Shadowing Practice: Data Science Interview Prep: 3 Key Resources You Need to Know - Learn English Speaking with Video
Ders oluşturuluyor...
1
Hey guys, welcome back to my channel.
2
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.
3
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.
4
Last time, we focused on the number one voted question, which was how to land a more senior data scientist job.
5
What would be different from preparing for entry and mid-level role
6
if you want to hear my answer to
7
that question i have a whole video focusing on it
8
that you can check out to learn more in this video
9
i am going to cover in four more questions from you
10
guys these questions range from interview preparation resources to broader career
11
advice thanks again to everyone who submitted questions let's get started
12
our first question deals with resources for interview preparation the question
13
is how to prepare for product analytics data scientist positions what preparation strategy to use what resources to use
14
and what depth of knowledge required for questions like statistics probability product sense
15
and python there are actually several great questions here and i'm going to briefly answer all of them
16
in terms of the resources i recommend i want to point
17
you to my blog on the resources i use for interview
18
preparation you can find the link in the description below this blog has all the resources I used.
19
It also categorizes the resources based on different subjects, so you can easily find what you are looking for.
20
If all of these resources seem a bit overwhelming, don't panic.
21
You don't need to learn all of them.
22
Depth is more important than breadth.
23
Just pick one or two resources in each category and get started, and use your own preferences to make your choices.
24
If you like reading, pick a book.
25
If you prefer watching, pick YouTube videos or an online course.
26
Preparation is is a lot easier if you work with your natural preferences.
27
In terms of the depth of knowledge required, it's hard to provide a standard because it varies from coming to coming.
28
The best way to get the sense of the level of difficulty is to collect some sample interview questions online.
29
Gastor is usually a very helpful resource in this regard.
30
However, if you cannot find any sample questions for coming online,
31
you can also communicate with the recruiter and ask if they can send you more detailed instructions.
32
While some people hesitate to ask a recruiter for anything, remember that this is their job.
33
They are motivated to help you.
34
You can even consider hopping on a call with a recruiter to ask specific questions.
35
This advice also applies to the part of the question that asks if we need data structures and algorithm for Python questions.
36
Some comments required it and others don't.
37
So it's helpful to clarify with a recruiter.
38
You can even ask for sample Python questions from the past.
39
If you want more help with Python questions, I made a blog about it with my friend Rob that you can check out.
40
Just to clarify though, the target audience is data scientists' machine learning.
41
For product data scientists, the blog makes it clear that it's typically the first two types coding questions.
42
I also have a video on this topic that may be helpful.
43
That's all I have to say about this question.
44
Hopefully you'll find it helpful.
45
Let's Let's move on to the next question in our Q&A
46
This question is from Nora and deals with A-B testing The question is, how to have a deep understanding of A-B testing
47
and past product case interviews that involve it when my work does not provide opportunities for doing A-B testing?
48
Thanks for your question, Nora
49
I can recommend three great resources for deepening your understanding of A-B testing Udacity's A-B testing course
50
my own videos on the topic and the book Trustworthy Online Control Experiments A Practical Guide to AP Testing Specifically,
51
I suggest tackling those resources in that order
52
The Udacity course will cover the fundamentals Then watch my videos and read the book to deepen your understanding
53
The book I recommend is a great comprehensive resource
54
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,
55
such as how to do geo-based randomization.
56
With these resources, you can advance your understanding of AP testing, even if your work does not give you opportunities.
57
That's my answer to this question.
58
Moving on to the next one.
59
The next question says, I found follow-up questions from case interviews especially hard.
60
Are there any ways for me to prepare for those?
61
Thanks to Lucy for asking this.
62
Follow-up questions can be quite difficult and I find that they're especially hard for people who rely on frameworks
63
The fact is that most of the time interviewers can tell if a candidate is following a framework
64
Sometimes they will purposely try to destructure you To avoid getting derailed,
65
when you practice you want to be able to not only express your ideas but also to defend them
66
You need to train yourself to think independently and critically Therefore, to answer this question about answering follow-up questions,
67
I want to briefly talk about how to leverage frameworks and how to develop your critical thinking skills.
68
First of all, I'm not saying frameworks are totally bad.
69
Far from it.
70
Frameworks are helpful when you get started and don't know how to answer certain questions.
71
However, as you learn more, you're also going to have your own ideas.
72
This may come from your experience as a customer or your criticism of others' ideas or frameworks.
73
Incorporating your own ideas into answers is how you make your answers unique and train yourself to think independently.
74
Now, I will use myself as an example.
