Shadowing Practice: Learn how to SOLVE a data analytics case study problem - Learn English Speaking with Video

בחר מצב תרגול

כל מצב פותח חדר תרגול במסך מלא.

239 משפטים

אם המשפטים קצרים או ארוכים מדי, לחץ על ערוך כדי להתאים אותם.

and then we ask the interviewer hey is that good

or not

and they're like no do something a little bit more complicated

hi everyone today i'm going to solve a data analytics case

study problem first we're going to go over what constitutes a

data analytics problem a framework for how to tackle it

and then a step-by-step explanation of diving into it

so first off what is a data analytics case study for our purposes i've defined data analytics case study problems

as any problem that requires 1 formulating metrics for a hypothetical scenario

and 2 writing a SQL query or analyzing a dataset in Python to retrieve those metrics.

For example,

the problem we're tackling today involves understanding how to build a

new audio chat feature to improve the match rate between car buyers and sellers in a marketplace app.

Now if you're thinking that data analytics case study questions sound very similar to product metrics type questions,

you're probably right.

And for all intents and purposes,

most product metrics questions fall under the umbrella of data analytics.

It's just that product metrics questions are more focused around product case studies.

So for example, it'd be something like,

how would you investigate a 10% drop in Uber ride requests, right?

Or let's say that we want to launch a new feature for Uber,

how would you analyze results?

So all those product metrics questions are focused around a specific product itself

and so data analytics can be a little bit more broad than

that the second main difference is

that data analytics case studies questions we actually expect the candidate to use sql

or python to actually implement their metrics whereas in product metrics

case study questions you're usually just kind of brainstorming different ideas

clarifying the question giving out structured analysis without actually diving into the data itself

because there is no data it's a hypothetical scenario

so data analytics case studies what you'll find is

that they'll actually give you an actual data set many times this will happen in a take-home assignment

or an official phone screen

or even on an on-site where the interviewer will basically just give you a data set

and a python jupiter notebook and be like go to hand it's kind of nice

because basically it kills two birds with one stone

if you're an interviewer they're trying to assess how you do on sequel

and the same time they can also get a sense of your product intuition

The main issue with these is that if you have bad product intuition and you then work on a SQL query,

you're either going to make your life really difficult by working on a SQL problem that's extremely difficult

or on the entirely wrong problem.

So let's just dive into this question and then see how it goes.

All right.

An online marketplace company has introduced a new feature

that allows potential buyers and sellers to conduct audio chats with each other prior to transacting.

Let's say we have two tables that represents this data.

So we have a chats table and we have a marketplaces purchases table.

How would you measure the success of this new feature?

Write a query that can represent if this feature is successful or not.

How do we tackle this problem?

So the first thing we have to do is to treat this like it's a classic product metrics,

just any kind of analytics case study, right?

So we're doing stuff like we're clarifying,

we're assessing requirements, requirements, we're validating our solution.

Before that, we're proposing a solution and then we're validating it.

Yeah, let's just start out with that first.

And, you know, given the time constraints of most of these interviews,

I would say the most optimal thing

that we're going to be doing is actually be proposing one metric and immediately coding that up.

I think in every instance,

we also want to keep this metric pretty simple.

I see a lot of candidates shoot themselves in the foot by first proposing like a really complicated metric

and then

when they actually have to code it up in SQL they're

just spending you know like 40 minutes the rest of the time trying to get to a solution.

The best way and I think always the best way to approach this is always iteration right

so we iterate on our approaches we propose a simple solution

and then we code it up and we do that

and we do that fast and then we ask the interviewer hey is

that good or not and they're like no do something a little bit more complicated

and then they'll be like okay yeah so i'll do something new now

so again let's try to iterate on all of our solutions here

and start out by keeping it simple kiss framework right keep

it simple stupid the second tip is to keep the metric super flexible for further analysis okay

so if we start out with just one simple solution then we can actually make it flexible

so that later down the line

we are also not cheating ourselves on the foot again by having to rewrite our entire SQL query.

