Data Analysis with Python - Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn)

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Learn Data Analysis with Python in this comprehensive tutorial for beginners, with exercises included!
NOTE: Check description for updated Notebook links.
Data Analysis has been around for a long time, but up until a few years ago, it was practiced using closed, expensive and limited tools like Excel or Tableau. Python, SQL and other open libraries have changed Data Analysis forever.
In this tutorial you'll learn the whole process of Data Analysis: reading data from multiple sources (CSVs, SQL, Excel, etc), processing them using NumPy and Pandas, visualize them using Matplotlib and Seaborn and clean and process it to create reports.
Additionally, we've included a thorough Jupyter Notebook tutorial, and a quick Python reference to refresh your programming skills.
💻 Course created by Santiago Basulto from DataWars
🔗 Check out all Data Science courses from DataWars: datawars.io/re...
⚠️ Note: Instead of loading the notebooks on notebooks.ai, you should use Google Colab instead. Here are instructions on loading a notebook directly from GitHub into Google Colab: colab.research...
⭐️ Course Contents ⭐️
⌨️ Part 1: Introduction
What is Data Analysis, why Python?, what other options are there? what's the cycle of a Data Analysis project? What's the difference between Data Analysis and Data Science?
🔗 Slides for this section: docs.google.co...
⌨️ Part 2: Real Life Example of a Python/Pandas Data Analysis project (00:11:11)
A demonstration of a real life data analysis project using Python, Pandas, SQL and Seaborn. Don't worry, we'll dig deeper in the following sections
🔗 Notebooks: github.com/rmo...
⌨️ Part 3: Jupyter Notebooks Tutorial (00:30:50)
A step by step tutorial to learn how to use Juptyer Notebooks
🔗 Twitter Cheat Sheet: / 1122176794696847361
🔗 Notebooks: github.com/rmo...
⌨️ Part 4: Intro to NumPy (01:04:58)
Learn why NumPy was such an important library for the data-processing world in Python. Learn about low level details of computations and memory storage, and why tools like Excel will always be limited when processing large volumes of data.
🔗 Notebooks: github.com/rmo...
⌨️ Part 5: Intro to Pandas (01:57:08)
Pandas is arguably the most important library for Data Processing in the Python world. Learn how it works and how its main data structure, the Data Frame, compares to other tools like spreadsheets or DFs used for Big Data
🔗 Notebooks: github.com/rmo...
⌨️ Part 6: Data Cleaning (02:47:18)
Learn the different types of issues that we'll face with our data: null values, invalid values, statistical outliers, etc, and how to clean them.
🔗 Notebooks: github.com/rmo...
⌨️ Part 7: Reading Data from other sources (03:25:15)
🔗 Notebooks: github.com/rmo...
⌨️ Part 8: Python Recap (03:55:19)
If your Python or coding skills are rusty, check out this section for a quick recap of Python main features and control flow structures.
🔗 Notebooks: github.com/rmo...
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Learn to code for free and get a developer job: www.freecodeca...
Read hundreds of articles on programming: freecodecamp.o...

Опубликовано:

28 сен 2024

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Комментарии : 981

@freecodecamp 4 года назад

⚠️ Note: Instead of loading the notebooks on notebooks.ai, you should use Google Colab instead. Here are instructions on loading a notebook directly from GitHub into Google Colab: colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb#scrollTo=K-NVg7RjyeTk The code links in the description have been updated to the content stored on GitHub.

@mellowbeatz93 4 года назад

you cant load the root folder into google collab. Therefore you need to load the exercises directly to the collab but that leads to an error if you want to try to load data e.g. sales_data.csv (' cannot open 'data/sales_data.csv' for reading: No such file or directory')

@hossainmohaimenul4671 3 года назад

@@mellowbeatz93 check the path of the uploaded file . usually it is in a content folder .

@mahdik2335 3 года назад

please add persian subtitle😭😭😭🙏🙏🙏🙏

@ksreynosomoreno7036 3 года назад

Yea, there's an error, does anyone have any advice on loading the exercise files ?

