🚀 Complete Roadmap to Become a Data Scientist in 5 Months
📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.
🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.
🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.
🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.
🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.
📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.
🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.
💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.
🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.
👨💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.
🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.
🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.
🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.
🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!
🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥
📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.
🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.
🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.
🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.
🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.
📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.
🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.
💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.
🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.
👨💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.
🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.
🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.
🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.
🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!
🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥
❤11
Python Detailed Roadmap 🚀
📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)
📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules
📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON
📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation
📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions
📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators
📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)
📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)
📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI
📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)
📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub
📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews
Like for more ❤️💪
📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)
📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules
📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON
📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation
📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions
📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators
📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)
📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)
📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI
📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)
📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub
📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews
Like for more ❤️💪
❤5
Steps to become a data analyst
Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - https://t.iss.one/learndataanalysis
Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:
SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst
Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst
Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst
Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst
Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).
Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.
Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.
Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.
Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.
Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss
Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.
Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.
Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.
Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.
ENJOY LEARNING 👍👍
Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - https://t.iss.one/learndataanalysis
Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:
SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst
Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst
Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst
Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst
Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).
Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.
Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.
Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.
Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.
Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss
Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.
Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.
Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.
Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.
ENJOY LEARNING 👍👍
❤6👏1
HuggingFace released a ready-made hardcore guide how to train and host an LLM from scratch.
Content with 200+ pages, 7 big chapters, read + lots of diagrams and examples with Simple English:
Link: https://huggingface.co/spaces/HuggingFaceTB/smol-training-playbook
Content with 200+ pages, 7 big chapters, read + lots of diagrams and examples with Simple English:
– Architectures, their features, and hyperparameter optimization
– Working with data
– Pretraining and the pitfalls involved
– Post-training: all modern approaches and how to apply them
– Infrastructure, how to build and optimize it properly
Link: https://huggingface.co/spaces/HuggingFaceTB/smol-training-playbook
❤2
✅ Useful Resources to Learn Machine Learning in 2025 🤖📘
1. YouTube Channels
• StatQuest – Simple, visual ML explanations
• Krish Naik – ML projects and interviews
• Simplilearn – Concepts + hands-on demos
• freeCodeCamp – Full ML crash courses
2. Free Courses
• Andrew Ng’s ML – Coursera (audit for free)
• Google’s ML Crash Course – Interactive + videos
• Kaggle Learn – Short, hands-on ML tutorials
• Fast.ai – Practical deep learning for coders
3. Practice Platforms
• Kaggle – Real datasets, notebooks, and competitions
• Google Colab – Run Python ML code in browser
• DrivenData – ML competitions with impact
4. Projects to Try
• House price predictor
• Stock trend classifier
• Sentiment analysis on tweets
• MNIST handwritten digit recognition
• Recommendation system
5. Key Libraries
• scikit-learn – Core ML algorithms
• pandas – Data manipulation
• matplotlib/seaborn – Visualization
• TensorFlow / PyTorch – Deep learning
• XGBoost – Advanced boosting models
6. Must-Know Concepts
• Supervised vs Unsupervised learning
• Overfitting & underfitting
• Model evaluation: Accuracy, F1, ROC
• Cross-validation
• Feature engineering
7. Books
• “Hands-On ML with Scikit-Learn & TensorFlow” – Aurélien Géron
• “Python ML” – Sebastian Raschka
💡 Build a portfolio. Learn by doing. Share projects on GitHub.
💬 Tap ❤️ for more!
1. YouTube Channels
• StatQuest – Simple, visual ML explanations
• Krish Naik – ML projects and interviews
• Simplilearn – Concepts + hands-on demos
• freeCodeCamp – Full ML crash courses
2. Free Courses
• Andrew Ng’s ML – Coursera (audit for free)
• Google’s ML Crash Course – Interactive + videos
• Kaggle Learn – Short, hands-on ML tutorials
• Fast.ai – Practical deep learning for coders
3. Practice Platforms
• Kaggle – Real datasets, notebooks, and competitions
• Google Colab – Run Python ML code in browser
• DrivenData – ML competitions with impact
4. Projects to Try
• House price predictor
• Stock trend classifier
• Sentiment analysis on tweets
• MNIST handwritten digit recognition
• Recommendation system
5. Key Libraries
• scikit-learn – Core ML algorithms
• pandas – Data manipulation
• matplotlib/seaborn – Visualization
• TensorFlow / PyTorch – Deep learning
• XGBoost – Advanced boosting models
6. Must-Know Concepts
• Supervised vs Unsupervised learning
• Overfitting & underfitting
• Model evaluation: Accuracy, F1, ROC
• Cross-validation
• Feature engineering
7. Books
• “Hands-On ML with Scikit-Learn & TensorFlow” – Aurélien Géron
• “Python ML” – Sebastian Raschka
💡 Build a portfolio. Learn by doing. Share projects on GitHub.
