Python for Data Science | Beginner Friendly Full Course in 5 Hours

Starting your data science journey?
Just dipping your toe into Machine Learning?
Not too sure how to get started with the wild world of Python?
I got you.
There’s a ton of stuff to learn when you’re just getting started with data science, but having a good foundation in Python will set you up for success. That being said there’s a lot of fluff that can derail you when you’re learning.
So I put this together.
This is everything I wish I would have learned when starting off my journey in Data Science. It’s all of the Python that I use in my day to day job and it’s more than enough to get you started!
In this video, you’ll learn:
1. How to setup your environment for Python
2. Fundamentals of coding with Python with a focus on Data Science Applications
3. Applications of Python for Data Science along with some practical Python projects
Link to Code: github.com/nicknochnack/PythonForDataScience
Link to Projects:
Project 1 - End to End Machine Learning: kzbin.info/aero/PLgNJO2hghbmjNrHZqplNMEpsW-QLFdvJV
Project 2 - Data Science Basics: kzbin.info/aero/PLgNJO2hghbmjpjt9sa2POi4U0a1-GGTlj
Project 3 - Deep Learning Summarization: kzbin.info/www/bejne/iqTJfaBrns6MmJY
Project 4 - Sentiment Analysis: kzbin.info/www/bejne/qavGq6OdhKqXjtU
Other projects
AI for Gaming: kzbin.info/www/bejne/nnTIe5inbbpjotE
Object Detection: kzbin.info/www/bejne/r6LOeoZ_eK17Y7c
Chapters
0:00 - Start
1:16 - Why you should learn Python
6:08 - How to get started
6:59 - Installing Anaconda
11:42 - Starting Jupyter Notebooks
13:42 - Creating a Jupyter Notebook
16:10 - Jupyter Shortcuts
17:54 - Exporting Jupyter to .py
21:04 - Cell Types
23:16 - Working with Markdown
25:23 - Accessing Documentation
26:42 - Google Colab
28:40 - Watson Studio
33:23 - SECTION 2 Variables & Data Types
34:38 - CRUD
41:13 - Variables
47:18 - Data Types
49:09 - Strings
52:52 - Integers
54:55 - Floats
56:27 - Booleans
1:00:44 - Lists
1:05:28 - Tuples
1:12:43 - Sets
1:21:05 - Dictionaries
1:28:17 - CRUD for Lists
1:30:14 - Creating a List
1:31:33 - Reading a List Using Indexing
1:32:55 - Updating List Values
1:33:58 - Using .append()
1:34:57 - Using .insert()
1:39:21 - CRUD for Dictionaries
1:39:58 - Create a Dictionary
1:41:41 - Read from a Dictionary
1:42:46 - Accessing Dictionary .keys()
1:43:27 - Accessing Dictionary .values()
1:43:57 - Updating Dictionaries
1:46:50 - Deleting from a Dictionary
1:48:54 - SECTION 3 Conditions & Loops
1:52:23 - Conditions and Logic
1:54:47 - if Statement
2:02:54 - else Statement
2:05:28 - elif Statement
2:09:46 - in Statement
2:17:29 - for Loop
2:25:27 - continue, break, pass
2:32:32 - while Loop
2:39:59 - Looping through Dictionaries
2:48:28 - List comprehensions
2:52:00 - SECTION 4 Functions
2:55:59 - Defining Functions
3:02:36 - Positional Arguments
3:10:47 - Multiple Positional Arguments
3:15:34 - Looping with an Index
3:21:35 - Keyword Arguments
3:25:35 - Combining Positional and Keyword Args
3:32:11 - return Keyword
3:34:52 - lambda Functions
3:39:00 - SECTION 5 Classes
3:42:41 - Classes
3:44:52 - class Statement
3:45:45 - __init__ Method
3:47:01 - self keyword
3:49:02 - Assigning properties
3:49:34 - Creating an object
3:51:36 - Methods
4:03:03 - Class Inheritance
4:06:36 - Defining a Child Class
4:08:10 - Inheriting using the super() function
4:18:25 - SECTION 6 - Modules and Packages
4:21:45 - Modules
4:23:05 - Creating a helper module
4:25:42 - Importing modules
4:27:25 - Accessing Python Packages
4:29:00 - Working with APIs
4:32:02 - Installing packages with pip install
4:33:42 - Viewing installed packages with pip list
4:34:36 - Importing Packages
4:35:41 - Making API calls with requests.get()
4:45:46 - Parsing JSON
4:57:24 - SECTION 7 Files & Error handling
5:01:26 - Working with Files
5:02:32 - Writing Files using the with statement
5:07:04 - Reading from files
5:11:01 - Error Handling
5:14:26 - Using try except statements
5:19:37 - SECTION 8 Math and Projects
5:22:48 - Math in Python
5:24:54 - Math Operators
5:25:20 - Addition
5:26:01 - Subtraction
5:26:42 - Division
5:27:37 - Floor Division
5:29:06 - Modulus
5:31:28 - Multiplication
5:31:59 - Power
5:32:52 - Rounding with round()
5:34:15 - Absolute Values abs()
5:38:29 - Math Package
5:40:45 - Python Projects
Oh, and don't forget to connect with me!
LinkedIn: bit.ly/324Epgo
Facebook: bit.ly/3mB1sZD
GitHub: bit.ly/3mDJllD
Patreon: bit.ly/2OCn3UW
Join the Discussion on Discord: bit.ly/3dQiZsV
Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!