@victorblaer 3 года назад

@@mellowbeatz93 if you navigate to your files directory, in colab, there is an option to copy the full file path name and from there you copy that, and paste it into your code like this (I saved mine with capital letters.) sales = pd.read_csv( '/SALES_DATA.CSV', parse_dates=['Date'])

@leixun 4 года назад

*My takeaways:* *1. Table of Content* 1:45 *2. Introduction **2:52* 2.1 What is data analysis 2:52 2.2 Data analysis tools 4:38 2.3 Data analysis process 7:31 2.4 Data Analysis vs Data Science 8:56 2.5 Python and PyData Ecosystem 9:28 2.6 Python data analysis vs Excel 9:46 *3. Real example data analysis with Python: getting a sense of what you can learn from this course **11:00* *4. How to use Jupyter Notebooks **30:50* *5. Intro to NumPy **1:04:58* 5.1 Low-level basis: binary numbers, memory footprint 1:09:32 5.2 Python is not memory efficient to store numbers since it wraps everything into objects. Whereas in NumPy, we can select the number of bits to represent numbers 1:22:50 5.3 NumPy can compute arrays faster than Python 1:24:58 5.4 NumPy tutorial: NumPy arrays, matrices 1:29:47 5.5 Memory footprint and performance: Python vs NumPy 1:53:14 *6. Intro to Pandas: getting, processing and visualizing data **1:56:58* 6.1 Pandas data structure: Series 1:58:41 6.2 We can change the index of Pandas series and this is fundamentally different from NumPy arrays 2:02:55 6.3 The upper limit of slicing in Pandas series is included, whereas, in NumPy, the limit is excluded 2:07:55 6.4 Pandas data structure: DataFrames 2:14:36 6.5 Most operations in Pandas are immutable 2:29:10 6.7 Reading external data 2:36:47 6.8 Pandas plotting 2:44:41 *7. Data cleaning **2:47:18* 7.1 Handling miss data 2:51:40 7.2 Cleaning invalidate values 3:03:17 7.3 Handling duplicated data 3:06:09 7.4 Handling text data 3:11:05 7.5 Data visualization 3:13:41 7.6 Matplotlib global API 3:14:25 7.7 Matplotlib OOP API 3:18:27 *8. Working with data from(/to) SQL, CSV, txt, API etc. **3:25:15* 8.1 Python methods for working with files 3:26:37 8.2 Python methods for working with CSV files 3:29:33 8.3 Pandas methods for working with CSV files 3:30:05 8.4 Python methods for working with SQL 3:36:17 8.5 Pandas methods for working with SQL 3:38:58 8.6 Pandas methods for working with HTML 3:43:09 8.7 Pandas methods for working with Excel files 3:49:56 *9. Python recap **3:55:18*

@Amy_Yu2023 4 года назад

Lei Xun Thanks for sharing

@fc.soccercard 4 года назад

Many thx!

@jorgemendoza1160 2 года назад

goat 🐐

@kranthikumar4397 2 года назад

Thank u ❤️

@deidrascott8871 2 года назад

Thanks nan

@jacktorrence4595 4 года назад

This is the best channel ever. No one does so clear, long and ad free videos.... My compliments 👏👏🤟👊🤙

@adegokeadedeji6148 4 года назад

Freecodecamp all the way!

@colinhuang2325 4 года назад

all coding no play makes Jack a dull boy

@jacktorrence4595 4 года назад

@@colinhuang2325 wow, this is a good one😉😉

@pyshine_official 4 года назад

Excellent

@SarveshGupta-bu5ho 4 года назад

Is that enough to study in data analysis?

