Python Roadmap for 2025: Complete Guide
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
๐ ๐ฃ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ : https://topmate.io/coding/914624
๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Join What's app channel for jobs updates: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
๐ ๐ฃ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ : https://topmate.io/coding/914624
๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Join What's app channel for jobs updates: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐6โค2
If you're into deep learning, then you know that students usually one of the two paths:
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:
Before we start, What is NLP?
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through language.
It enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful.
Data scientists need NLP to analyze, process, and generate insights from large volumes of textual data, aiding in tasks ranging from sentiment analysis to automated summarization.
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through language.
It enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful.
Data scientists need NLP to analyze, process, and generate insights from large volumes of textual data, aiding in tasks ranging from sentiment analysis to automated summarization.
๐2
Tokenization
Tokenization involves breaking down text into smaller units, such as words or phrases. This is the first step in preprocessing textual data for further analysis or NLP applications.
Tokenization involves breaking down text into smaller units, such as words or phrases. This is the first step in preprocessing textual data for further analysis or NLP applications.
๐2
Part-of-Speech Tagging:
This process involves identifying the part of speech for each word in a sentence (e.g., noun, verb, adjective). It is crucial for various NLP tasks that require understanding the grammatical structure of text.
This process involves identifying the part of speech for each word in a sentence (e.g., noun, verb, adjective). It is crucial for various NLP tasks that require understanding the grammatical structure of text.
โค1
Stemming and Lemmatization
These techniques reduce words to their base or root form. Stemming cuts off prefixes and suffixes, while lemmatization considers the morphological analysis of the words, leading to more accurate results.
These techniques reduce words to their base or root form. Stemming cuts off prefixes and suffixes, while lemmatization considers the morphological analysis of the words, leading to more accurate results.
โค1
Named Entity Recognition (NER)
NER identifies and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It's essential for tasks like data extraction from documents and content classification.
NER identifies and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It's essential for tasks like data extraction from documents and content classification.
โค2
Sentiment Analysis
This technique determines the emotional tone behind a body of text. It's widely used in business and social media monitoring to gauge public opinion and customer sentiment.
This technique determines the emotional tone behind a body of text. It's widely used in business and social media monitoring to gauge public opinion and customer sentiment.
Complete Roadmap to land a Data Scientist job in 2025
Phase 1: Build Foundations (3-6 months)
1. Learn Python programming basics
2. Understand statistics and mathematics concepts (linear algebra, calculus, probability)
3. Familiarize yourself with data visualization tools (Matplotlib, Seaborn)
Phase 2: Data Science Skills (6-9 months)
1. Master machine learning algorithms (scikit-learn, TensorFlow)
2. Learn data manipulation frameworks (Pandas, NumPy)
3. Study data visualization libraries (Plotly, Bokeh)
4. Understand database management systems (SQL, NoSQL)
Phase 3: Practice and Projects (3-6 months)
1. Work on personal projects (Kaggle competitions, datasets)
2. Participate in data science communities (GitHub, Reddit)
3. Build a portfolio showcasing skills
Phase 4: Job Preparation (1-3 months)
1. Update resume and online profiles (LinkedIn)
2. Practice whiteboarding and coding interviews
3. Prepare answers for common data science questions
Best Resources to learn Data Science ๐๐
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING๐๐
Phase 1: Build Foundations (3-6 months)
1. Learn Python programming basics
2. Understand statistics and mathematics concepts (linear algebra, calculus, probability)
3. Familiarize yourself with data visualization tools (Matplotlib, Seaborn)
Phase 2: Data Science Skills (6-9 months)
1. Master machine learning algorithms (scikit-learn, TensorFlow)
2. Learn data manipulation frameworks (Pandas, NumPy)
3. Study data visualization libraries (Plotly, Bokeh)
4. Understand database management systems (SQL, NoSQL)
Phase 3: Practice and Projects (3-6 months)
1. Work on personal projects (Kaggle competitions, datasets)
2. Participate in data science communities (GitHub, Reddit)
3. Build a portfolio showcasing skills
Phase 4: Job Preparation (1-3 months)
1. Update resume and online profiles (LinkedIn)
2. Practice whiteboarding and coding interviews
3. Prepare answers for common data science questions
Best Resources to learn Data Science ๐๐
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING๐๐
๐7โค3
5 Data Analytics Project Ideas to boost your resume:
1. Stock Market Portfolio Optimization
2. YouTube Data Collection & Analysis
3. Elections Ad Spending & Voting Patterns Analysis
4. EV Market Size Analysis
5. Metro Operations Optimization
1. Stock Market Portfolio Optimization
2. YouTube Data Collection & Analysis
3. Elections Ad Spending & Voting Patterns Analysis
4. EV Market Size Analysis
5. Metro Operations Optimization
๐9
Jupyter Notebooks are essential for data analysts working with Python.
