I dont understand why group by is necesery in the last select query

looks like Hadoop is more or less deprecated

I am genuinely concerned if anyone attempted any of that.

I might misunderstood the challenge! Doesn't below code solve the problem? select user1, count(distinct user2) / (cast (select count(distinct user1 from facebook_friends) as float) ) * 100 as popularity from facebook_friends group by user1 order by popularity desc

such a professional video, I'm really thankful :)

I believe the choice between using ntile(100) with num <= 2 and ntile(50) with num = 1 depends on the total number of records. In my tests on Redshift, if the dataset exceeds 50 records, both methods yield the same result. However, when the dataset contains fewer than 50 records, ntile(50) with num = 1 returns only one record, while ntile(100) with num <= 2 still returns two records. Therefore, it's better to clarify with the interviewer regarding the expected outcome, especially since it's not feasible to evenly distribute the data into buckets in such scenarios. Considering that the result cannot be fractional, returning one row might be preferable. However, better to communicate and discuss these considerations.

What kind of psychopath would use a logistic regression instead of a linear regression when the situation would call for a linear regression?! I need to go lay down now.

Makes me remember one time before presentation i tried changing accuracy by changing random_state 😂

😂 Hilarious… 1:29 … so funny …

I’m saving this video as a road map .lol. Thanks, I’m grateful for the info 🙌🏽

haha sarcasm is strong with this one, love it

This is so funny 😂😂

If your boss doesn't like the figures you come up with, just change them! Sometimes you just gotta smooth the numbers

Thanks for always churning out great contents Nathan. I look forward to more of your insights.

Made more api progress today than with days of Internet and ai. Thank you. I love how pragmatic this video is.

i mean why you had to hide API key?

Python data sciences libs. API naming is just horrible (((

All ML/DS courses taught me the latter steps - data cleaning, building a model, data visualization. I learned the deployment part from different resources. So it looks like I lack the beggining steps, that is a real-life data that is updated frequently, using APIs to retrieve it and using a DB in cloud to store it. Thank you for this insightful video. I already feel inspired, I think I will try to do something with Spotify data, if it's publicly accessible :) You are a great motivator.

Thank you for your time and effort. Explained in detail...

thank you!

im having a problem with channel id, in CHANNEL_ID, i put my own channel, but each time i press response, i get a different channel

am I the only one who doesn't see the link to download the data? Would love to work on this project on my own. Thank you!

very interesting🔥🔥

After watching this video, I will return after becoming an expert data scientist.🔥

This is lots of information with short time, love it!

Just a heads up, the notebooks you show at the end of each algorithm section is switched up for GB, K-Means and PCA

What if the data file is not in a perfect tabular / csv format? For example, I have a large text file like this: #Time (HHH:MM:SS): 002:34:02 # T(ms) BUS CMD1 CMD2 FROM SA TO SA WC TXST RXST ERROR DT00 DT01 DT02 DT03 DT04 DT05 DT06 DT07 # ===== === ==== ==== ==== == ==== == == ==== ==== ====== ==== ==== ==== ==== ==== ==== ==== ==== 816 B0 D84E BC RT27 2 14 D800 2100 0316 0000 0000 0000 0000 CCCD 0000 817 A0 DC50 RT27 2 BC 16 D800 2120 0000 4080 3000 0000 3000 0000 0000 #Time (HHH:MM:SS): 002:34:03 # T(ms) BUS CMD1 CMD2 FROM SA TO SA WC TXST RXST ERROR DT00 DT01 DT02 DT03 DT04 DT05 DT06 DT07 # ===== === ==== ==== ==== == ==== == == ==== ==== ====== ==== ==== ==== ==== ==== ==== ==== ==== 056 B0 D84E BC RT27 2 14 D800 2100 0316 0000 0000 0000 0000 CCCD 0000 057 A0 DC50 RT27 2 BC 16 D800 2120 0000 4080 3000 0000 3000 0000 0000 How can get just the data from DT00 thru DT07 into an array, without doing lots of preprocessing to scrub out the repeating #Time headers that appear throughout the file?

Thanks for this video Nathan. Your facts are not just true for Data Scientist but also Software engineer and programmers.