We want to be able to make it so

that we can actually write it in a way that is flexible for further solutions down the line.

Given this problem and given

that we have to understand how a feature basically allows users to conduct audio chats with each other,

try this again is just to visualize exactly what the output is that I want.

So I want something that says use audio chat,

right and then if it is a one or a zero then i want also purchase completion rates

and then this is going to be something like you know 50 and then this will be something like

25 right so this is the output that i want to see right

if i'm a pm i want to see that oh people

that use the audio chat completed their purchases at a higher rate than people

that did not use audio chat okay

so awesome we visualize this output we come up with this

metric this thing i want to note in our analysis is

that you know

if we'd like to expand the analysis we can actually analyze it by the call duration

or the number of calls and so uh conversations

that probably have on average three audio chats

or 2x the total length of call time then other conversations are more likely to have transactions completed than something

that has you know on average one audio chat

and then like 30 seconds of call time instead of one minute of call time on average

and those have a lower transaction rate let's say we're analyzing

100

call chat time basically like 30 seconds 60 seconds 90 seconds

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and then we look at our metric that we care about which is purchase

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completion rates 30 percent 50 percent 69 basically we're seeing

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that you know on average if we bucket our call chat time

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and as the chat time goes longer the purchase completion rates

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go higher then probably this audio feature is then working for us

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so i just want to say that out of all of our metrics

107

that we put together one thing

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that we do have to keep in mind is normalizing the data

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so usually we have to compare two equally interested groups of conversational buyers

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so that we know

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that the audio chat feature is actually making the difference it's not the fact

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that because i make more calls i'm already inherently more likely to complete a transaction

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and so a lot of that normalization you know we can't really do

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that with our existing data set so i would just make

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that assumption up front so that you get extra brownie points with your interviewer

116

and you know most of the analytics problems are all causal inference

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if you mention you know you know causal inference they're like

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oh cool you know what causal inference is let me check off this box right to be honest

120

that is really what a lot of interviewers and interviews are like

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because of the fact that they have to do

122

so many interviews they're just trying to speed it up so they can get to their lunch break

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all right so let's just solve for this uh simple calculation first right we just want to know

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if they use the audio chat did they actually increase their

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purchase completion rate first i'm gonna write select from

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chats left join marketplace purchases as c as

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mp on c dot buyer user id equals mp.buyer user id look at

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that autocomplete oh my god we need to pay our engineers more

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user id is equal to mp.seller user id uh group by call connected

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so when we group by the call connected uh

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so as you can see we're doing double join right we

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want to join the buyer to the buyer of the chat

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and to the buyer of the marketplace we want to join

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the seller of the chat to the the seller of the marketplace.

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Then we want to group by if the call was connected or not.

136

And then we want to grab the distinct marketplace ID divided by the distinct number of chats.

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And then I can just run this query.

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Hopefully it'll run.

139

I have an error.

140

Of course, I have an error.

141

I didn't comment this out.

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Let me comment this out.

143

Cool.

144

Awesome.

145

So as you can see below,

146

and I'll with my video.

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The number of calls that were connected resulted in a higher conversion rate.

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Obviously, this is fake data,

149

so of course it was going to.

150

So we're good, except I spot an error in our SQL query.

151

And if you want to pause the video,

152

maybe you guys can try to find it first before I go on.

153

Okay, let's unpause it.

154

So where is the error in my SQL query?

155

Basically, what I noticed was that If you look at chats,

156

technically you could have more than one chat with the same person, right?

157

So if I call this person once,

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twice, they don't pick up.

159

Then I call the seller three times, they finally pick up.

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Then technically, I'm actually grouping by call connected,

161

which would then mess up my data, right?

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Because I'd have two zeros and then a one.

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So if I group by that,

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then this wouldn't really work out and this data is wrong.

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So what I have to do instead is group by the distinct number of chats and then rejoin it.