💬 Tap ❤️ for more!
❤8
Top 10 important data science concepts
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
❤1👍1
🐍 Python Roadmap
1️⃣ Basics: 📝📜 Syntax, Variables, Data Types
2️⃣ Control Flow: 🔄🤖 If-Else, Loops, Functions
3️⃣ Data Structures: 🗂️🔢 Lists, Tuples, Dictionaries, Sets
4️⃣ OOP in Python: 📦🎭 Classes, Inheritance, Decorators
5️⃣ File Handling: 📄📂 Read/Write, JSON, CSV
6️⃣ Modules & Libraries: 📦🚀 NumPy, Pandas, Matplotlib
7️⃣ Web Development: 🌍🔧 Flask, Django, FastAPI
8️⃣ Automation & Scripting: 🤖🛠️ Web Scraping, Selenium, Bash Scripting
9️⃣ Machine Learning: 🧠📈 TensorFlow, Scikit-learn, PyTorch
🔟 Projects & Practice: 📂🎯 Create apps, scripts, and contribute to open source
1️⃣ Basics: 📝📜 Syntax, Variables, Data Types
2️⃣ Control Flow: 🔄🤖 If-Else, Loops, Functions
3️⃣ Data Structures: 🗂️🔢 Lists, Tuples, Dictionaries, Sets
4️⃣ OOP in Python: 📦🎭 Classes, Inheritance, Decorators
5️⃣ File Handling: 📄📂 Read/Write, JSON, CSV
6️⃣ Modules & Libraries: 📦🚀 NumPy, Pandas, Matplotlib
7️⃣ Web Development: 🌍🔧 Flask, Django, FastAPI
8️⃣ Automation & Scripting: 🤖🛠️ Web Scraping, Selenium, Bash Scripting
9️⃣ Machine Learning: 🧠📈 TensorFlow, Scikit-learn, PyTorch
🔟 Projects & Practice: 📂🎯 Create apps, scripts, and contribute to open source
❤4
Free Python Courses
Introduction to Python 3 (basics) - Learning to Program with Python 3
🎬 15 lessons
⏰ 2 hours of video + code examples and readings
📝 blogpost for each lesson
🔗 Link to course
Introduction To Python Programming
Rating ⭐️: 4.4 out of 5
Students 👨🏫: 824,949 students
Duration ⏰: 1hr 39min of on-demand video
Created by: Avinash Jain, The Codex
🔗 Course link
Intermediate Python Programming introduction
🎬 28 lessons
⏰ 4.5 hours of video + code examples and readings
⏰ Free Online Course
🏃♂️ Self paced
🔗 Link to course
Sockets Tutorial with Python 3 part 1 - sending and receiving data
🎬 5 lessons
⏰ 100 minutes of video + code examples and readings
⏰ Free Online Course
🏃♂️ Self paced
🔗 Link to course
Machine Learning with Python: Zero to GBMs
🎬 Watch hands-on coding-focused video tutorials
🧮 Practice coding with cloud Jupyter notebooks
💻 Build an end-to-end real-world course project
📜 Earn a verified certificate of accomplishment
📊 You will solve 2 coding assignments & build a course project where you'll train ML models using a large real-world datasets
🔗 Course Link
Introduction to Computer Science and Programming in Python
The most common starting point for MIT students with little or no programming experience. This half-semester course introduces computational concepts and basic programming.
⏰ Free Online Course
🏃♂️ Self paced
🎬 Lecture videos
🔗 Course link
Python for Everybody (PY4E)
by Charles R. Severance (aka Dr. Chuck)
🎬 17 sections with multiple video lessons
👨🏫 Prof. Dr. Charles R. Severance
✅ Completely free
🔗 Course link
The fundamentals of programming - Python Tutorial
👨🏫 Teacher: Annyce Davis
🎬 39 short video lessons
📊 Level: beginner
⏰ Free Online Course
🏃♂️ Self paced
🔗 Course link
Python course by kaggle
Learn the most important language for data science.
🎬 8 lessons
⏰ 5 hours
⏰ Free Online Course
🏃♂️ Self paced
🔗 Course link
Scientific Computing with Python
Author: Dr. Charles Severance (also known as Dr. Chuck).