Hereโs how to make the most of this great tool:
1. ๐ข๐ฟ๐ด๐ฎ๐ป๐ถ๐๐ฒ ๐ฌ๐ผ๐๐ฟ ๐๐ผ๐ฑ๐ฒ ๐๐ถ๐๐ต ๐๐น๐ฒ๐ฎ๐ฟ ๐ฆ๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ:
Break your notebook into logical sections using markdown headers. This helps you and your colleagues navigate the notebook easily and understand the flow of analysis. You could use headings (#, ##, ###) and bullet points to create a table of contents.
2. ๐๐ผ๐ฐ๐๐บ๐ฒ๐ป๐ ๐ฌ๐ผ๐๐ฟ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐:
Add markdown cells to explain your methodology, code, and guidelines for the user. This Enhances the readability and makes your notebook a great reference for future projects. You might want to include links to relevant resources and detailed docs where necessary.
3. ๐จ๐๐ฒ ๐๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐๐ฒ ๐ช๐ถ๐ฑ๐ด๐ฒ๐๐:
Leverage ipywidgets to create interactive elements like sliders, dropdowns, and buttons. With those, you can make your analysis more dynamic and allow users to explore different scenarios without changing the code. Create widgets for parameter tuning and real-time data visualization.
๐ฐ. ๐๐ฒ๐ฒ๐ฝ ๐๐ ๐๐น๐ฒ๐ฎ๐ป ๐ฎ๐ป๐ฑ ๐ ๐ผ๐ฑ๐๐น๐ฎ๐ฟ:
Write reusable functions and classes instead of long, monolithic code blocks. This will improve the code maintainability and efficiency of your notebook. You should store frequently used functions in separate Python scripts and import them when needed.
5. ๐ฉ๐ถ๐๐๐ฎ๐น๐ถ๐๐ฒ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐๐ณ๐ณ๐ฒ๐ฐ๐๐ถ๐๐ฒ๐น๐:
Utilize libraries like Matplotlib, Seaborn, and Plotly for your data visualizations. These clear and insightful visuals will help you to communicate your findings. Make sure to customize your plots with labels, titles, and legends to make them more informative.
6. ๐ฉ๐ฒ๐ฟ๐๐ถ๐ผ๐ป ๐๐ผ๐ป๐๐ฟ๐ผ๐น ๐ฌ๐ผ๐๐ฟ ๐ก๐ผ๐๐ฒ๐ฏ๐ผ๐ผ๐ธ๐:
Jupyter Notebooks are great for exploration, but they often lack systematic version control. Use tools like Git and nbdime to track changes, collaborate effectively, and ensure that your work is reproducible.
7. ๐ฃ๐ฟ๐ผ๐๐ฒ๐ฐ๐ ๐ฌ๐ผ๐๐ฟ ๐ก๐ผ๐๐ฒ๐ฏ๐ผ๐ผ๐ธ๐:
Clean and secure your notebooks by removing sensitive information before sharing. This helps to prevent the leakage of private data. You should consider using environment variables for credentials.
Keeping these techniques in mind will help to transform your Jupyter Notebooks into great tools for analysis and communication.
I have curated the best interview resources to crack Python Interviews ๐๐
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Hereโs how to make the most of this great tool:
1. ๐ข๐ฟ๐ด๐ฎ๐ป๐ถ๐๐ฒ ๐ฌ๐ผ๐๐ฟ ๐๐ผ๐ฑ๐ฒ ๐๐ถ๐๐ต ๐๐น๐ฒ๐ฎ๐ฟ ๐ฆ๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ:
Break your notebook into logical sections using markdown headers. This helps you and your colleagues navigate the notebook easily and understand the flow of analysis. You could use headings (#, ##, ###) and bullet points to create a table of contents.
