability to explain clearly how two to three algorithms work below the hood , how they are evaluated and used in a project. It could be some of the simpler ones (Decision Tree / RF / XGboost) but this gives me the confidence that the person has not tried to skim the surface of all common algorithms / memorized some FAQs. For specific Deep Learning roles I have to change my focus a bit towards some common architectures, handling np arrays, GPU usage etc.

Practice coding on whiteboard, as this is a common format for technical interviews. Be comfortable with data manipulation libraries and have proficiency in programming language. Practice solving data science problems. Platforms like LeetCode, HackerRank, or Kaggle provide a variety of challenges that can help you improve your problem solving skills.

Having domain knowledge in the industry you're interviewing for is crucial. Understanding algorithms alone is insufficient; you also need to grasp the specific context in which models/algorithms can enhance business operations.

Be prepared to write code during the interview. Practice coding by hand or in a simple text editor to simulate interview conditions. Websites like LeetCode or HackerRank can be useful for practicing coding problems. Understand the commonly used algorithms in data science, their use cases, and their strengths and weaknesses. Be prepared to explain why you would choose one algorithm over another in a given scenario.

Make sure you have a solid understanding of the fundamental concepts of data science, including statistics, machine learning, and data visualization.

Although it is often recommended that no specific tool is necessary, the first round online tests of many companies require you to be proficient in one. That's because the time limit requires one's hold over all the nooks and crannies of the tool/ query/ spreadsheet. For example if you intend to use Excel to analyse the provided dataset, be aware of filtering, formulae, sorting, ranking. The question maybe, Given a restaurant dataset find the 3rd most contributing main dish from Chinese restaurants in Suburb A. One can only tackle this question if one can filter the data first and apply a pivot. Or generate a chart. One simply cannot hope to wing it during the test. So, choose a vis tool/ SQL editor/ language and get practicing.

Practice, practice, practice: The more you practice answering data science interview questions, the more confident and prepared you'll be during the actual interview.

Be ready to answer technical questions that test your understanding of data science concepts. This might include explaining how certain algorithms work, discussing data cleaning techniques, or solving case studies. Having a portfolio of projects can be a great way to demonstrate your skills. Be prepared to discuss your projects, the challenges you faced, the techniques you used, and the outcomes. Data science can be complex, but being able to explain your work to non-technical stakeholders is crucial. Practice explaining complex concepts in a simple, concise manner.

While it’s a brief term but the in depth techniques on data analysis is add on. How to approach and exploring multiple ways of using structured or unstructured data shall help the knowledge further.

To excel in a data science interview, it's crucial to start with a strong understanding of the fundamental concepts. But beyond that, a tailored approach is key. Before the interview, invest time in researching the company. Understand the nature of their data challenges, the specific problems they face, and the metrics they likely use for evaluation. Familiarize yourself with these elements to align your knowledge and experience with their needs. This preparation will enable you to engage deeply during the interview, discussing relevant issues with confidence and insight.

Knowing about the company will always help you in the interview. I was once in an interview with this company - Dunhumby. I did a good research about the company , about the work they do and I did a good research about the job profile and role and department. And to my surprise most of the interview questions were revolving around the theme of how they deal with data, how they do business and the requirements mentioned in the JD. Hence knowing a company will broaden not only your domain knowledge but also your perspective to accurately answer questions in the interview.

Besides technical skills, interviewers often look for candidates who fit the company culture. Be prepared to answer questions about your work style, how you handle conflicts, and past experiences in team settings. Conduct mock interviews with friends or mentors to get feedback on your answers, communication style, and body language. Data science is a rapidly evolving field. Stay informed about the latest trends, tools, and best practices. While technical skills are important, it's also essential to let your personality shine through. Be honest about what you know and don't know.

This is by far the most important aspect. Does the company even have data you are interested in? Are you taking over and maintaining processes or will you be setting everything up from scratch. Depending on which data stage the company is at your day to day will look vastly different.

