Pratique du Shadowing: Data Science Interview Prep: 3 Key Resources You Need to Know - Apprendre l'anglais à l'oral avec la vidéo
Création de la leçon...
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Hey guys, welcome back to my channel.
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In this video, I will do another Q&A session to answer questions I did not get the chance to answer in the last video.
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As I mentioned in the last video, my Q&A session will be too long if I answer all questions in a single video, so I split it into two videos.
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Last time, we focused on the number one voted question, which was how to land a more senior data scientist job.
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What would be different from preparing for entry and mid-level role
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if you want to hear my answer to
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that question i have a whole video focusing on it
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that you can check out to learn more in this video
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i am going to cover in four more questions from you
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guys these questions range from interview preparation resources to broader career
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advice thanks again to everyone who submitted questions let's get started
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our first question deals with resources for interview preparation the question
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is how to prepare for product analytics data scientist positions what preparation strategy to use what resources to use
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and what depth of knowledge required for questions like statistics probability product sense
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and python there are actually several great questions here and i'm going to briefly answer all of them
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in terms of the resources i recommend i want to point
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you to my blog on the resources i use for interview
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preparation you can find the link in the description below this blog has all the resources I used.
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It also categorizes the resources based on different subjects, so you can easily find what you are looking for.
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If all of these resources seem a bit overwhelming, don't panic.
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You don't need to learn all of them.
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Depth is more important than breadth.
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Just pick one or two resources in each category and get started, and use your own preferences to make your choices.
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If you like reading, pick a book.
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If you prefer watching, pick YouTube videos or an online course.
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Preparation is is a lot easier if you work with your natural preferences.
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In terms of the depth of knowledge required, it's hard to provide a standard because it varies from coming to coming.
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The best way to get the sense of the level of difficulty is to collect some sample interview questions online.
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Gastor is usually a very helpful resource in this regard.
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However, if you cannot find any sample questions for coming online,
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you can also communicate with the recruiter and ask if they can send you more detailed instructions.
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While some people hesitate to ask a recruiter for anything, remember that this is their job.
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They are motivated to help you.
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You can even consider hopping on a call with a recruiter to ask specific questions.
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This advice also applies to the part of the question that asks if we need data structures and algorithm for Python questions.
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Some comments required it and others don't.
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So it's helpful to clarify with a recruiter.
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You can even ask for sample Python questions from the past.
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If you want more help with Python questions, I made a blog about it with my friend Rob that you can check out.
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Just to clarify though, the target audience is data scientists' machine learning.
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For product data scientists, the blog makes it clear that it's typically the first two types coding questions.
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I also have a video on this topic that may be helpful.
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That's all I have to say about this question.
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Hopefully you'll find it helpful.
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Let's Let's move on to the next question in our Q&A
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This question is from Nora and deals with A-B testing The question is, how to have a deep understanding of A-B testing
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and past product case interviews that involve it when my work does not provide opportunities for doing A-B testing?
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Thanks for your question, Nora
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I can recommend three great resources for deepening your understanding of A-B testing Udacity's A-B testing course
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my own videos on the topic and the book Trustworthy Online Control Experiments A Practical Guide to AP Testing Specifically,
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I suggest tackling those resources in that order
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The Udacity course will cover the fundamentals Then watch my videos and read the book to deepen your understanding
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The book I recommend is a great comprehensive resource
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but you may find that it's not as in-depth as you would like However, the book does a great job of recommending resources for more advanced topics,
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such as how to do geo-based randomization.
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With these resources, you can advance your understanding of AP testing, even if your work does not give you opportunities.
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That's my answer to this question.
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Moving on to the next one.
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The next question says, I found follow-up questions from case interviews especially hard.
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Are there any ways for me to prepare for those?
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Thanks to Lucy for asking this.
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Follow-up questions can be quite difficult and I find that they're especially hard for people who rely on frameworks
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The fact is that most of the time interviewers can tell if a candidate is following a framework
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Sometimes they will purposely try to destructure you To avoid getting derailed,
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when you practice you want to be able to not only express your ideas but also to defend them
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You need to train yourself to think independently and critically Therefore, to answer this question about answering follow-up questions,
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I want to briefly talk about how to leverage frameworks and how to develop your critical thinking skills.