[@NicholasRenotte]

Here's the blog post that goes with the course guys! towardsdatascience.com/how-i-would-learn-python-for-data-science-if-i-had-to-start-over-83fc40ab93ba

[@yevgeniybondar7509]

Do you offer tutoring? I need some more education

[@NicholasRenotte]

@@yevgeniybondar7509 not just yet but will probably have a few courses out soonish.

[@beyobeya]

Perfect for newbies. Thanks a lot 🙏

[@namangoyal8477]

@@NicholasRenotte Please do one on deep learning research papers. I will buy.

[@bhavukxd]

Timestamps 00:04 - Introduction to Python's significance in data science. 01:54 - Python is essential for data science due to its popularity and innovations. 05:53 - Python is essential for data science; let’s set up your coding environment. 07:47 - Installing Anaconda for Data Science setup. 11:39 - Setting up Jupyter Notebook for Python coding in data science. 13:32 - Creating a Jupyter Notebook and writing your first Python line. 17:00 - Learn to delete cells and export Jupyter notebooks to Python scripts. 18:46 - Using Jupyter Notebook for Python programming. 22:05 - Using code and markdown cells effectively in Jupyter notebooks. 23:39 - Using Markdown to document Jupyter Notebook code effectively. 27:03 - Introduction to Google Colab for Python users. 28:42 - Introduction to Watson Studio for enterprise data science workflows. 32:12 - Introduction to Jupyter Notebook for Python data science. 33:55 - Introduction to Python fundamentals and essential programming concepts. 37:31 - Creating a Jupyter Notebook for Python coding. 39:07 - Starting and setting up Jupyter Notebooks for Python coding. 42:42 - Understanding variable assignment and data types in Python. 44:28 - Understanding variable reassignment and data types in Python. 47:54 - Understanding variables and data types is crucial for Python data science. 49:31 - Understanding comments and strings in Python code. 52:58 - Understanding and creating integer variables in Python. 54:50 - Introduction to float data type in Python. 58:44 - Understanding boolean values and type casting in Python. 1:00:32 - Introduction to type casting and lists in Python for data science. 1:04:20 - Introduction to Python data types: lists, strings, integers, floats, booleans, and tuples. 1:06:02 - Tuples are less common than lists and are used mainly for coordinates. 1:09:31 - Tuples are immutable data types, unlike lists which are mutable. 1:11:14 - Tuples are immutable, meaning their values cannot be changed after creation. 1:15:00 - Sets in Python eliminate duplicate values and are mutable. 1:16:56 - Learn how to remove duplicates from lists using sets in Python. 1:20:26 - Overview of basic Python data types: boolean, list, tuple, set, and dictionary. 1:22:24 - Understanding dictionary variables in Python for data science. 1:26:04 - Overview of essential Python data types and their characteristics. 1:27:51 - Introduction to lists and their operations in Python. 1:31:20 - Understanding lists in Python with indexing and built-in functions. 1:33:08 - Learn to update and add values in a Python list. 1:36:46 - Learn how to manipulate lists in Python including insertion and deletion. 1:38:26 - Learn how to manipulate lists in Python, including creation, updating, and deletion. 1:41:54 - Working with dictionaries in Python for data retrieval. 1:43:39 - Managing dictionaries: updating and adding key-value pairs. 1:47:15 - Learn to delete key-value pairs in Python dictionaries. 1:49:09 - Introduction to conditions and loops in Python. 1:52:38 - Learn how to create and manage markdown cells in Jupyter Notebook. 1:54:13 - Understanding and applying the if statement in Python for logical conditions. 1:57:42 - Understanding logical operators in Python for conditional checks. 1:59:25 - Understanding logical operators for conditional statements in Python. 2:02:45 - Understanding conditional statements in Python for probability checks. 2:04:32 - Understanding conditionals in Python with proper indentation. 2:08:22 - Understanding conditional statements and list creation in Python. 2:10:12 - Using Python's 'in' statement for efficient list value checks. 2:13:47 - Understanding conditional statements and their structure in Python. 2:15:41 - Understanding string quotes and the importance of loops in Python. 2:19:03 - Looping through a list of names with conditions in Python. 2:20:44 - Implementing conditions within loops to trigger specific actions. 2:24:05 - Using break and continue in loops to control flow. 2:25:53 - Using continue to skip iterations in a loop. 2:29:09 - Understanding loops in Python with examples of for, break, continue, and pass statements. 2:31:00 - Introduction to looping in Python using 'for' and 'while' loops. 2:34:07 - Incrementing launch count with Python syntax and conditions. 2:35:58 - Using loops and break statements to control shuttle launches in Python. 2:39:09 - Understanding Python error debugging and while loops for shuttle launches. 2:40:52 - Understanding loops and dictionaries in Python programming. 