Shadowing Practice: Data Science Interview Prep: 3 Key Resources You Need to Know - Learn English Speaking with Video
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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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אוצר מילים והערות הגייה לשיעור הזה
שיעור הדיבור הזה ברמה B2 מבוסס על הסרטון “Data Science Interview Prep: 3 Key Resources You Need to Know”. המילים שחוזרות הכי הרבה: answer, skill, resource, communication, scientist. בסרטון הזה יש 145 משפטים ו-2002 מילים לתרגול Shadowing. הדיבור נמשך 11:22. הדובר מדבר בקצב טבעי של כ-176 מילים בדקה, קרוב לשיחה יומיומית. 86% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית; כדאי ללמוד את השאר לפני שמתחילים.
אוצר מילים מרכזי בסרטון הזה
15 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:
| מילה | הגייה | משמעות |
|---|---|---|
| clarify פועל | /ˈklæɹ.ɪ.faɪ/ | הבהיר |
| metric שם תואר | /ˈmɛt.ɹɪk/ | מֶטְרִי |
| preference שם עצם | /ˈpɹɛf(ə)ɹ(ə)ns/ | הַעֲדָפָה |
| categorize פועל | /ˈkætɪɡəˌɹaɪz/ | קטלג, סִוֵּג |
| diagnose פועל | /ˈdaɪ.əɡˌnoʊs/ | אבחן |
| preposition שם עצם | /ˌpɹɛpəˈzɪʃən/ | מילת יחס, מִלַּת יַחַס |
| listener שם עצם | /ˈlɪs(ə)nɚ/ | מַאֲזִין |
| motivate פועל | /ˈmoʊ.tɪ.veɪt/ | דרבן |
| feasible שם תואר | /ˈfiː.zɪ.bəl/ | בר ביצוע |
| hesitate פועל | /ˈhɛz.ə.teɪt/ | היסס |
| advertisement שם עצם | /ˈædvɚˌtaɪzmənt/ | פִּרְסוֹמֶת |
| leverage שם עצם | /ˈlɛv.(ə.)ɹɪd͡ʒ/ | הנפה |
| execute פועל | /ˈɛksɪˌkjuːt/ | להוציא להרוג |
| subscribe פועל | /səbˈskɹaɪb/ | נִמְנָה |
| algorithm שם עצם | /ˈælɡəɹɪðm̩/ | אַלגוֹרִיתְם |
Phrasal verbs שתשמעו
| מילה | משמעות |
|---|---|
| check out פועל | בדק |
משפטים שכדאי לחזור עליהם
משפטים קצרים ושלמים מהסרטון שאפשר להשתמש בהם בשיחה יומיומית:
- 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?
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| משפטי תנאי (Conditional sentences) if + clause, will/would + verb — תנאי ותוצאה שלו | if learned earlier would benefit · If you know anyone who would be |
| משפטי זיקה (Relative clauses) who / which + clause — מידע נוסף על אדם או דבר | question, which was · everyone who submitted · people who rely |
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | have thought · haven't done |
הגייה ששווה לשים לב אליה
הדובר משתמש ב-13 קיצורים וצורות מקוצרות, כמו don't, I'm, haven't. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.
- הצלילים של “th”: breadth /bɹɛdθ/, algorithm /ˈælɡəɹɪðm̩/
- הצלילים של “sh” ו-“zh”: preposition /ˌpɹɛpəˈzɪʃən/, instruction /ɪnˈstɹʌkʃən/
- מילים ארוכות — שימו לב להטעמה: analytics /ˌæn.əˈlɪt.ɪks/, independently /ˌɪndɪˈpɛndəntli/, categorize /ˈkætɪɡəˌɹaɪz/, feasibility /ˌfiːzɪˈbɪlɪti/, moderator /ˈmɒdəˌɹeɪtə(ɹ)/
צלילים שדוברי עברית מתקשים בהם:
- /w/ — שפתיים מעוגלות, לא /v/: overwhelm /ˌoʊ.vɚˈwɛlm/, equip /ɪˈkwɪp/, workplace /ˈwɝkˌpleɪs/
- /æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: clarify /ˈklæɹ.ɪ.faɪ/, analytics /ˌæn.əˈlɪt.ɪks/, categorize /ˈkætɪɡəˌɹaɪz/, randomization /ˌɹændəməˈzeɪt͡ʃən/, gradual /ˈɡɹæd͡ʒuəl/
- /r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: recruit /ɹɪˈkɹut/, clarify /ˈklæɹ.ɪ.faɪ/, metric /ˈmɛt.ɹɪk/, preference /ˈpɹɛf(ə)ɹ(ə)ns/, breadth /bɹɛdθ/
איך לתרגל עם הסרטון הזה
- הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
- התחילו במהירות 0.75×, תרגלו Shadowing משפט אחר משפט, וחזרו למהירות רגילה כשזה מרגיש קל.
- הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו clarify, metric, preference.
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.