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First of all, I'm not saying frameworks are totally bad.
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Far from it.
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Frameworks are helpful when you get started and don't know how to answer certain questions.
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However, as you learn more, you're also going to have your own ideas.
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This may come from your experience as a customer or your criticism of others' ideas or frameworks.
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Incorporating your own ideas into answers is how you make your answers unique and train yourself to think independently.
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Now, I will use myself as an example.
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When I first started learning how to crack case interviews I felt
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that the questions were
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so hard I struggled to come up with a single idea for a long time
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so I did some research by reading books and blog posts
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and watching YouTube videos and learned how others would answer the question at
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that stage I thought that the people were
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so brilliant to be able to come up with those ideas
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but as I learned more I I began to feel
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that some of the answers did not make much sense to me For example, in some interviews, when asked how to diagnose if a metric shifted to a negative direction
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The candidate uses the framework to talk about different factors without even clarifying how large the change is
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and if the change is sudden or gradual
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Another example is people recommended looking at 10 metrics from an A-B test And I wondered if this is even feasible
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I never monitored 10 metrics in a project in my own work experience
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so that made me question the feasibility of that answer
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So those answers gave me a great starting point
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but I was able to use my own experience
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and understanding to make them better I developed my own personal
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frameworks to answer certain questions You can find some of them in this video
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By taking the time to think through your own personal knowledge and develop more unique answers to questions
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you will be far better equipped to defend your answers and respond to follow-up questions
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okay we have one more question
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or rather two more questions for this q a session these
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are from abushak he first asks based on your experience what
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things would you suggest someone focus on in the early stage of their data science career
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which if learned earlier would benefit throughout the career
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or at least make sense easier in the long run this is a great question
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The two things I would say are the most important are communication and technical skills Without good communication skills, putting forth your ideas, getting noticed,
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and eventually advancing your career will all be much harder.
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These skills are vital to your career and you can make yourself stand out if you focus on them early.
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How do you develop good communication skills though?
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It takes practice, but I have a few suggestions
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that can help you work on this vital skill first take
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opportunities to present your work practice is key for developing any skill whether it be in school
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or the workplace when you have a chance to present take it
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when you do present your work the preparation you do is
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critical prep time gives you a chance to think about how to communicate effectively
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and develop strategies feedback from the audience is also important.
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Reflecting on the feedback and the question you get from the audience helps you with improving your presentation.
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Developing good communication skills also means being aware of what communication is.
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Good communication is not just about talking well.
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Communication is also about listening.
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Aim to be a good listener.
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Listen to others' ideas and concerns.
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Although everyone on a team or at the company often has the same overall goal, they often have different ideas about how to get there or how to execute.
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You can learn from their ideas.
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Being able to really listen is crucial to communication, but it's also a skill many people neglect.
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So it is smart to learn it early.
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Besides communication skills, technical skills are another thing I would encourage you to work on early in your career.
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Technical skills obviously allow you to do your job and are therefore critical.
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Common technical skills for data scientists include statistics, coding, SQL, machine learning, and data analytics.
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You should feel comfortable with those technical skills because other people rely on you to provide data-informed decisions.
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Besides those necessary skills, I would also suggest data scientists learn more software engineering skills.
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Data scientists work closely with engineers, so knowing the fundamentals of software engineering can prove extremely helpful.
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For example, understanding things like the software development life cycle, the importance of writing clean and modular code, the importance of writing tests,
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the difference between web and mobile development, and data quality by logging on front-end versus back-end.
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Knowing the software engineering fundamentals can give you a distinct advantage as a data scientist.
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That's my answer to the first question.
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Abyshek also asked, you and Jesse talk about the importance of market reviews in the preparation process.
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Can you suggest some places where one can do such things?
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I mean, if there are any kind of study groups or something like that.
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Well, the short answer is that I'm not aware of such a community.
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Actually, I have thought about creating one before, and I did some research about it.
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What I found was that it needs time and effort to maintain a free community so that members follow certain community rules,
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such as no advertisements, only post relevant information,
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etc. So the challenging part was about effectively managing such a community so that everyone benefits by being part of it.