@satyasieng5166 Год назад

1. Table of Content 1:45 2. Introduction 2:52 2.1 What is data analysis 2:52 2.2 Data analysis tools 4:38 2.3 Data analysis process 7:31 2.4 Data Analysis vs Data Science 8:56 2.5 Python and PyData Ecosystem 9:28 2.6 Python data analysis vs Excel 9:46 3. Real example data analysis with Python: getting a sense of what you can learn from this course 11:00 4. How to use Jupyter Notebooks 30:50 5. Intro to NumPy 1:04:58 5.1 Low-level basis: binary numbers, memory footprint 1:09:32 5.2 Python is not memory efficient to store numbers since it wraps everything into objects. Whereas in NumPy, we can select the number of bits to represent numbers 1:22:50 5.3 NumPy can compute arrays faster than Python 1:24:58 5.4 NumPy tutorial: NumPy arrays, matrices 1:29:47 5.5 Memory footprint and performance: Python vs NumPy 1:53:14 6. Intro to Pandas: getting, processing and visualizing data 1:56:58 6.1 Pandas data structure: Series 1:58:41 6.2 We can change the index of Pandas series and this is fundamentally different from NumPy arrays 2:02:55 6.3 The upper limit of slicing in Pandas series is included, whereas, in NumPy, the limit is excluded 2:07:55 6.4 Pandas data structure: DataFrames 2:14:36 6.5 Most operations in Pandas are immutable 2:29:10 6.7 Reading external data 2:36:47 6.8 Pandas plotting 2:44:41 7. Data cleaning 2:47:18 7.1 Handling miss data 2:51:40 7.2 Cleaning invalidate values 3:03:17 7.3 Handling duplicated data 3:06:09 7.4 Handling text data 3:11:05 7.5 Data visualization 3:13:41 7.6 Matplotlib global API 3:14:25 7.7 Matplotlib OOP API 3:18:27 8. Working with data from(/to) SQL, CSV, txt, API etc. 3:25:15 8.1 Python methods for working with files 3:26:37 8.2 Python methods for working with CSV files 3:29:33 8.3 Pandas methods for working with CSV files 3:30:05 8.4 Python methods for working with SQL 3:36:17 8.5 Pandas methods for working with SQL 3:38:58 8.6 Pandas methods for working with HTML 3:43:09 8.7 Pandas methods for working with Excel files 3:49:56 9. Python recap 3:55:18

@iskandarzulkarnain9705 4 года назад

I swear this is the best channel in youtube ever.

@Tyong-sk7vt 4 года назад

what if you don't like coding you would not think like this

@yoonamurders 4 года назад

@@Tyong-sk7vt then you better learn to understand the logic at least or dont venture into this field.

@halfword 4 года назад

iskandar zulkarnain lmao it’s filled with info

@subrajeetmaharana 3 года назад

var Iskandar comment = "you swear wrong" P.S:- I created variable unethically

@ZaidAlzamil-z4y 3 года назад

@@Tyong-sk7vt he speaks as a coders

@Nachiketa_TheCutiePie 4 года назад

Part 1: Introduction Part 2: Real Life Example of a Python/Pandas Data Analysis project 00:11:11 Part 3: Jupyter Notebooks Tutorial (00:30:50) Part 4: Intro to NumPy (01:04:58), (01:30:00) Part 5: Intro to Pandas (01:57:08) Part 6: Data Cleaning (02:47:18) Part 7: Reading Data from other sources (03:25:15) Part 8: Python Recap (03:55:19)

@Papivors 4 года назад

Thanks

@SarveshGupta-bu5ho 4 года назад

Is that enough to study in data analysis?

@soafiavyjunior7045 4 года назад

@@SarveshGupta-bu5ho i think need more research

@aryan04jaiswal 4 года назад

@@SarveshGupta-bu5ho u wish XD, this is just the start

@aryan04jaiswal 4 года назад

@@SarveshGupta-bu5ho but If u have a good grasp of these libraries, it will be very beneficial in model creation for machine learning and deep learning

@datafreak911 4 года назад

I have been watching your course for 2 weeks and I can say this is the best guide I have ever seen. Thank you guys

@SantiagoBasulto 3 года назад

Thank you!

@SuganthanMadhav 2 года назад

How you are doing the practicals? Can you help me out?

@sagnikmukherjee5108 3 года назад

I just finished it and the content is just awesome. It gets easy the way trainer explains things here. Thanks a ton for this lovely content.

@sheldoncooper3373 3 года назад

Accha course tar prerequisites ki ki? Ami c java valo kore jani.. python er ekdom basic gulo jani.. mane oi if else loop eisob gulo just. Python a Kono coding experience nei. Ei video ta start kora jbe?

@sagnikmukherjee5108 3 года назад

@@sheldoncooper3373 ekdom start kora jabe. Ami jokhon start korechilam ami o python r basic e jantam. Chotpot start kore dao. Khub valo kore e bujhiyeche. Best of luck.