Please I am building a recommendation system for diet and exercise. Where can I get my data set or API. A link will be appreciated.

Was gpt 4 used?

Funny how all the experts say chatgpt is useless, but the tech priests keep hyping it as the savior and doom of humanity.

Wait, we can upload data to chatgpt? How do I do that?

The only caveat I see in using real data for personal projects is its availability. Most APIs offering relevant functionalities, such as those for cryptocurrencies and market data, require a paid subscription for extensive use. While free APIs are available, it is essential to assess the data based on your project objectives, as well as the quantity of data you require.

🎯 Key Takeaways for quick navigation: 00:00 *📝 Importance of Code Structure* - Understanding the importance of code structure for comprehension and logic, - Exploring a real SQL interview question to demonstrate code structuring, - Highlighting the significance of well-structured code in job interviews and professional work environments. 02:41 *🛠️ Refactoring Nested Queries* - Refactoring nested queries for clarity and improved logic flow, - Demonstrating the use of common table expressions (CTEs) for better code organization, - Emphasizing the importance of understanding and manipulating data before applying additional logic. 07:14 *🔄 Ensuring Consistent Aliases* - Utilizing consistent and descriptive aliases for tables and columns, - Improving code readability and comprehension through clear naming conventions, - Highlighting the significance of explicitness in code for future reference and collaboration. 10:42 *📊 Eliminating Unnecessary ORDER BY Clauses* - Streamlining code by reducing redundant ORDER BY clauses, - Optimizing code execution for efficiency and performance, - Emphasizing the importance of minimizing unnecessary code execution for code optimization. 12:47 *🧹 Simplifying Code with CASE Statements* - Simplifying code logic using CASE statements for concise and efficient code, - Demonstrating optimization by reducing the number of common table expressions (CTEs), - Streamlining code structure for improved readability and maintenance. 13:02 *🔍 Choosing Between HAVING and WHERE Clauses* - Differentiating between HAVING and WHERE clauses based on usage and optimization, - Opting for WHERE clauses over HAVING clauses for simpler and faster query execution, - Highlighting the importance of understanding SQL nuances for code optimization.

Great video!!

This is brilliant. I took way too much time searching and googling and watching hours long videos to find pretty much everything I need in a clear crisp straight forward 30min video. Minus a tiny bit of API beginner information and knowing the basics of Python, this was perfect. Well done. I imagine the next video will be everything I need and nothing that I don’t to take the next step of processing/analyzing the data. I am hitting play on that……. Now!

Good Explanation

Feels good to be an ML engineer right now then aha

Quality content, as usual from the hero. Thank you, wish I'd have a mentor like you who I knew personally.

@3:03 That's @NicolusRenotte. I love him no homo.

my database created folder is empty :(

Extremely useful video on the "MUST TO KNOW" Python libraries for everyone who wants to dive into Data Science roles! I think that Dash is also a popular framework for building interactive Python dashboards! Thank you for sharing!!!

Thank you this is helpfull

This is an amazing session, thank you

StrataScratch Platform. Hmm ... 🤔

Really appreciate the tutorial, tremendously helpful getting my own api project started.

woaw super helpful

🔥

I streamlit good enough for a data driven app, I guess you are going to say no, for obvious reasons as cloud experience is crutial, thank again for the video

I would like to express my sincere gratitude to the creator of this KZfaq video for providing excellent content and being truly inspirational. The video not only taught me valuable skills in working with real data, APIs, databases in the cloud, and building models, but it also emphasized the importance of making an impact and getting validation in the field of data science. The way the creator explained the decision-making process when building models and the underlying math behind them was incredibly helpful. Additionally, the interview questions shared in the video provided valuable insights into the best practices for cleaning data, validating models, and optimizing them. Moreover, I greatly appreciate the emphasis on sharing code and insights with the data science community. Learning how to deploy an application and create interactive dashboards or APIs opens up endless possibilities for collaboration and knowledge sharing. Overall, this video has been an exceptional resource for me, and I want to thank the creator for their dedication in providing such valuable content. They have truly inspired me to continue my journey in data science and strive for excellence in every project I undertake.