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If you know anyone who would be interested in being a moderator
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or community manager I'd be more than happy to advocate it on my channel
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and encourage people to join It is an excellent suggestion Alright, that's all the questions I have time for in this video Please let me know
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if you think these Q&A sessions are helpful If so, I can do more videos like this every month or every few months I would appreciate your feedback Finally,
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don't forget to subscribe to my channel if you haven't done already There is more content to come.
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I will see you guys soon.
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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale de niveau B2 s’appuie sur la vidéo « Data Science Interview Prep: 3 Key Resources You Need to Know ». Les mots qui reviennent le plus souvent : answer, skill, resource, communication, scientist. Cette vidéo contient 145 phrases et 2002 mots à répéter en shadowing. La partie parlée dure 11:22. Le locuteur parle à un rythme naturel d’environ 176 mots par minute, proche d’une conversation courante. 86 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer.
Vocabulaire clé de cette vidéo
Les 15 mots les plus avancés de la vidéo, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| recruit verbe | /ɹɪˈkɹut/ | recruter, enrôler |
| clarify verbe | /ˈklæɹ.ɪ.faɪ/ | clarifier |
| metric adjectif | /ˈmɛt.ɹɪk/ | métrique |
| deepen verbe | /ˈdiːpən/ | approfondir |
| analytics nom | /ˌæn.əˈlɪt.ɪks/ | analytique |
| independently adverbe | /ˌɪndɪˈpɛndəntli/ | indépendamment |
| preference nom | /ˈpɹɛf(ə)ɹ(ə)ns/ | préférence |
| categorize verbe | /ˈkætɪɡəˌɹaɪz/ | classer, classifier |
| derail verbe | /ˌdiːˈɹeɪl/ | dérailler |
| diagnose verbe | /ˈdaɪ.əɡˌnoʊs/ | diagnostiquer |
| feasibility nom | /ˌfiːzɪˈbɪlɪti/ | faisabilité |
| moderator nom | /ˈmɒdəˌɹeɪtə(ɹ)/ | modérateur, modératrice |
| overwhelm verbe | /ˌoʊ.vɚˈwɛlm/ | abreuver |
| preposition nom | /ˌpɹɛpəˈzɪʃən/ | préposition |
| purposely adverbe | /ˈpɝpəsli/ | exprès |
Les verbes à particule que vous entendrez
| Mot | Sens |
|---|---|
| come up with verbe | inventer, imaginer |
| check out verbe | mater, regarder |
Des phrases à répéter
Des phrases courtes et complètes de la vidéo, à réutiliser dans la conversation de tous les jours :
- You don't need to learn all of them.
- That's all I have to say about this question.
- Hopefully you'll find it helpful.
- That's my answer to this question.
- How do you develop good communication skills though?
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Phrases conditionnelles if + proposition, will/would + verbe — une condition et son résultat | if learned earlier would benefit · If you know anyone who would be |
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | question, which was · everyone who submitted · people who rely |
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | have thought · haven't done |
Prononciation à surveiller
Le locuteur utilise 13 contractions et formes réduites, comme don't, I'm, haven't. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « th »: breadth /bɹɛdθ/, algorithm /ˈælɡəɹɪðm̩/
- Les sons « sh » et « zh »: preposition /ˌpɹɛpəˈzɪʃən/, instruction /ɪnˈstɹʌkʃən/
- Mots longs — placez bien l’accent: analytics /ˌæn.əˈlɪt.ɪks/, independently /ˌɪndɪˈpɛndəntli/, categorize /ˈkætɪɡəˌɹaɪz/, feasibility /ˌfiːzɪˈbɪlɪti/, moderator /ˈmɒdəˌɹeɪtə(ɹ)/
Les sons difficiles pour les francophones :
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: randomization /ˌɹændəməˈzeɪt͡ʃən/, modular /ˈmɑd͡ʒəlɚ/, gradual /ˈɡɹæd͡ʒuəl/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/
- /r/ anglais — langue recourbée, sans frotter la gorge: recruit /ɹɪˈkɹut/, clarify /ˈklæɹ.ɪ.faɪ/, metric /ˈmɛt.ɹɪk/, preference /ˈpɹɛf(ə)ɹ(ə)ns/, breadth /bɹɛdθ/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme recruit, clarify, metric.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.