⚠️ 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.
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')
@@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'])
I have a data science minor (with a Masters in Mech. Engineering). I work as a data scientist with focus on engine systems at the airlines. I'll tell you right now, this course was more informative than all the classes combined that I took in college. Santiago is one of the best you'll get, and also for free. This is truly a wonderful refresher.
I want to start this course. Is it still relevant in the end of 2023 or the information shared has become obsolete. Any insight will be highly appreciated brother.
ABSOLUTELY still relevant. Depending on where you work, you'll likely use Python or R, with occasional Excel uses as well. The important thing is the groundwork that is laid in this course. If you have a chance to take some probability and statistics classes at your school, do that. Also take your math classes seriously. Finally, Take an intro to data class somewhere online (easy to find), and you will likely know everything I know right now. I make about $150K/year so...@@TONYSTARK-ee5wy
*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*
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
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!
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?
@@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.
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)
@@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
i have reached the part where they r showing what we can do after this tutorial.. please help me out and let me know if i should practise more than the basic SQL before i continue this???
I am new to python. but I enjoyed this. If you are a newbie, dont focus on learning the syntax in this video. the best way to learn programming is to learn the functions first and then set aside sometime to work on your syntax skills. syntax overwhelms in the beginning. thank you for this. also loved your voice :)
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 !
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?
@@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.
i dont know a dang thing about programing ,just a little command prompt and thats it.. yet i actually understood what he was talking about. damn good teaching skills and communication skills .thank you.
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...
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.
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
Thank you for showing real life example at the start. I’ve been trying to learn Python from an online live class and I sometimes watch on TH-cam, but they don’t give me any idea what I’ll be doing with Python. Yeah, they teach me how to code, but I didn’t know how I would use Python for data analysis until this video.
2:08:09 The difference where the upper limit is included only seems to apply if you've defined your own index. It seems to work the same if you use the default numeric index.
This is a great video. They are not easy to find, a lot of times I will watch a video and learn nothing. This has been very well put together with great organization and exercises to retain the information.
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 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.
A word of advice for the guy teaching, please type the commands/codes yourself while explaining each line of code. You are just skimming what was already written and barely giving any time before scrolling down. Writing the code (prepare then read it from another screen or script) and explaining it will have a massive difference for your audience/students to understand. Teachers like blackboards over slideshows/pre-written materials to teach, because it helps the pace to train on it, is almost equal to the pace students understand. Sure, the video gets a bit longer or maybe a lot but nothing beats understanding it better than a quick video where it's harder to understand.
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.
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 :)
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 :-)
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. 😘
1:45:43 Notably you _can_ multiply arrays of different dimensions so long as the array with more dimensions is made up of arrays of the same shape as the smaller array.
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
@@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
@@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
@@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.
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.
Copy of @leixun comment, so I can see it at the top. 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
Greetings from Montenegro I'm so happy that I haven't stopped to continue learning English and there're lots of opportunities opened for me. Thanks for this course It's really useful! I'm currently looking for a new job as a Product Analyst in Russia (recenty relocated from there) and maybe I've to try to find a job in Western/Eastern companies as well. Idk if my Enlish level'll be enough but I'll see Best wishes, Anton!
I am going to watch this video in two rounds. The first to get overview of the whole idea. The second round to take notes and actually put in the work. I will dedicate 1 hour everyday after my work. So if this goes as planned it should take me 8 to 10 days. I will update my progress here.
First round day 1 done: It was mainly about what data analysis is , what we should expect from the course and how we use jupyter notebook. I have watched 1 hour and will stop here to avoid burnout.
As he ventures into the captivating world of artificial intelligence, let’s embark on this exciting journey together. I, too, am currently exploring of this field.
Excel is a good tool however it cannot deal with large data. Image doing all the analysis with even 5000 rows having to draw graph after graph onto different sheet. Excel gets very sluggish. With Python and their libraries with constant updates and support from the community, your good to go with even 1 million rows. Also like he said in the video, it gets cluster and tiring looking at the spreadsheets constantly, I find it quite distracting to do my analysis. SQL is good. I got no comments on that :)
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...
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.