2. ๐๐ผ๐ฐ๐๐บ๐ฒ๐ป๐ ๐ฌ๐ผ๐๐ฟ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐:
Add markdown cells to explain your methodology, code, and guidelines for the user. This Enhances the readability and makes your notebook a great reference for future projects. You might want to include links to relevant resources and detailed docs where necessary.
3. ๐จ๐๐ฒ ๐๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐๐ฒ ๐ช๐ถ๐ฑ๐ด๐ฒ๐๐:
Leverage ipywidgets to create interactive elements like sliders, dropdowns, and buttons. With those, you can make your analysis more dynamic and allow users to explore different scenarios without changing the code. Create widgets for parameter tuning and real-time data visualization.
๐ฐ. ๐๐ฒ๐ฒ๐ฝ ๐๐ ๐๐น๐ฒ๐ฎ๐ป ๐ฎ๐ป๐ฑ ๐ ๐ผ๐ฑ๐๐น๐ฎ๐ฟ:
Write reusable functions and classes instead of long, monolithic code blocks. This will improve the code maintainability and efficiency of your notebook. You should store frequently used functions in separate Python scripts and import them when needed.
5. ๐ฉ๐ถ๐๐๐ฎ๐น๐ถ๐๐ฒ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐๐ณ๐ณ๐ฒ๐ฐ๐๐ถ๐๐ฒ๐น๐:
Utilize libraries like Matplotlib, Seaborn, and Plotly for your data visualizations. These clear and insightful visuals will help you to communicate your findings. Make sure to customize your plots with labels, titles, and legends to make them more informative.
6. ๐ฉ๐ฒ๐ฟ๐๐ถ๐ผ๐ป ๐๐ผ๐ป๐๐ฟ๐ผ๐น ๐ฌ๐ผ๐๐ฟ ๐ก๐ผ๐๐ฒ๐ฏ๐ผ๐ผ๐ธ๐:
Jupyter Notebooks are great for exploration, but they often lack systematic version control. Use tools like Git and nbdime to track changes, collaborate effectively, and ensure that your work is reproducible.
7. ๐ฃ๐ฟ๐ผ๐๐ฒ๐ฐ๐ ๐ฌ๐ผ๐๐ฟ ๐ก๐ผ๐๐ฒ๐ฏ๐ผ๐ผ๐ธ๐:
Clean and secure your notebooks by removing sensitive information before sharing. This helps to prevent the leakage of private data. You should consider using environment variables for credentials.
Keeping these techniques in mind will help to transform your Jupyter Notebooks into great tools for analysis and communication.
I have curated the best interview resources to crack Python Interviews ๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
๐4
For working professionals willing to pivot their careers to AI:
Here are the steps you can take right now:
1. Learn the basics of AI
==================
You need to understand the differences among various AI jargons (e.g., what is the difference between statistical ML vs. deep learning? What exactly is an LLM?) and when to use which to solve a given business problem. Many fast-paced courses can teach you all of this without having to learn coding. (Shameless plug: I have a course that I will add in the comments section below)
2. Build an AI project in your current work
==============================
Find a problem statement in your current work that can be solved using AI and will deliver some value. Work on this during your extra hours, then showcase it to your management to get official approval to make it a full-fledged project.
3. Collaborate with the AI team in your company for inner sourcing
================================================
Many companies have the concept of inner sourcing where, say, an AI team is too busy and has a list of tasks they have opened on their GitHub repository that others can work on. Use this as an opportunity to do some real AI work and build rapport with the AI team.
4. Attend AI conferences
==================
By attending AI conferences, you will not only learn but also build a network with AI professionals who will help you in your AI career journey.
5. Attend an AI bootcamp at a university or online learning company
=================================================
Artificial Intelligence
๐Telegram Link: https://t.iss.one/addlist/4q2PYC0pH_VjZDk5
Like for more โค๏ธ
All the best ๐๐
Here are the steps you can take right now:
1. Learn the basics of AI
==================
You need to understand the differences among various AI jargons (e.g., what is the difference between statistical ML vs. deep learning? What exactly is an LLM?) and when to use which to solve a given business problem. Many fast-paced courses can teach you all of this without having to learn coding. (Shameless plug: I have a course that I will add in the comments section below)
2. Build an AI project in your current work
==============================
Find a problem statement in your current work that can be solved using AI and will deliver some value. Work on this during your extra hours, then showcase it to your management to get official approval to make it a full-fledged project.