2:44:25 - Learn how to access and loop through dictionary values in Python. 2:46:18 - Understanding how to loop through Python dictionaries using items method. 2:49:57 - Understanding list comprehensions and loops in Python. 2:51:31 - Exploring functions and their applications in Python for data science. 2:55:07 - Creating and structuring cells in Jupyter Notebook for functions. 2:56:48 - Functions streamline code organization and reuse in data science. 3:00:20 - Functions allow repetitive tasks and enhance power with arguments. 3:02:10 - Creating and using custom functions with lists in Python. 3:06:02 - Understanding custom functions and positional arguments in Python. 3:07:43 - Using for loops and functions for cleaner, reusable Python code. 3:11:16 - Creating functions with multiple positional arguments in Python. 3:12:55 - Understanding positional arguments in Python functions. 3:16:54 - Using enumerate for indexing and values in Python lists. 3:18:42 - Learn positional arguments and list looping in Python functions. 3:22:16 - Understanding default keyword arguments in Python functions. 3:24:04 - Understanding keyword arguments and their default values in functions. 3:27:42 - Understanding argument types in Python functions: positional vs keyword. 3:29:34 - Understanding positional and keyword arguments in Python functions. 3:33:09 - Understanding how to use return statements in Python functions. 3:35:13 - Understanding functions in Python with arguments and lambda expressions. 3:38:44 - Introduction to Object-Oriented Programming in Python. 3:40:37 - Navigating to Jupyter Notebook using Command Prompt. 3:43:59 - Creating classes allows for customizable object templates in Python. 3:45:41 - Introduction to class definition and initialization in Python. 3:49:13 - Defining a class and creating instances in Python. 3:50:47 - Accessing and customizing class attributes in Python. 3:54:14 - Creating and using methods in Python classes for data processing. 3:55:55 - Introduction to Object-Oriented Programming in Python using classes. 3:59:26 - Understanding class creation and method definitions in Python. 4:01:02 - Creating and accessing attributes and methods in Python classes. 4:04:33 - Understanding inheritance in Python's OOP enhances code reusability. 4:06:26 - Introduction to class inheritance in Python using an astronaut example. 4:10:17 - Inheritance allows classes to use attributes and methods from parent classes. 4:12:12 - Extending methods in Python to calculate age on return. 4:15:49 - Understanding class inheritance and method overriding in Python. 4:17:33 - Understanding classes, inheritance, and modular code in Python. 4:21:05 - Understanding module creation and importation in Jupyter for efficient coding. 4:22:50 - Creating a Python script in VS Code for reusable functions. 4:26:10 - Using modules in Jupyter for efficient data science workflows. 4:27:59 - Introduction to essential Python libraries for data science. 4:31:41 - Organize code documentation and install packages in Jupyter Notebook. 4:33:19 - Managing output and importing packages in a Python environment. 4:36:46 - Understanding the GET request method and API endpoints in Python. 4:38:33 - Creating and using API endpoints in Python. 4:42:17 - Understanding popular Python packages enhances development skills. 4:44:07 - Making API calls in Python using HTTP request methods. 4:47:44 - Understanding dictionary access and API calls in Python. 4:49:35 - Understanding API responses and making ISS API calls. 4:53:12 - Understanding API calls and HTTP methods in Python using requests. 4:55:06 - APIs enhance data access and process automation in data science. 4:58:40 - Starting Jupyter Notebook for Python coding. 5:00:20 - Understand structured coding and file handling using try-except blocks. 5:03:30 - Understanding file handling in Python using the open function. 5:05:09 - Learn how to write to a file using Python's write method. 5:08:31 - Understanding file reading and error handling in Python. 5:10:15 - Introduction to file handling and error management in Python. 5:13:48 - Sets in Python are immutable and cannot be modified directly. 5:15:29 - Error handling enhances code robustness in software development. 5:19:02 - Introduction to math functions and data science projects in Python. 5:20:50 - Introduction to Python math through Jupyter Notebook setup. 5:24:22 - Introduction to Python math operators for basic arithmetic. 5:26:07 - Understanding basic arithmetic operations in Python. 5:29:56 - Understanding modular arithmetic and basic math functions in Python. 5:31:55 - Understanding mathematical operations in Python, including power and rounding functions. 5:35:25 - Understanding absolute values, minimum, and maximum functions in Python. 5:37:15 - Recap of basic arithmetic operations and functions in Python. 5:40:41 - Explore additional projects to enhance your Python and data science skills. 5:42:21 - Learn to build machine learning models using scikit-learn for regression tasks.