Thank you Santiago Basulto ! The beginner course training was excellent. It was a delicious appetiser. Now I am waiting in anticipation for the main dish :)
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.
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.
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.
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.
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.
2:17:25 DAY 1 hopefully day 2 goes strong, please like this video so that I get the notification to do this course from 2:17:25 ** 2:15:22 maybe you could have explained the data frame maybe that could be bettter**
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.
![Python Tutorial for Beginners - Learn Python in 5 Hours [FULL COURSE]](/img/n.gif)
⚠️ 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.
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')
@@mellowbeatz93 check the path of the uploaded file . usually it is in a content folder .
please add persian subtitle😭😭😭🙏🙏🙏🙏
Yea, there's an error, does anyone have any advice on loading the exercise files ?
@@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'])
I have a data science minor (with a Masters in Mech. Engineering). I work as a data scientist with focus on engine systems at the airlines. I'll tell you right now, this course was more informative than all the classes combined that I took in college. Santiago is one of the best you'll get, and also for free. This is truly a wonderful refresher.
I want to start this course. Is it still relevant in the end of 2023 or the information shared has become obsolete. Any insight will be highly appreciated brother.
ABSOLUTELY still relevant. Depending on where you work, you'll likely use Python or R, with occasional Excel uses as well. The important thing is the groundwork that is laid in this course. If you have a chance to take some probability and statistics classes at your school, do that. Also take your math classes seriously. Finally, Take an intro to data class somewhere online (easy to find), and you will likely know everything I know right now. I make about $150K/year so...@@TONYSTARK-ee5wy
@@TONYSTARK-ee5wy of course technology has been updated but you still need to learn the basics
May get you linked in plz?
*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*
Lei Xun Thanks for sharing
Many thx!
goat 🐐
Thank u ❤️
Thanks nan
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
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!
Hi it seems like you were able to successfully complete the course did u have any troubles in accessing the sales file?
how much do you earn asa data analyst
With a name like that, how come you are not yet sanctioned by Biden?
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?
@@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.
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)
Thanks
Is that enough to study in data analysis?
@@SarveshGupta-bu5ho i think need more research
@@SarveshGupta-bu5ho u wish XD, this is just the start
@@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
There's a special place in heaven for you guys.
After the python course, I had to try different videos like numpy, pandas etc. This is way better!
i have reached the part where they r showing what we can do after this tutorial.. please help me out and let me know if i should practise more than the basic SQL before i continue this???
This is far better than many high-priced tutorial courses on the most popular MOOC platforms. I will forever keep this for future reference ❤❤
hi sorry please what's MOOC😢
I am new to python. but I enjoyed this. If you are a newbie, dont focus on learning the syntax in this video. the best way to learn programming is to learn the functions first and then set aside sometime to work on your syntax skills. syntax overwhelms in the beginning. thank you for this. also loved your voice :)
I swear this is the best channel in youtube ever.
what if you don't like coding you would not think like this
@@Tyong-sk7vt then you better learn to understand the logic at least or dont venture into this field.
iskandar zulkarnain lmao it’s filled with info
var Iskandar comment = "you swear wrong"
P.S:- I created variable unethically
@@Tyong-sk7vt he speaks as a coders
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
Thank you!
How you are doing the practicals? Can you help me out?
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 !
I never could imagine to find such an invaluable complete course for FREE in TH-cam. I can not find words to appreciate.
This is the best channel ever.
No one does so clear, long and ad free videos....
My compliments 👏👏🤟👊🤙
Freecodecamp all the way!
all coding no play makes Jack a dull boy
@@colinhuang2325 wow, this is a good one😉😉
Excellent
Is that enough to study in data analysis?
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.
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?
@@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.
@@sagnikmukherjee5108 thank you dada😊😊
hello, does this course have matplotlib, seaborn and all?
i dont know a dang thing about programing ,just a little command prompt and thats it.. yet i actually understood what he was talking about. damn good teaching skills and communication skills .thank you.
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...
Completely agreed.
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.