75
When I first started learning how to crack case interviews I felt
76
that the questions were
77
so hard I struggled to come up with a single idea for a long time
78
so I did some research by reading books and blog posts
79
and watching YouTube videos and learned how others would answer the question at
80
that stage I thought that the people were
81
so brilliant to be able to come up with those ideas
82
but as I learned more I I began to feel
83
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
84
The candidate uses the framework to talk about different factors without even clarifying how large the change is
85
and if the change is sudden or gradual
86
Another example is people recommended looking at 10 metrics from an A-B test And I wondered if this is even feasible
87
I never monitored 10 metrics in a project in my own work experience
88
so that made me question the feasibility of that answer
89
So those answers gave me a great starting point
90
but I was able to use my own experience
91
and understanding to make them better I developed my own personal
92
frameworks to answer certain questions You can find some of them in this video
93
By taking the time to think through your own personal knowledge and develop more unique answers to questions
94
you will be far better equipped to defend your answers and respond to follow-up questions
95
okay we have one more question
96
or rather two more questions for this q a session these
97
are from abushak he first asks based on your experience what
98
things would you suggest someone focus on in the early stage of their data science career
99
which if learned earlier would benefit throughout the career
100
or at least make sense easier in the long run this is a great question
101
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,
102
and eventually advancing your career will all be much harder.
103
These skills are vital to your career and you can make yourself stand out if you focus on them early.
104
How do you develop good communication skills though?
105
It takes practice, but I have a few suggestions
106
that can help you work on this vital skill first take
107
opportunities to present your work practice is key for developing any skill whether it be in school
108
or the workplace when you have a chance to present take it
109
when you do present your work the preparation you do is
110
critical prep time gives you a chance to think about how to communicate effectively
111
and develop strategies feedback from the audience is also important.
112
Reflecting on the feedback and the question you get from the audience helps you with improving your presentation.
113
Developing good communication skills also means being aware of what communication is.
114
Good communication is not just about talking well.
115
Communication is also about listening.
116
Aim to be a good listener.
117
Listen to others' ideas and concerns.
118
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.
119
You can learn from their ideas.
120
Being able to really listen is crucial to communication, but it's also a skill many people neglect.
121
So it is smart to learn it early.
122
Besides communication skills, technical skills are another thing I would encourage you to work on early in your career.
123
Technical skills obviously allow you to do your job and are therefore critical.
124
Common technical skills for data scientists include statistics, coding, SQL, machine learning, and data analytics.
125
You should feel comfortable with those technical skills because other people rely on you to provide data-informed decisions.
126
Besides those necessary skills, I would also suggest data scientists learn more software engineering skills.
127
Data scientists work closely with engineers, so knowing the fundamentals of software engineering can prove extremely helpful.
128
For example, understanding things like the software development life cycle, the importance of writing clean and modular code, the importance of writing tests,
129
the difference between web and mobile development, and data quality by logging on front-end versus back-end.
130
Knowing the software engineering fundamentals can give you a distinct advantage as a data scientist.
131
That's my answer to the first question.
132
Abyshek also asked, you and Jesse talk about the importance of market reviews in the preparation process.
133
Can you suggest some places where one can do such things?
134
I mean, if there are any kind of study groups or something like that.
135
Well, the short answer is that I'm not aware of such a community.
136
Actually, I have thought about creating one before, and I did some research about it.
137
What I found was that it needs time and effort to maintain a free community so that members follow certain community rules,
138
such as no advertisements, only post relevant information,
139
etc. So the challenging part was about effectively managing such a community so that everyone benefits by being part of it.
140
If you know anyone who would be interested in being a moderator
141
or community manager I'd be more than happy to advocate it on my channel
142
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
143
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,
144
don't forget to subscribe to my channel if you haven't done already There is more content to come.
145
I will see you guys soon.
✨ Video Öner
B24
23 cümleler
Interviewing Part 1 - What You Need To Ask In Advance
Business EnglishInterview EnglishUniversity Students
B21
181 cümleler
8 Years of Data Analytics Advice in 12 Minutes
Business EnglishInterview EnglishUniversity Students
B2182 cümleler
🔥HR Interview Questions and Answers (2025) | Top HR Interview Questions for Freshers | Intellipaat
Business EnglishInterview EnglishUniversity Students
C12
257 cümleler
A/B Testing in Data Science Interviews by a Google Data Scientist | DataInterview
Business EnglishInterview EnglishUniversity Students
B22
141 cümleler
QUALITY CONTROL Interview Questions & Answers! (Inspector, Manager + Assessor Interview Questions!
Business EnglishInterview EnglishUniversity Students
B27
191 cümleler
AI (Artificial Intelligence) JOB INTERVIEW QUESTIONS & ANSWERS! (How to PASS an AI JOB INTERVIEW!)
Business EnglishInterview EnglishUniversity Students
B2171 cümleler
Interview Tips from a Senior Software Engineer... and a 4 year old
Business EnglishInterview EnglishUniversity Students
C13
205 cümleler
Top 5 Insider Interview Questions Data Analysts Must Master Before Any Interview!