3. Collaborate with the AI team in your company for inner sourcing
================================================
Many companies have the concept of inner sourcing where, say, an AI team is too busy and has a list of tasks they have opened on their GitHub repository that others can work on. Use this as an opportunity to do some real AI work and build rapport with the AI team.
4. Attend AI conferences
==================
By attending AI conferences, you will not only learn but also build a network with AI professionals who will help you in your AI career journey.
5. Attend an AI bootcamp at a university or online learning company
=================================================
Artificial Intelligence
๐Telegram Link: https://t.iss.one/addlist/4q2PYC0pH_VjZDk5
Like for more โค๏ธ
All the best ๐๐
๐4โค2
Forwarded from Health Fitness & Diet Tips - Gym Motivation ๐ช
Food is Medicine:
๐ง. Garlic - good for the immune system
๐. Bananas - good for the nerves
๐ . Sweet potatoes - good for digestion
๐ฐ. Walnuts - good for memory
๐. Oranges - good for the skin
๐ฅฌ. Kale - good for the bones
๐ป. Chia seeds - good for the heart
๐ถ. Peppers - good for metabolism
๐. Mushrooms - good for the immune system
๐ . Tomatoes - good for the blood
๐ซ. Blueberries - good for the brain
- If you aren't currently following us, you'll probably never see us again. ๐ฟ
๐ง. Garlic - good for the immune system
๐. Bananas - good for the nerves
๐ . Sweet potatoes - good for digestion
๐ฐ. Walnuts - good for memory
๐. Oranges - good for the skin
๐ฅฌ. Kale - good for the bones
๐ป. Chia seeds - good for the heart
๐ถ. Peppers - good for metabolism
๐. Mushrooms - good for the immune system
๐ . Tomatoes - good for the blood
๐ซ. Blueberries - good for the brain
- If you aren't currently following us, you'll probably never see us again. ๐ฟ
๐7
โญโญโญ Advance Level Data science Projects โญโญโญ
1) Identify your Digits Dataset : https://www.kaggle.com/c/digit-recognizer/data
2) Recommendation Engine : https://cseweb.ucsd.edu/~jmcauley/datasets.html
3) Visual QA : https://visualqa.org/download.html
4) Vox Celebrity : https://www.robots.ox.ac.uk/~vgg/data/voxceleb/
5) Breast cancer classification : https://www.kaggle.com/martinab/breast-cancer-classification-wisconsin-dataset
6) Traffic signals : https://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset
7) Image caption generator : https://academictorrents.com/details/9dea07ba660a722ae1008c4c8afdd303b6f6e53b
1) Identify your Digits Dataset : https://www.kaggle.com/c/digit-recognizer/data
2) Recommendation Engine : https://cseweb.ucsd.edu/~jmcauley/datasets.html
3) Visual QA : https://visualqa.org/download.html
4) Vox Celebrity : https://www.robots.ox.ac.uk/~vgg/data/voxceleb/
5) Breast cancer classification : https://www.kaggle.com/martinab/breast-cancer-classification-wisconsin-dataset
6) Traffic signals : https://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset
7) Image caption generator : https://academictorrents.com/details/9dea07ba660a722ae1008c4c8afdd303b6f6e53b
๐7
Practice projects to consider:
1. Implement a basic search engine: Read a set of documents and build an index of keywords. Then, implement a search function that returns a list of documents that match the query.
2. Build a recommendation system: Read a set of user-item interactions and build a recommendation system that suggests items to users based on their past behavior.
3. Create a data analysis tool: Read a large dataset and implement a tool that performs various analyses, such as calculating summary statistics, visualizing distributions, and identifying patterns and correlations.
4. Implement a graph algorithm: Study a graph algorithm such as Dijkstra's shortest path algorithm, and implement it in Python. Then, test it on real-world graphs to see how it performs.