[@s4lvat0re]

it took me like two weeks but i finally got through this tutorial :) love your channel, keep it going

[@mocabeentrill]

Hi Nick. Just finished this python for beginners course. It's professionally compiled, explained thoroughly and adequately. Thanks for comprehensively curating the basics on python . Blessings!

[@cripthereaper1075]

You are the most amazing content creator, entertaining and educational, simple but clean. Overall amazing, praying for your succes!

[@NicholasRenotte]

Thanks sooo much!!

[@morenomt27]

why am I finding this channel just today?! This is the best!

[@aimenbaig6201]

Hey dude! just discovered your channel, the content here is insanely amazing. I am an hour into this course now. I really needed this! Love it

[@SasuKev099009]

Finished this in two days 🗿

[@OkamiChan-lv8de]

bro I am going to finish in a single day lol

[@Destroy34-g4l]

You just cook bro

[@Jackson_rw]

Man, thank you so much Nick. I have been struggling with learning python until I found this little beauty. You Legend!

[@kanikashama3016]

Thankyou sooo much for getting my interest back to Python. This is the first 1 shot video I have completed. CONGRATULATIONS TO MYSELF 🎉🎉🥳🥳🥳

[@kevincdurand]

Im learning something new everytime I watch your videos. Fantastic work!

[@prathibhaDhadbanjan]

Dear Nicholas, thank you so much for sharing this video. After watching it, I feel much more confident in learning Python and the fundamental concepts necessary for data science.

[@goadyjerry967]

MAN I BELIEVE YOU SHOULD BE A PROFESSOR OF COMPUTER SCIENCE , I JUST LIKE THE WAY YOU TEACH

[@ashleysami1640]

Loved all the live streams!

[@NicholasRenotte]

Ayyyyee, love having you there!

[@aishatyakub8188]

After searching everywhere for a comprehensive beginner friendly Python video, I finally found a Gem tutor❤❤

[@zoroXgamings]

Best , always on top 🤗👍

[@blessinq5]

You sir, are a fantastic tutor. This was fun, easy to follow and very helpful. Thank you for your work and wishing lots of blessings on you.