@sheldoncooper3373 3 года назад

@@sagnikmukherjee5108 thank you dada😊😊

@saumitrapathak6416 3 года назад

hello, does this course have matplotlib, seaborn and all?

@JoseAntonio-wt3zm 3 года назад

This course is awesome ! The explanations are very clear and the teaching way is very fine. Thank you so much for all the hard work you put in making this !

@daniellakshan 4 года назад

Oh, thank you so much brothers, I have been waiting for a course like this from you guys, your channel has been so much helpful for me to improve my coding skills. You guys deserve to receive an award for this incredible service. Thanks again brothers, keep it up. 😘

@SarveshGupta-bu5ho 4 года назад

Is that enough to study in data analysis?

@nanditsrivastava2026 3 года назад

i dont know how many comments down here are real, but i think this tutorial was wayyy to direct for a beginner... Numpy and Pandas are explained very well no doubts on that...the data cleaning part was very direct, no beginner will get a bit of it, reading external files section was Ok, Matplotlib was explained like everyone knows about the attributes since birth. I am working as a data scientist from the past 4 years, i would not recommend this to anyone who is a beginner, except for the Intro , numpy , pandas and reading data from external sources section...

@shusma4069 3 года назад

Completely agreed.

@kasturidas4081 2 года назад

Sir can you please recommend me some tutorials as I am completely beginner and don't know anything about python. But I have to learn atleast basics within this month. It's urgent. Please recommend me some videos.

@ahmadumeta4 2 года назад

I disagree, I'm a beginner and this is very straight forward. A beginner in programmatic data analysis should know what he's going to get into and this lecturer definitely has a great breakdown of that

@StevenTokyo06 4 года назад

Been looking forward to this course since the beginning of the year. It could not have come at a better time. Thank you very much!

@halfword 4 года назад

Steven Negishi exaclty

@davidfe9281 4 года назад

It's great to see so many thankfulness from every corner. It would be even better to see some gratitude transformed into donations. Some day this videos/courses will be over otherwise, no one survives only from free gratitude :)

@Persona_Incognito 3 года назад

This video was an unmitigated GODSEND! Thanks ever so much for posting it, Santiago! Since this has helped thousands of future DS folks out there (some of whom are struggling with a few of of these subjects like me), 1000000 karma points have been credited to your account :-)

@timgellings1428 3 года назад

"Welcome to our Data Analasis with Python Tutorial. My name is Santiago and I will be your instructor." 760.000 people: "Ok, here we go"

@somdebsar7378 3 года назад

1:42:36 kindly note that the method np.arange is "arange" instead of "arrange", there is single 'r' instead

@noxolongcobo8390 4 года назад

Freecodecamp coming through for us like nobody's business wow i stan❤️

@esraahamada4784 3 года назад

I started the course on July 15th and I will add a comment when I finish it edit: I finished the course on August 2nd Considering that I know Python and spent seven days of time learning fast typing

@Invin_cibles 3 года назад

is this video suitable for people who have no programming experience at all but want to look into using python for data analysis?

@kartikhegde533 3 года назад

@@Invin_cibles programming skill isn't required. but basic python should be known

@Invin_cibles 3 года назад

@@kartikhegde533 Thanks, I'm struggling to find the notebooks.ai demo or the interactive tutorial he's using, dont quite get the comment about google colab

@kartikhegde533 3 года назад

@@Invin_cibles if you are using VS Code they have notebook option. In command palette type 'create new notebook' experiment for a couple of days. And watch a separate tutorial . There are plenty available

@esraahamada4784 3 года назад

@@Invin_cibles You will need to learn the basics of python and the basics of statics for data analysis, and that will not take as many days as you think. It may take a couple of weeks. Just start, you'll get there at the end.