1. Implement a basic search engine: Read a set of documents and build an index of keywords. Then, implement a search function that returns a list of documents that match the query.
2. Build a recommendation system: Read a set of user-item interactions and build a recommendation system that suggests items to users based on their past behavior.
3. Create a data analysis tool: Read a large dataset and implement a tool that performs various analyses, such as calculating summary statistics, visualizing distributions, and identifying patterns and correlations.
4. Implement a graph algorithm: Study a graph algorithm such as Dijkstra's shortest path algorithm, and implement it in Python. Then, test it on real-world graphs to see how it performs.
๐6
Data Analytics Projects for Beginners ๐
Web Scraping
https://github.com/shreyaswankhede/IMDb-Web-Scraping-and-Sentiment-Analysis
Product Price Scraping and Analysis
https://github.com/CodesdaLu/Web-Scrapping
News Scraping
https://github.com/rohit-yadav/scraping-news-articles
Real Time Stock Price Scraping with Python
https://youtu.be/rONhdonaWUo?si=A3oDEVbLIAP78cCz
Zomato Analysis
https://youtu.be/fFi_TBw27is?si=E0iLd3J06YHfQkRk
IPL Analysis
https://github.com/Yashmenaria1/IPL-Data-Exploration
https://www.youtube.com/watch?v=ur-v0dv0Qtw
https://www.youtube.com/watch?v=ur-v0dv0Qtw
Football Data Analysis
https://youtu.be/yat7soj__4w?si=h5CLIvVFzzKm8IEP
Market Basket Analysis
https://youtu.be/Ne8Sbp2hJIk?si=ThEuvdOnRrpcVjOg
Customer Churn Prediction
https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction
Employeeโs Performance for HR Analytics
https://www.kaggle.com/code/rajatraj0502/employee-s-performance-for-hr-analytics
Food Price Prediction
https://github.com/VectorInstitute/foodprice-forecasting
Web Scraping
https://github.com/shreyaswankhede/IMDb-Web-Scraping-and-Sentiment-Analysis
Product Price Scraping and Analysis
https://github.com/CodesdaLu/Web-Scrapping
News Scraping
https://github.com/rohit-yadav/scraping-news-articles
Real Time Stock Price Scraping with Python
https://youtu.be/rONhdonaWUo?si=A3oDEVbLIAP78cCz
Zomato Analysis
https://youtu.be/fFi_TBw27is?si=E0iLd3J06YHfQkRk
IPL Analysis
https://github.com/Yashmenaria1/IPL-Data-Exploration
https://www.youtube.com/watch?v=ur-v0dv0Qtw
https://www.youtube.com/watch?v=ur-v0dv0Qtw
Football Data Analysis
https://youtu.be/yat7soj__4w?si=h5CLIvVFzzKm8IEP
Market Basket Analysis
https://youtu.be/Ne8Sbp2hJIk?si=ThEuvdOnRrpcVjOg
Customer Churn Prediction
https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction
Employeeโs Performance for HR Analytics
https://www.kaggle.com/code/rajatraj0502/employee-s-performance-for-hr-analytics
Food Price Prediction
https://github.com/VectorInstitute/foodprice-forecasting
๐8โค1
If you want to get a job as a machine learning engineer, donโt start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
๐5โค1
Top 10 Programming Languages to learn in 2025 (With Free Resources to learn) :-
1. Python
- learnpython.org
- t.iss.one/pythonfreebootcamp
2. Java
- learnjavaonline.org
- t.iss.one/free4unow_backup/550
3. C#
- learncs.org
- w3schools.com
4. JavaScript
- learnjavascript.online
- t.iss.one/javascript_courses
5. Rust
- rust-lang.org
- exercism.org
6. Go Programming
- go.dev
- learn-golang.org
7. Kotlin
- kotlinlang.org
- w3schools.com/KOTLIN
8. TypeScript
- Typescriptlang.org
- learntypescript.dev
9. SQL
- datasimplifier.com
- t.iss.one/sqlanalyst
10. R Programming
- w3schools.com/r/
- r-coder.com
ENJOY LEARNING ๐๐
1. Python
- learnpython.org
- t.iss.one/pythonfreebootcamp
2. Java
- learnjavaonline.org
- t.iss.one/free4unow_backup/550
3. C#
- learncs.org
- w3schools.com
4. JavaScript
- learnjavascript.online
- t.iss.one/javascript_courses
5. Rust
- rust-lang.org
- exercism.org
6. Go Programming
- go.dev
- learn-golang.org
7. Kotlin
- kotlinlang.org
- w3schools.com/KOTLIN
8. TypeScript
- Typescriptlang.org
- learntypescript.dev
9. SQL
- datasimplifier.com
- t.iss.one/sqlanalyst
10. R Programming
- w3schools.com/r/
- r-coder.com
ENJOY LEARNING ๐๐
๐4โค1
Randomized experiments are the gold standard for measuring impact. Hereโs how to measure impact with randomized trials. ๐
๐. ๐๐๐ฌ๐ข๐ ๐ง ๐๐ฑ๐ฉ๐๐ซ๐ข๐ฆ๐๐ง๐ญ
Planning the structure and methodology of the experiment, including defining the hypothesis, selecting metrics, and conducting a power analysis to determine sample size.