Business EnglishInterview EnglishUniversity Students
C12
239 cümleler
Excellent 2 Years AWS DevOps Engineer Mock Interview with Questions and Answers | Cloud | Docker
Business EnglishInterview EnglishUniversity Students
B23
119 cümleler
How to Become a Sales Engineer | Without Any Experience
Business EnglishInterview EnglishWorking Adults
B21
674 cümleler
FULL Data Science Mock Interview | SQL, Statistics, Business
Interview EnglishUniversity StudentsWorking Adults
B22
142 cümleler
think like a business analyst in tech (3 core skills + solving case study)
Business EnglishInterview EnglishUniversity Students
Bu dersin kelimeleri ve konuşma notları
Bu B2 seviyesindeki konuşma dersi “Data Science Interview Prep: 3 Key Resources You Need to Know” videosuna dayanıyor. En çok tekrarlanan kelimeler: answer, skill, resource, communication, scientist. Bu videoda gölgeleme çalışması için 145 cümle ve 2002 kelime var. Konuşma bölümü 11:22 sürüyor. Konuşmacı dakikada yaklaşık 176 kelimeyle, günlük konuşmaya yakın doğal bir hızda konuşuyor. Kelimelerin %86’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var.
Bu videodaki önemli kelimeler
Videodaki en ileri düzey 15 kelime, telaffuzu ve anlamıyla:
| Kelime | Telaffuz | Anlam |
|---|---|---|
| clarify fiil | /ˈklæɹ.ɪ.faɪ/ | temizlemek |
| preference isim | /ˈpɹɛf(ə)ɹ(ə)ns/ | tercih |
| feasibility isim | /ˌfiːzɪˈbɪlɪti/ | fizibilite, yapılabilirlik |
| moderator isim | /ˈmɒdəˌɹeɪtə(ɹ)/ | aracı, moderatör |
| preposition isim | /ˌpɹɛpəˈzɪʃən/ | ilgeç, edat |
| equip fiil | /ɪˈkwɪp/ | donatmak |
| interviewer isim | /ˈɪntɚvjuɚ/ | röportajcı |
| listener isim | /ˈlɪs(ə)nɚ/ | dinleyici |
| feasible sıfat | /ˈfiː.zɪ.bəl/ | kılgın, mümkün |
| hesitate fiil | /ˈhɛz.ə.teɪt/ | çekinmek, kararsız kalmak |
| advertisement isim | /ˈædvɚˌtaɪzmənt/ | reklam, ilan |
| incorporate fiil | /ɪŋˈkɔɹ.pɚ.eɪt/ | içermek, ihtiva etmek |
| leverage isim | /ˈlɛv.(ə.)ɹɪd͡ʒ/ | kaldıraç kuvveti |
| execute fiil | /ˈɛksɪˌkjuːt/ | idam etmek |
| subscribe fiil | /səbˈskɹaɪb/ | abone olmak |
Duyacağınız deyimsel fiiller
| Kelime | Anlam |
|---|---|
| check out fiil | gözden geçirmek |
Tekrar etmeye değer cümleler
Videodan, günlük konuşmada yeniden kullanabileceğiniz kısa ve tam cümleler:
- 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?
Bu videodaki dil bilgisi
Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:
| Yapı | Videoda |
|---|---|
| Koşul cümleleri if + cümle, will/would + fiil — bir koşul ve sonucu | if learned earlier would benefit · If you know anyone who would be |
| İlgi cümlecikleri who / which + cümle — bir kişi ya da şey hakkında ek bilgi | question, which was · everyone who submitted · people who rely |
| Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem | have thought · haven't done |
Dikkat edilecek telaffuzlar
Konuşmacı don't, I'm, haven't gibi kısaltılmış biçimleri 13 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “th” sesleri: breadth /bɹɛdθ/, algorithm /ˈælɡəɹɪðm̩/
- “sh” ve “zh” sesleri: preposition /ˌpɹɛpəˈzɪʃən/, instruction /ɪnˈstɹʌkʃən/
- Uzun kelimeler — vurguyu doğru yere koyun: analytics /ˌæn.əˈlɪt.ɪks/, independently /ˌɪndɪˈpɛndəntli/, categorize /ˈkætɪɡəˌɹaɪz/, feasibility /ˌfiːzɪˈbɪlɪti/, moderator /ˈmɒdəˌɹeɪtə(ɹ)/
Türkçe konuşanların zorlandığı sesler:
- /w/ — dudaklar yuvarlak, /v/ değil: overwhelm /ˌoʊ.vɚˈwɛlm/, equip /ɪˈkwɪp/, workplace /ˈwɝkˌpleɪs/
- Kelime başındaki ünsüz kümesi — araya ünlü eklemeyin: clarify /ˈklæɹ.ɪ.faɪ/, preference /ˈpɹɛf(ə)ɹ(ə)ns/, breadth /bɹɛdθ/, preposition /ˌpɹɛpəˈzɪʃən/, gradual /ˈɡɹæd͡ʒuəl/
- /æ/ — “e”den daha açık: clarify /ˈklæɹ.ɪ.faɪ/, analytics /ˌæn.əˈlɪt.ɪks/, categorize /ˈkætɪɡəˌɹaɪz/, randomization /ˌɹændəməˈzeɪt͡ʃən/, gradual /ˈɡɹæd͡ʒuəl/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- 0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; clarify, preference, feasibility gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.