@SanjeevKumar-nc2rt 4 года назад

I have completed whole tutorial I leant so much from this.. thanks free code camp and tutor..😍☺

@SantiagoBasulto 3 года назад

Happy it helped :)

@Tommy_007 2 года назад

Good video. Note that not all exercise soulutions are correct. For instance, the exercise "Given the X numpy array, return True if any of its elements is zero" has an incorrect solution. Here is a correct (example) solution: X = np.array([-1, 2, 0, -4, 5, 6, 0, 0, -9, 10]) print(not all(X)) Y = np.array([1,-5,3,7]) not all(Y)

@ahmadumeta4 2 года назад

I think you're also wrong in your code. The correct form is "np.any()" and not "not all()"

@Tommy_007 2 года назад

@@ahmadumeta4 I don't agree. I think that "np.any()" checks if any of its elements is NON-zero. But that was not the task.

@ahmadumeta4 2 года назад

@@Tommy_007 shouldn't that be "not any()" since it negates the condition and returns true if any of the elements is zero?

@Tommy_007 2 года назад

@@ahmadumeta4 No, "not any" is the same as "none" which is the same as "all are zero" which not what we want here. "Not all" (as I wrote) means that "at least one is not non-zero" which is the same as "at least one is zero" - and that is what we want.

@samarthdhroov 4 года назад

Cant imagine learning so good anywhere else :)

@tonysoviet3692 3 года назад

As a data analyst in Maersk, I really appreciate this course in balancing between the technical foundations and actual executions! Most people only get to learn the codes without understanding the concepts, which are what separate workers from engineers!

@tngzhexuan964 2 года назад

Hi it seems like you were able to successfully complete the course did u have any troubles in accessing the sales file?

@peterjamali772 2 года назад

how much do you earn asa data analyst

@aerodynamico6427 2 года назад

With a name like that, how come you are not yet sanctioned by Biden?

@vegetossgss1114 Год назад

I am 25 and I come from a strong pure and applied mathematical background, but I am a total newbie in programmation. I please have some questions: 1/ Are SQL, Python and R enough to get a permanent position as a Data Analyst / Data Scientist? 2/ How much time do I need to be able to manage and organize databases, and use them to produce statistical analysis and graphs? 3/ If I am hired, can the employer change his / her mind and ask me to code in other languages that were not written in my curriculum, such as C++ or Java?

@matiasdiaz3808 11 месяцев назад

@@vegetossgss1114 I could anwer your questions: I`m working in a data science team. In an entring possition you are generally hired as data analyst in the first place, even thought some business don`t make such diferences between data analyst and data scientists. 1- SQL knowledge is a must. You should be fine opting between R and Python. If you are starting and don`t know neither of them, I would reccomend Python, since it´s the most popular. 2- In most companies they already have data bases and you need to know how to write SQL queries to access the data and, after that, cleaning it with Python or R. If you are just trying things out, you only need data tables from websites such as kaggle. CSV or excel files. 3- It obviously deppends on the company, but that would be odd, since data analys/scientists are not meant to be "professional programmers". If you have a strong mathematical background I think it will be quite easy for you to understand machine learinig algorithms in the future, which is something most people struggle with.

@jeswin499 4 года назад

Just when I wanted it. Good timing.

@pavelmurtin3943 2 года назад

All in all, thanks for providing a free course for us :) Just started the course, Jupyter went great, I decided to use Jupyter lab instead of Jupyter Notebooks. Went through Numpy lecture, seemed good. Now I'm at Numpy Excercises, where I had trouble loading the notebook and I ran into a couple of errors in the Numpy Excercise problems. If i run into more, i'll try to post them here for other people by editing the comment. Can't promise anything though. Possible error list: Numpy Excercises - Logical operations: Given the X numpy array, return True if any of its elements is zero (prosing a change to non zero here) because np.any() is a test for true (non zero) elements, and therefore the wrong answer.

@shaswat2 4 года назад

1:52:26 Taught a very complex topic in the easiest way.

@noranviccanto2230 4 года назад

Just seen the first few minutes and I seem to loke it. You have one of the most easy, quick and to the point explanations. Subscribed and willing to complete the video...

@wyllerdesousa1710 Год назад

Thank you to help the TI community, I am a beginner so I apreciate it.

@BiologyIsHot 3 года назад

The flow in this needs some work in my opinion. I've been using Pandas/Matplotlib for like a year and I still had a hard time following this. It's normal to hit high-level discussion before diving into specifics, but the way it's done here is not the best example on this channel. Doesn't help that the guy's pace of speech is not great. Slows down at all the wrong parts and rattles off important info.