โคท Ensures the experiment is well-structured and statistically sound, minimizing bias and maximizing reliability.
๐. ๐๐ฆ๐ฉ๐ฅ๐๐ฆ๐๐ง๐ญ ๐๐๐ซ๐ข๐๐ง๐ญ๐ฌ
Creating different versions of the intervention by developing and deploying the control (A) and treatment (B) versions.
โคท Allows for a clear comparison between the current state and the proposed change.
๐. ๐๐จ๐ง๐๐ฎ๐๐ญ ๐๐๐ฌ๐ญ
Choosing the right statistical test and calculating test statistics, such as confidence intervals, p-values, and effect sizes.
โคท Ensures the results are statistically valid and interpretable.
๐. ๐๐ง๐๐ฅ๐ฒ๐ณ๐ ๐๐๐ฌ๐ฎ๐ฅ๐ญ๐ฌ
Evaluating the data collected from the experiment, interpreting confidence intervals, p-values, and effect sizes to determine statistical significance and practical impact.
โคท Helps determine whether the observed changes are meaningful and should be implemented.
๐. ๐๐๐๐ข๐ญ๐ข๐จ๐ง๐๐ฅ ๐ ๐๐๐ญ๐จ๐ซ๐ฌ
โคท Network Effects: User interactions affecting experiment outcomes.
โคท P-Hacking: Manipulating data for significant results.
โคท Novelty Effects: Temporary boost from new features.
Hope this helps you ๐
๐. ๐๐๐ฌ๐ข๐ ๐ง ๐๐ฑ๐ฉ๐๐ซ๐ข๐ฆ๐๐ง๐ญ
Planning the structure and methodology of the experiment, including defining the hypothesis, selecting metrics, and conducting a power analysis to determine sample size.
โคท Ensures the experiment is well-structured and statistically sound, minimizing bias and maximizing reliability.
๐. ๐๐ฆ๐ฉ๐ฅ๐๐ฆ๐๐ง๐ญ ๐๐๐ซ๐ข๐๐ง๐ญ๐ฌ
Creating different versions of the intervention by developing and deploying the control (A) and treatment (B) versions.
โคท Allows for a clear comparison between the current state and the proposed change.
๐. ๐๐จ๐ง๐๐ฎ๐๐ญ ๐๐๐ฌ๐ญ
Choosing the right statistical test and calculating test statistics, such as confidence intervals, p-values, and effect sizes.
โคท Ensures the results are statistically valid and interpretable.
๐. ๐๐ง๐๐ฅ๐ฒ๐ณ๐ ๐๐๐ฌ๐ฎ๐ฅ๐ญ๐ฌ
Evaluating the data collected from the experiment, interpreting confidence intervals, p-values, and effect sizes to determine statistical significance and practical impact.
โคท Helps determine whether the observed changes are meaningful and should be implemented.
๐. ๐๐๐๐ข๐ญ๐ข๐จ๐ง๐๐ฅ ๐ ๐๐๐ญ๐จ๐ซ๐ฌ
โคท Network Effects: User interactions affecting experiment outcomes.
โคท P-Hacking: Manipulating data for significant results.
โคท Novelty Effects: Temporary boost from new features.
Hope this helps you ๐
๐1