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
Thank you for showing real life example at the start. I’ve been trying to learn Python from an online live class and I sometimes watch on TH-cam, but they don’t give me any idea what I’ll be doing with Python. Yeah, they teach me how to code, but I didn’t know how I would use Python for data analysis until this video.
same
2:08:09 The difference where the upper limit is included only seems to apply if you've defined your own index. It seems to work the same if you use the default numeric index.
This is a great video. They are not easy to find, a lot of times I will watch a video and learn nothing. This has been very well put together with great organization and exercises to retain the information.
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)
I think you're also wrong in your code. The correct form is "np.any()" and not "not all()"
@@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.
@@Tommy_007 shouldn't that be "not any()" since it negates the condition and returns true if any of the elements is zero?
@@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.
Best Data science course on the planet period
Cant imagine learning so good anywhere else :)
1:42:36 kindly note that the method np.arange is "arange" instead of "arrange", there is single 'r' instead
A tremendous effort to go through data science topics. Extremely beneficial. Highly recommended and appriciation
I have been falling love in with this channel for one year ❤️
I have completed whole tutorial I leant so much from this.. thanks free code camp and tutor..😍☺
Happy it helped :)
Best channel on TH-cam ☺️☺️☺️🙂🙂
"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"
You know how to teach. Very rare skill. Thanks for sharing this.
You’re absolutely brilliant and generous for giving out this much information for all of us to learn, thank you!
I love how fast this instructor goes, instead of skipping I need to pause haha
A word of advice for the guy teaching, please type the commands/codes yourself while explaining each line of code. You are just skimming what was already written and barely giving any time before scrolling down. Writing the code (prepare then read it from another screen or script) and explaining it will have a massive difference for your audience/students to understand. Teachers like blackboards over slideshows/pre-written materials to teach, because it helps the pace to train on it, is almost equal to the pace students understand.
Sure, the video gets a bit longer or maybe a lot but nothing beats understanding it better than a quick video where it's harder to understand.
Well said
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.
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 :)
Thank you to help the TI community, I am a beginner so I apreciate it.
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!
Steven Negishi exaclty
1:52:26 Taught a very complex topic in the easiest way.
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 :-)
Coming from Excel to Python i found this really helped. Thank you for helping me get my bearings.
Hi, am also coming from Excel to Python just like you
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. 😘
Is that enough to study in data analysis?
1:45:43 Notably you _can_ multiply arrays of different dimensions so long as the array with more dimensions is made up of arrays of the same shape as the smaller array.
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
is this video suitable for people who have no programming experience at all but want to look into using python for data analysis?
@@Invin_cibles programming skill isn't required. but basic python should be known
@@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
@@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
@@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.
Best of the best channel for computer nerds
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.
LEGEND
It is thousand times better than CS courses at college!!
Just finished python in 4 hours today, time to learn Numpy, Pandas, and Ski-learn!
me too
Yesss !!! I’ve been waiting for this for a long time !!
Is that enough to study in data analysis?
No
Check IIIT Syllabus
1:39:23 You can also do things like this A [ [ 1 , 2 : 5 , -1 ] , 2 ]
Just when I wanted it. Good timing.
you are the Best freeCodeCamp channel
Amazing video, really really good, thank you Santiago for offering such a great free class online.
Copy of @leixun comment, so I can see it at the top.
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
Freecodecamp coming through for us like nobody's business wow i stan❤️
Greetings from Montenegro
I'm so happy that I haven't stopped to continue learning English and there're lots of opportunities opened for me. Thanks for this course It's really useful!
I'm currently looking for a new job as a Product Analyst in Russia (recenty relocated from there) and maybe I've to try to find a job in Western/Eastern companies as well. Idk if my Enlish level'll be enough but I'll see
Best wishes, Anton!
Thank you for all of this. Went through the whole video and was very valuable.
ALL I CAN SAY..... YOU ARE AWESOME..............................!
Impressive and helpful tutorial. Thanks for this amazing teaching.
I am going to watch this video in two rounds. The first to get overview of the whole idea. The second round to take notes and actually put in the work. I will dedicate 1 hour everyday after my work. So if this goes as planned it should take me 8 to 10 days. I will update my progress here.
First round day 1 done: It was mainly about what data analysis is , what we should expect from the course and how we use jupyter notebook. I have watched 1 hour and will stop here to avoid burnout.