@brianbatista1368 3 года назад

I agree, he seems to start skimming more and more as you get deeper into the video which was disappointing..

@kepstein8888 2 года назад

You know how to teach. Very rare skill. Thanks for sharing this.

@Maan_ta_nepali 4 года назад

found i was looking for. thank you so much.

@madhur8537 4 года назад

Best channel on RU-vid ☺️☺️☺️🙂🙂

@nzubeonuigwe3315 4 года назад

Impressive and helpful tutorial. Thanks for this amazing teaching.

@PiterMandemaker 3 года назад

I love the way he explains but the text is on the small side. Have to use fullscreen. Which is inconvenient.

@siludavidson 2 года назад

Great contents. The speaker knows exactly what he's talking about and has great deal of detail knowledge about python and various libraries. Thank you for sharing. I'm just wondering if sharing the fundamentals and ground level details (numpy memory) could have been a separate course all together and this one could have just focused on data analysis, may be....

@KhoaNguyen-pg8qx 4 года назад

This tutorial is perfect. Thank you very much for making it!

@TheCsePower 2 года назад

1:39:23 You can also do things like this A [ [ 1 , 2 : 5 , -1 ] , 2 ]

@saby826 4 года назад

Thanks for the introduction part..kindly implement project also from start to end...

@John83118 9 месяцев назад

Your perspective is enlightening. Similar to a book that's an exemplar. "Adapting with Aging" by Various Authors

@heinrichruv2136 4 года назад

what about a "freeMathCamp" or "freeLogicCamp" section? Sooner or later you will have to use math and logic in programming.

@Mark-pg9mu 3 года назад

Check out their "Mathematics" playlist.

@pazakharov Год назад

I advise you always keep the explained fragment in the center of the screen. Subtitles cover it at the bottom of the screen

@datamonster8114 4 года назад

Please get Closed CCaptions for all your videos. I understand your instructions but sometimes want to speed things up and not miss something. Thanks. Great job

@disinimoviefilm1276 3 года назад

please include or enable subtitles, thankyou:)

@ChangesOfTomorrow 3 года назад

If this is intended for people with no knowledge of Python, it's hard to follow. I don't get how to write commands or what the basic principles are, so it seems to me by 'beginners' you meant people who know some python and want to dive into data analysis using it.

@jeremyjoe7494 3 года назад

A beginner's course to Python is meant for people who are new to Python. Once you want to use Python for a specific goal, there is no reason to repeat the basics of Python since you can learn that in their Python course.

@koboni2660 Год назад

Vraiment

@redahegazy8605 Год назад

I liked it even from the beginning , you are doing a great effort to explain every detail. thank you

@activestate 4 года назад

Very thorough video, does a great job of explaining intricate concepts in a simple way.

@KelvinMafurendi 9 месяцев назад

Thank you so much for this beginner friendly course. I have had a good start!👍

@konishiwasanni6364 4 года назад

you are the Best freeCodeCamp channel

@moeinghahremani54 4 года назад

Dear Santiago! Great thanks releasing the valuable video! You have rescued me from confusion in data science

@jkally123 3 года назад

By far the best tutorial on data analysis. Have same on machine learning?

@xXHelsingGamingXx 7 месяцев назад

Brilliant blue eyes and this course is awesome!!

@stillwrinkled Год назад

This course isn't good. The instructor moves like a bullet train between topics. Fonts are too small for Mac book Air. The section on data cleaning is very confusing. I had to pause this video and follow other videos on data cleaning. The flow of topics is also very jarring. Why isn't data cleaning the second topic after 'Introduction'? I tried my best but gave up after 3 hours or so.

@charminghostes 10 месяцев назад

generally super! Now I'm in a great mood for the whole day! thank you!:)

@siyabonga.fortune 2 года назад

Great content👏, still rocking in 2022 💯

@YlmazDALKIRANscallion 3 года назад

2:18 Please change the video description and use the index below 00:00:00 Introduction 00:11:11 Real Life Example of a Python/Pandas Data Analysis project 00:30:50 Jupyter Notebooks Tutorial 01:04:58 Intro to NumPy 01:57:08 Intro to Pandas 02:47:18 Data Cleaning 03:25:15 Reading Data from other sources 03:55:19 Python Recap So we can jump out the section by using video process bar.