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So let's do that again.

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So instead, let me think.

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I still think I can reuse some of my data here.

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So I'd say let's group by the buyer user ID,

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the seller user ID.

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And then let's run actually a function in here where we want to know if there was at least one connection.

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So instead, our actual formula means use audio chat at least once.

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Because here, you know, technically,

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if I'm like talking to this guy and I make two purchases,

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you know, without the audio chat,

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but then I use the audio chat in a separate purchase and I use it at least once,

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but then I chat with him,

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like create like three different chats with that same guy.

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I want to know if at least using it once influenced

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and gives me a higher purchase completion rate than never using it ever.

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This is the bare bones analysis.

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If I use this feature at least once, does it do anything?

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Does it change up the completion rates at all?

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So here we're going to just run a max on call connected.

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So this means that for every buyer-seller combo,

186

did they at least connect on a call at least once?

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So I'll label this at least one connection, call connected.

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Then next I'll just do count distinct mp.id same as this one divided by the

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Actually no so instead I'll just use I will also just group by 1 and 2 So as total purchases

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Then I'll wrap this in a subquery with distinct chat as this.

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So we're grabbing the total purchases,

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this is actually completed transaction.

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So we know that we completed this transaction.

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So now I want to group by this value and I can do that with the CTE.

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So I'm grouping by this.

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This is going to be distinct chats.

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And then group by this value, one.

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And then now I can actually just look at the average number of completed transactions.

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And then the sum of completed transactions.

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Okay, cool.

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So why did I make this completed transaction?

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So basically for every combo,

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what this query does is that it basically finds exactly if there is at least one call connected,

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and then if there is a completed transaction.

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So for example, you know,

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one buyer and seller, they didn't connect in a call, no completed transaction.

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Let's say they did complete at least one call and they did complete a transaction.

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So this would be effectively just,

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you know, one because they're only doing one transaction either way so once we take the average of this

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we're taking the average of ones and zeros and

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so that's how we're getting this value of 0.435 or sorry 4.35

212

and 3.92 that's this that's the data analytics case question i

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think this is a great kind of solution for now i

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think one last thing i'd like to do is like why don't we try to

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and do an analysis and see if like more call completions lead to more purchase transaction rates.

216

So kind of that scenario that I was drawing above,

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except this one is like if we have three audio chats instead of two audio chats instead of one audio chat,

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like does the three audio chats lead to more completed transactions than the one audio chat?

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If it does, then it probably means that audio chats are beneficial for this business.

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So I'll actually leave that for the exercise for you guys.

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If you guys want to see the answer,

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just go to interview query,

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go to the link below,

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and then you'll see the answer in the solution bar right here,

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because I'm going to finish that before this video comes out officially.

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And I hope this was helpful.

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I hope this video isn't all over the place.

228

I hope I have a really good video editor that helps me out with this stuff.

229

And please remember to like and subscribe on this video

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so I can go through this exercise of doing more videos for you,

231

even though, you know, I'm jumping back into it.

232

So it's been a painful experience so far,

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but I'm hoping I'll get better and better at it.

234

I just need your guys' support.

235

Awesome.

236

All right?

237

Cool.

238

Thanks.

239

Bye.

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אוצר מילים והערות הגייה לשיעור הזה

שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “Learn how to SOLVE a data analytics case study problem”. המילים שחוזרות הכי הרבה: chat, audio, metric, transaction, purchase. בסרטון הזה יש 239 משפטים ו-2518 מילים לתרגול Shadowing. הדיבור נמשך 14:20. הדובר מדבר בקצב טבעי של כ-176 מילים בדקה, קרוב לשיחה יומיומית. 83% 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
metric שם תואר	/ˈmɛt.ɹɪk/	מֶטְרִי
buyer שם עצם	/ˈbaɪ.əɹ/	קוֹנֶה
cellar שם עצם	/ˈsɛləɹ/	מַרְתֵּף
clarify פועל	/ˈklæɹ.ɪ.faɪ/	הבהיר
seller שם עצם	/ˈsɛlɚ/	מוֹכֵר, מוֹכֶרֶת
brainstorming שם עצם		סִעוּר מֹחוֹת
normalization שם עצם	/ˌnɔɹ.mə.ləˈzeɪ.ʃən/	נרמול, נוֹרְמָלִיזָצְיָה
normalize פועל	/ˈnɔː.mə.laɪz/	נִרְמֵל
notebook שם עצם	/ˈnoʊtˌbʊk/	מַחְבֶּרֶת, דִּפְתָּר
calculation שם עצם	/ˌkælkjuˈleɪʃn̩/	חִשׁוּב \ חישוב
constitute פועל	/ˈkɑnstɪt(j)uːt/	היווה
umbrella שם עצם	/ʌmˈbɹɛl.ə/	מִטְרִיָּה, שמשייה
optimal שם תואר	/ˈɑptɪməl/	אופטימלי
sequel שם עצם	/ˈsiːkwəl/	המשך
pause שם עצם	/pɔːz/	אתנח

Phrasal verbs שתשמעו

מילה	הגייה	משמעות
pick up פועל		הרים, אסף
check off פועל		סמנב
come out פועל	/ˌkʌm ˈaʊt/	יָצָא

משפטים שכדאי לחזור עליהם

משפטים קצרים ושלמים מהסרטון שאפשר להשתמש בהם בשיחה יומיומית:

How would you measure the success of this new feature?
How do we tackle this problem?
I didn't comment this out.
Because I'd have two zeros and then a one.
If I use this feature at least once, does it do anything?

דקדוק בסרטון הזה

המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:

מבנה	בסרטון
סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה	is let · was connected · were connected
הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו	i've defined · has introduced · it's been

הגייה ששווה לשים לב אליה

הדובר משתמש ב-66 קיצורים וצורות מקוצרות, כמו we're, I'm, I'll. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.

הצלילים של “sh” ו-“zh”: visualize /ˈvɪʒuəˌlaɪz/, intuition /ˌɪn.tjuːˈɪʃ.ən/, conversational /ˌkɒnvə(ɹ)ˈseɪʃənəl/, iteration /ˌɪt.əˈɹeɪ.ʃən/, normalization /ˌnɔɹ.mə.ləˈzeɪ.ʃən/
מילים ארוכות — שימו לב להטעמה: analytics /ˌæn.əˈlɪt.ɪks/, intuition /ˌɪn.tjuːˈɪʃ.ən/, hypothetical /ˌhaɪpəˈθɛtɪkəl/, conversational /ˌkɒnvə(ɹ)ˈseɪʃənəl/, iteration /ˌɪt.əˈɹeɪ.ʃən/

צלילים שדוברי עברית מתקשים בהם:

/w/ — שפתיים מעוגלות, לא /v/: query /ˈkwɪɹ.i/, sequel /ˈsiːkwəl/
/æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: analytics /ˌæn.əˈlɪt.ɪks/, analyze /ˈæn.əˌlaɪz/, validate /ˈvæl.ɪ.deɪt/, clarify /ˈklæɹ.ɪ.faɪ/, transact /ˌtɹænˈzækt/
/r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: metric /ˈmɛt.ɹɪk/, buyer /ˈbaɪ.əɹ/, query /ˈkwɪɹ.i/, cellar /ˈsɛləɹ/, propose /pɹəˈpəʊz/

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 metric, buyer, cellar.

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.

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אוצר מילים והערות הגייה לשיעור הזה

אוצר מילים מרכזי בסרטון הזה

Phrasal verbs שתשמעו

משפטים שכדאי לחזור עליהם

דקדוק בסרטון הזה

הגייה ששווה לשים לב אליה

איך לתרגל עם הסרטון הזה

מהי טכניקת השאדואינג?