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.
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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.
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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.
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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.
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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