@neelbanga 3 года назад

LEGEND

@kubakondraciuk5180 Год назад

Really appreciate this material. Helped me a lot with my dissertation.

@thanhvu4736 3 года назад

Great video. Please turn on subtitles. Thanks you!!!

@Opoliades 5 месяцев назад

There is a small typo* when generating the dynamic plot with Bokeh. It seems that the title of the plot i not equal to "legend", but now is "legend_label". The codesnippet cannot run without that change. timestamp in video: 1:00:15 ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-r-uOLxNrNk8.html

@eilidhwells6059 2 года назад

Quite disappointed by the teaching structure. Nothing hands on, just endless narrations, code executions and assumptions. It is far from beginner friendly and seems rushed all the time. Not sure if the comments are legitimate because, I can't be the only one struggling to process and grasp the plenty methods and arguments while the course progresses. Started off well, but completely sunk from Numpys down.

@abioyeorimadegun7851 Год назад

I almost got discouraged by your comment but I decided to follow the video comprehensively The best way I think to understand the tutorial is by practicing the codes available via the links in the description and trying the exercises after every lesson. Although, you might end up spending more than 4 hours on the video but i believe it's a good sacrifice for knowledge. That aside, I agree with you that the tutorial is not 100% beginner friendly. You must have some basic knowledge of Python before even thinking about using Python for Data Analysis. If you don't, it's better to watch a tutorial on general Python programming for beginners before this.

@andredias5061 Год назад

It's for beginners in data analysis, not python or programming in general

@Polyglot-HM Год назад

@@abioyeorimadegun7851 do you recommended it?

@prashansingh8861 Год назад

@@andredias5061the title says "DATA ANALYSIS WITH *PYTHON*", it's a clickbait and totally confusing( I'm a total noob in coding or analytics)

@bigazTV 4 месяца назад

@@prashansingh8861with python meaning you are supposed to to know python already. Come on guys

@limitlesschidera 2 года назад

you could have explained better how one will get the jupyter notebook and install some of the files you used in the tutorial. that would have made things easier for beguneers

@ruchirverma4216 4 года назад

Thanks for this tutorial. Can anyone tell me where can I find these csv files ?

@vasushanmugam9072 2 года назад

It would be great if you can upload some data file for access during the training which is used as running the python in videos

@philippetsongo90 8 месяцев назад

See how awesome this course is 😎

@KhanhTran-ds8ds 2 года назад

This is insane effort. Thank you, thank you !!!!

@AaronDennis1111 4 года назад

You know, you have to tell us about import requests. This isn't something python beginners automatically know exists. This is a big problem with all these "learn code" courses. You're not walking us through line by line and describing what the code does or when, why, how we should use it. It's great to see what python "can" do, but we need to know when to do what with it. These courses are the equivalent of telling us that numbers exist, giving us 0-9, showing that numbers can be added and such, and then expecting us to go out into the real world and apply them to geometry without ever providing the intermediary steps.

@yuvrajkarna3752 8 месяцев назад

Thank you for this course. After learning this can anyone suggest some projects related to this course??

@mntolee3626 4 года назад

I just want to marry you guys~ love the course!!

@collectpad2207 4 года назад

coz of course marry

@MarkMcDaniel 4 года назад

Title of video: Data Analysis with Python. Spends no time coding, just reading websites. 😠

@sivaprakash2247 4 года назад

Actually data analysis involve less coding more depends on analytical part

@darshankumar5546 4 года назад

the best channel,the best video,on this topic ,,really a damn costly course,getting for free,really lucky!! long live free code camp!!!

@agatasiembida 4 года назад

thank you very much for sharing your knowledge, I found this tutorial very useful!

@vishxl 3 года назад

Great tutorial. Very grateful to have found this!

@sathyasayesh9914 2 года назад

USD 60 bil, the worth of Bezos, an example the tutor quotes. As of now in Dec 2021, Bezos is worth upwards of USD 120 bil

@manojchauhan2633 3 года назад

Very well structured and delivered, Thank you.