Before a data science interview, it's essential to learn about the company to demonstrate your interest and understanding. Start by exploring the company website to grasp their mission, products, and recent news. Check social media platforms and news articles for additional insights into their culture, recent developments, and employee experiences. Understanding the company's goals and values will enable you to tailor your responses effectively during the interview and showcase how your skills align with their needs.

To prepare for a behavioral interview, start by identifying key competencies the employer is likely to assess, such as problem-solving, teamwork, or communication skills. Reflect on past experiences that demonstrate these competencies and prepare specific examples to share during the interview. Use the STAR method (Situation, Task, Action, Result) to structure your responses, focusing on the actions you took and the results achieved. Practice articulating your experiences concisely and confidently, emphasizing how you applied relevant skills and handled challenges effectively. Finally, research the company culture and values to align your responses with their expectations and demonstrate your fit for the role.

If you explain how you act in specific situations, you give the interviewer the chance to understand your attitude. In a lot of cases interviewers are not looking for the perfect answer to a technical question, but they want to understand if you have a problem-solving and growth mindset, meaning that you have the motivation and ability to solve a problem, which you haven’t seen before. The sentence “hire for attitude, not for skills” becomes more and more important as our technical landscape is evolving quickly. So it’s important that you can demonstrate that you have the right attitude.

Preparing for behavioral questions in an interview is crucial because these questions aim to assess your past experiences, behaviors, and how you handle various situations. Here's a guide on how to effectively prepare for behavioral questions: 1. Understand the Purpose 2. Identify Key Competencies 3. Create STAR Stories 4. Review Common Behavioral Questions 5. Use Diverse Examples 6. Quantify Results 7. Be Honest and Positive 8. Practice with a Friend or Mentor 9. Self-Reflection 10. Research the Company's Culture 11. Stay Calm and Positive 12. Continuous Improvement

In addition to technical questions, you may also be asked behavioral questions about your work style, teamwork skills, and problem-solving abilities. Be prepared to give specific examples of how you've handled similar situations in the past.

Prepare thoughtful questions for the interviewer about the team, projects, and challenges in the role. This shows your interest and engagement.

Confidently showcase your proficiency in understanding, solving, and transparently communicating complex findings in alignment with business requirements. Demonstrate your confidence by staying informed about industry trends, maintaining a continuous learning mindset, and openly discussing your approach to overcoming challenges in data-driven projects during the interview.

Communicate effectively: Data science is a collaborative field, so being able to communicate your findings and insights effectively is just as important as technical skills. Practice explaining complex concepts in simple terms.

Confidence and honesty contribute significantly to your success in an interview. While confidence helps you present yourself positively and professionally, honesty establishes trust and sets the foundation for a successful and sustainable working relationship. Combining these qualities creates a compelling and authentic impression, increasing your chances of securing the job and thriving in your role.

During the interview, take the opportunity to ask thoughtful questions about the company's data science initiatives and culture to demonstrate your interest and engagement in the field.

What usually sets data scientists apart is their ability to focus on business impact in addition to technical expertise. Show the interviewer that you have business acumen and relevant domain knowledge. Additionally, highlight your collaboration skills, as data science projects are rarely done in isolation. Emphasize your understanding of ethical considerations in AI development and deployment, given the increasing power of data.

In today's competitive job market, personal connections and influence often eclipse traditional qualifications. Unconventional strategies for landing your dream job include crafting a Networked Résumé, building strategic alliances at events, subtly persuading hiring managers, optimizing your LinkedIn profile, strategically presenting your résumé at executive-filled coffee shops, and cultivating a Thought Leader Persona through blogging and social media. While merit remains crucial, these tactics underscore the significance of strategic networking and influence in unlocking career opportunities. So, let's toast to a career built on connections, acknowledging that success often involves knowing which rungs to skip on the career ladder.

Brush Up on Puzzles: Puzzles are often used to assess problem-solving skills. Practice solving them and explain your thought process during the interview. Prepare for Case Studies: Case studies test your approach to real-life problems. Practice different case studies and focus on a structured problem-solving approach.