[@bellemarravelo6002]

Hi sir, thanks for sharing codes and please make more videos about codes, it would be helpful for students out their, like me but I graduated now, so hopefully keep sharing and we gain some knowledge from you...

[@MM-un7wg]

This an excellent video - Great work Nicholas

[@jayparth]

You are such a gem, Nicholas Sir. Much thanks for this free enlightenment ✨

[@saharnazeri631]

Amazingggg that is exactly what i am looking for

[@flutterflowhack]

Your explainations are so good. I didnt know anything regarding python but im able to follow through. Tank you for repettition and emphasis. Because of your video, I am posistive I can be a data scientist again.

[@Anbrinx]

Thank you so much Sir. I'm trying to learn data science. Your lecture helped me a lot. I want to exell in NumPy & Pandas. Lord Almighty Bless you Sir.

[@namangoyal8477]

Thank you so much nick for this video. Helped a ton.

[@abrh2793]

Love the guide! Looking forward for more these! Keep up

[@zafry]

Addicted to your Channel Brother 😍 ! A great tutor .

[@wusportmanagement6635]

Thanks Nicholas!!!! Impecable!!!

[@anabelamiranda8943]

Excellent. Such amazing explanations! Thank you so much.

[@EmmanuelPreciousJoy]

You have been very helpful. Thank you so much!

[@ScriptureFirst]

I’m going to live in this video for a few weeks. Been fighting to get jupyter to run my GTX 1080 via anaconda environments & I’m killing all my momentum gained over the summer in this weeklong battle.

[@ege621]

you mean by using cuda and cudnn?

[@ameerazam3269]

Cheers one more time .......................thank you Man

[@digigoliath]

Super Awesome!!! TQVM!!

[@ZulhadWajajo]

Thank you so much, this video really means to me, I really appreciated it 🙏🙏🙏

[@yusrahafizi4975]

You are amazing sir I learn a lot from your video, tnx for sharing your knowledge with us.

[@hedikh7907]

Thank you man

[@MegaOmarTv]

amazing work! I've learned a lot from this one 5:09:14 that's actually the right way to say journal in french 😆 (but it means newspaper though lol)

[@pwdrhrn]

At around 1:26, when you are describing dictionaries, it would be helpful if you described when to use {} and when to use []. They are defined with {} - essentially a set of dictionary items, yet when you want a single item from the set, you used [] as an index. Kind of confusing.

[@sergiodelpino7548]

ufff is perfect!!!...muy buen video!

[@pwdrhrn]

Around 2:40, where you explain how to find errors from the error message, you could show the feature of jupyter that turns on line numbers. (view/Toggle Line Numbers. in my version of Jupyter)

[@shwetapatil1726]

Nice tutorial.. can you also create a video on federated learning?

[@kadichidu4171]

Please make more videos on data science..love from India

[@mukulsaklani1545]

Big fan!

[@NicholasRenotte]

Thanks a bunch @Mukul!

[@venugowda4533]

Hey bro Awesome content, I am looking for an internship and wanted to enter the data science field. Could you please share to how to start with the projects like where exactly to find the projects? (How long does it take to get a better grasp on this?)

[@NicholasRenotte]

Yup, will do a vid on gearing up for jobs!

[@Hosam_2]

I really appreciate you and the course; it's amazing. I have one question about the projects. Could you recommend an order in which we should study these projects or videos? What order would be the most beneficial for us?

[@NicolasGertler]

Great video, this was very helpful, thank you! Also, I have to ask: Can you please link the wallpaper that you're using (I think it's great!)

[@NicholasRenotte]

Here you go :) www.reddit.com/r/wallpapers/comments/7f3mgz/small_memory_by_mikael_gustafsson_1440p/

[@NicolasGertler]

@@NicholasRenotte Thank you!

[@yangliu5727]

Nick we want see a full video about API's, thankyou

[@chenvinh9232]

Hi Nick ! I think you should update this tutorial (Mind Blowing Machine Learning Apps with Node.JS and Watson Machine Learning || Python ML PT.5) Some info have been omitted and we could not follow the whole excellent tutorial. like WML_INSTANCE_ID, WML_SCORING_URL and AUTH_URL. Hope you can fix it soon! Thanks a lot fabulous professor Nick

[@NicholasRenotte]

I've been meaning to do it. Will try to bump it up the list!