@ricodsanchez6792 4 года назад

Really helpful great upload glad I subbed

@vixxiv515 4 года назад

thanks for the great work here. could you clarify please how to load the notebook/database used? thanks

@ultraeye3919 3 года назад

Please zoom your video. My eyes are soooo paining. (Watching in mobile display) I love "Programming with mosh" channel. He uploads Good quality zoomed edited videos!

@lolikpof Год назад

How does this compare to the _Python for Data Science_ video?

@mehsanm 3 года назад

This is a fantastic introduction to Python!

@Dongnanjie 8 месяцев назад

Love it. Thank you so much!

@electricalcoder3025 4 года назад

Awesome fantastic marvellous amazing....video.. If mins==32: RU-vid.execute(2.9kviews) If mins >33: RU-vid.execute(me+"100 likes")

@sakarias7272 7 месяцев назад

This tutorial is really good but why are the answers to the exercises already in the exercise sheets?

@shyamsundarshrestha7590 4 года назад

Thank You Sir! Wonderful

@dabaobaobao 2 года назад

I think part of the video is broken, around 1:34:00-1:36:00

@utkarshbansal2758 4 года назад

Networking course please

@TheGrandmaster1 3 года назад

I can't believe this channel exists. I really truly can't.

@littlecodely1265 4 месяца назад

Can you please! provide csv file on which u guys are explaining so that we can practice it

@oyelaranpaul 2 месяца назад

I love this session, please i want to ask if I can get link to the sales dataset used. thanks

@abhaysoni3146 4 года назад

Big Thank you!! You guys are best!!

@sagalpreetsingh 4 года назад

Can we have a video on some great flutter project from your side, maybe a complete e-commerce application? Anyways, your channel is much more than just awesome !

@lucasbravo7873 4 года назад

Que grande Santi Basulto. Un orgullo para Firulete escuela de futbol.

@enrichomsrivera3637 3 года назад

Muchas gracias Santiago !! Fantastic tutorial.

@y.g.5102 Год назад

It's amazing! Thank you!

@ryanblumenow 3 года назад

This content is great. Would it be possible to upload a pdf of the twitter cheat sheet? It tells me that tweet is not available.

@prerakshah4360 4 года назад

What are the prerequisites of this course?

@animay8713 4 года назад

I love every second of this video. You're an INCREDIBLE teacher. I'm so grateful. Thank you.

@uditkapoor2611 4 года назад

so are you.

@AK-Star007 4 года назад

this great and exactly what i was looking for.

@seedata8351 4 года назад

If you are interested in data visualization, you can watch my channel for more info 🙏

@jalalkarimov5754 3 года назад

Thank you for effort making this video, but it is not easy to follow it to be honest. Especially intro part to each topic was not smooth and hard to understand if you are on a beginner level.

@diogos3475 4 года назад

High quality stuff, but I miss some of what he says because of the accent, subtitles would be highly helpful here. Just a suggestion, really good content either way.

@marziehfarahani7121 2 года назад

Thanks, would you please check the pronunciation of Tuple, It is confusing for students.

@higheloguy9057 3 года назад

1. How python in jupyter works 2. How Jupyther works. NICE

@menkiguo7805 Год назад

he did not talk about some most important things in data science, such as linear regression, t-test, F-test, PCA, Correlation matrix, etc

@easydatascience2508 Год назад

YOU can watch mine, most of the fundamentals in Python and R playlists.

@menkiguo7805 Год назад

@@easydatascience2508 it is still language specific tho. I usually won't spend time on language specific things but more of the basic concepts and equations. Reasons: language come and go, python might be replaced by r or Julia, who knows? Also, those things are really easy to look up on documentation. Like say I want to know how to do a t test in Python, I just google scipy t test and I got the function name and I pass in the array. But yeah, to be fair, you do need to learn some basic python syntax and pandas/numpy operations. But I learned most of them on the fly during a data festival (not python, I learned that way back in elementary school). I am in analytical chemistry and computer science. So maybe the ways I use them are really specific and narrow so I cannot say for all.