Searching for data analysis course on the web... Bingo got the notification.. No search required 😁
Agar kisi chez ko dil se Chaho to puri kayanat tumhe usse Mila deti hai😊
@@creativebeing1108 yaah truly well said... I believe
25:00 Sakila Database
31:00 Jupyter notebook
1:05:00 Numpy
1:25:00 Data Types
2:48:00 Data Cleaning
found i was looking for. thank you so much.
As he ventures into the captivating world of artificial intelligence, let’s embark on this exciting journey together. I, too, am currently exploring of this field.
Thanks for the introduction part..kindly implement project also from start to end...
The best tutorial in youtube
I heard that we should focus on excel, sql skills for Data Analysis. People are now learning Python because it is getting popular.
So confused
@ifabulocitygirl that makes sense. Thanks.
Can you reply me because I have some confusion I want to clear it please help me
Excel is a good tool however it cannot deal with large data. Image doing all the analysis with even 5000 rows having to draw graph after graph onto different sheet. Excel gets very sluggish. With Python and their libraries with constant updates and support from the community, your good to go with even 1 million rows. Also like he said in the video, it gets cluster and tiring looking at the spreadsheets constantly, I find it quite distracting to do my analysis. SQL is good. I got no comments on that :)
Brilliant blue eyes and this course is awesome!!
This tutorial is perfect. Thank you very much for making it!
See how awesome this course is 😎
where can i find the file csv thx
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...
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.
I agree, he seems to start skimming more and more as you get deeper into the video which was disappointing..
Wes Mckinney as a Visual. Good job!
please help. where can i find the dataset " btc-market-price"?
Bravo 👏 Maestro 👏 Lit 🌠 Impressive 😍
Gratitude 🙏 for your satisfactory Work 💪🚀🌱
what about a "freeMathCamp" or "freeLogicCamp" section?
Sooner or later you will have to use math and logic in programming.
Check out their "Mathematics" playlist.
Your perspective is enlightening. Similar to a book that's an exemplar. "Adapting with Aging" by Various Authors
SHOUTOUT TO EVERYONE LEARNING HOW TO CODE ON THIS CORONA TIMES
Is that enough to study in data analysis?
if you want to go more in depth
Thanks!
NO algo expert,NOOO..i dont want to be a software developer at google.
why not, Google is a big company
@@huseyinsusever5159 iyk yk
Excellent course, you can download the material and practice and check your knowledge. Thank Freecode and Santiago!
54:37 is when he starts to actually teach. Just saying.
Well, I was _teaching_ how to use Jupyter Notebooks before, some people might not know how they work.
You shouldnt discourage my brothet
Either lockdown or corona will kill us but u came as life saver
Great course! The repo for part 7 doesn't exist, fyi
I've updated it, should be working now!
Thank you Santiago Basulto ! The beginner course training was excellent. It was a delicious appetiser. Now I am waiting in anticipation for the main dish :)
Thanks for this tutorial. Can anyone tell me where can I find these csv files ?
I liked it even from the beginning , you are doing a great effort to explain every detail. thank you
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.
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.
Vraiment
generally super! Now I'm in a great mood for the whole day! thank you!:)
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.
The best tutorial ever! Excellent!
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.
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.
It's for beginners in data analysis, not python or programming in general
@@abioyeorimadegun7851 do you recommended it?
@@andredias5061the title says "DATA ANALYSIS WITH *PYTHON*", it's a clickbait and totally confusing( I'm a total noob in coding or analytics)
@@prashansingh8861with python meaning you are supposed to to know python already. Come on guys
2:17:25 DAY 1
hopefully day 2 goes strong, please like this video so that I get the notification to do this course from 2:17:25
** 2:15:22 maybe you could have explained the data frame maybe that could be bettter**
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.
Awesome!! TQVM. 4 hours is not too long.
I just want to marry you guys~ love the course!!
coz of course marry
thanks
Title of video: Data Analysis with Python.
Spends no time coding, just reading websites. 😠
Actually data analysis involve less coding more depends on analytical part
i have not started yet, but i think i will love it
It sounds like he's saying "raiding data" which is honestly so much better