[@animeshkumar1684]

Excellent content and very clearly explained!

[@bomb143]

Hello! I wanted to ask, all that you taught about python in this video is it enough for data science ? Or their is something more we have to learn?

[@ezranwobodo4912]

Quality!

[@mdafzal8367]

Hi sir, thank you for this video.I am so keen to learn about data scientist but I am just a beginner. So i am just wandering what is my next step should be after this video to be a data scientist? If you suggest me something, it will be really helpful. Thank you for your time.

[@NicholasRenotte]

Give this a go :) kzbin.info/aero/PLgNJO2hghbmjNrHZqplNMEpsW-QLFdvJV

[@aliosmanbaykus3525]

Awesome tutorial!

[@yevgeniybondar7509]

Thank you

[@tzahigeller6266]

Hi Nick Just so the tutorial and it is truly amazing, thanks so much! So you have a ton of videos in the channel and I was wondering if there is some sort of ordering or everyone handles a different topic Do you have a recommendation of what to see when? I think it will be truly helpful for someone who want to get into ML and DL using your resources but is a bit confused and what to see when Thanks again

[@singhbits8540]

THANK YOU

[@KenJee_ds]

🔥

[@irshaadbahemia666]

Hello nicholas renotte.......thanks for your videos which I would sometimes watch. Do I have to know python beyond the basics such itertools, functools and any other intermediate and advanced concepts in python ?

[@Hgyr-k8j]

Thank you❤

[@sanyamkarnavat9745]

Appreciate it 🤟

[@morancium]

Nice Sir

[@vikashchand.]

If anyone watching this is not subscribed, you're missing out! 🤷‍♂️

[@NicholasRenotte]

My guy!!!

[@WhiphTikoto]

Hey a tiny bit unrelated to the video, but I'm thinking of getting a MAC to begin coding for data science. Are there any cons to using a mac over a windows based laptop?

[@farshidghafarzadeh3759]

Hi. it's a wonderful tutorial but the quality is so low . would you mind upload high quality?

[@NicholasRenotte]

KZbin is still processing as we speak, should be 1080p once it’s done!

[@NicholasRenotte]

Just finished processing! Check it out now my guy :)

[@farshidghafarzadeh3759]

@@NicholasRenotte thanks alot

[@zafry]

Amazing ! how do you do the snapping windows thing ? 4:19:53

[@kisho2679]

When use Streamlit instead of Dash with Python?

[@bro-lk5qj]

Thanks bro

[@aliusman6339]

Hello for the sign language detection video I only collected and trained images for hello. Now I want to collect images and do more signs for it what do I have to do now. I think it was regarding freezing the model but I'm not sure.

[@NicholasRenotte]

Yup, freeze the model, export and repoint fine_tune_checkpoint to that exported model. Try commenting on the video it relates to @Ali, this helps be identify what you're referring to.

[@mcdeheeger]

I had a question... how much of this is memorized through muscle memory over time versus referenced from other material? Thanks! How long does it take to feel comfortable coding like this from memory?

[@nishantpatil8384]

what should i watch next?

[@firosiam7786]

I can't seem to memorize all these functions methods and keep it in memory for long periods would that make me unfit to learn programing??

[@NicholasRenotte]

HELL NAH! You keep going my guy, you'll learn it. Practice makes perfect. BTW, I google all of this stuff all the time!

[@gerlandoterrasi9133]

I am furious because I follow the installation instructions for my Windows 7 32-bit computer and the images I get do not match the images I see in the video.

[@NicholasRenotte]

Hmmm, what's happening @Gerlando?

[@CartVista-kb4mw]

I think you shouldn't have started from the beginning i mean it is data science

[@m.tsaqifwismadi4625]

26:32

[@m.tsaqifwismadi4625]

1:30:01

[@m.tsaqifwismadi4625]

2:31:13

[@m.tsaqifwismadi4625]

4:18:25

[@JoseAntonio-nq6nz]

use chapstick

[@Kaurenjalalifarahani]

Starts at 34:00