🎬 56 lessons
💻 5 scientific projects
📜 Free certification
🔗 Link to course
Python from scratch
by University of Waterloo
🆓 Free Online Course
⏳ 13 modules
🏃♂️ Self paced
🔗 Course Link
Learn Python PyQt
(Python binding of the cross-platform GUI toolkit Qt, used as a Python module)
⏰ Free Online Course
🏃♂️ Self paced
🔗 Course link
Python for Beginners
Programming with Python
By Microsoft
Authors: Susan Ibach, GeekTrainer
🎬 44 episodes
⏰ 180 mins
🔗 Link to course
Python Programming MOOC 2022
🆓 Free Online Course
🧮 Problem Sets
⏳ 12 modules
🏃♂️ Self paced
📶 Assignments with Examples
🔗 Link to course
Free Python course by Datacamp
🆓 Free Online Course
🎬 video lessons
✅ Completely free
interactive code exercises
No registration or download needed:
🔗 Link to course
CS50’s Web Programming with Python by Harvard University
⏰ Free Online Course
🏃♂️ Self paced
🔗 Course link
Python course by Google
⏰ Free Online Course
🏃♂️ Self paced
No registration or download needed.
🔗 Course link
NOC:Programming, Data Structures and Algorithms using Python
⏰ Free Online Course
🏃♂️ Self paced
⌛️ 6 weeks
👨🏫 45 lectures
🔗 Link to course
Additional materials
Books
A list of Python books in English that are free to read online or download
Learn Python the Hard Way
python intro notes
An introduction to Python for absolute beginners
python programming notes
Python Data Science Handbook
Cheat sheets
Python Tutorial -> Condensed Cheatsheet
Python Programming Exercises, 2022., gently explained
python matplotlib
python panda
python basics
python seaborn
Useful Python for data science cheat sheets
python data type cheat sheet
python cheat sheets
GitHub Repositories
Machine Learning University: Accelerated Natural Language Processing Class
Hands on ML notebook series
Machine learning cheat sheet with code
#python
Introduction to Python 3 (basics) - Learning to Program with Python 3
🎬 15 lessons
⏰ 2 hours of video + code examples and readings
📝 blogpost for each lesson
🔗 Link to course
Introduction To Python Programming
Rating ⭐️: 4.4 out of 5
Students 👨🏫: 824,949 students
Duration ⏰: 1hr 39min of on-demand video
Created by: Avinash Jain, The Codex
🔗 Course link
Intermediate Python Programming introduction
🎬 28 lessons
⏰ 4.5 hours of video + code examples and readings
⏰ Free Online Course
🏃♂️ Self paced
🔗 Link to course
Sockets Tutorial with Python 3 part 1 - sending and receiving data
🎬 5 lessons
⏰ 100 minutes of video + code examples and readings
⏰ Free Online Course
🏃♂️ Self paced
🔗 Link to course
Machine Learning with Python: Zero to GBMs
🎬 Watch hands-on coding-focused video tutorials
🧮 Practice coding with cloud Jupyter notebooks
💻 Build an end-to-end real-world course project
📜 Earn a verified certificate of accomplishment
📊 You will solve 2 coding assignments & build a course project where you'll train ML models using a large real-world datasets
🔗 Course Link
Introduction to Computer Science and Programming in Python
The most common starting point for MIT students with little or no programming experience. This half-semester course introduces computational concepts and basic programming.
⏰ Free Online Course
🏃♂️ Self paced
🎬 Lecture videos
🔗 Course link
Python for Everybody (PY4E)
by Charles R. Severance (aka Dr. Chuck)
🎬 17 sections with multiple video lessons
👨🏫 Prof. Dr. Charles R. Severance
✅ Completely free
🔗 Course link
The fundamentals of programming - Python Tutorial
👨🏫 Teacher: Annyce Davis
🎬 39 short video lessons
📊 Level: beginner
⏰ Free Online Course
🏃♂️ Self paced
🔗 Course link
Python course by kaggle
Learn the most important language for data science.
🎬 8 lessons
⏰ 5 hours
⏰ Free Online Course
🏃♂️ Self paced
🔗 Course link
Scientific Computing with Python
Author: Dr. Charles Severance (also known as Dr. Chuck).
🎬 56 lessons
💻 5 scientific projects
📜 Free certification
🔗 Link to course
Python from scratch
by University of Waterloo
🆓 Free Online Course
⏳ 13 modules
🏃♂️ Self paced
🔗 Course Link
Learn Python PyQt
(Python binding of the cross-platform GUI toolkit Qt, used as a Python module)
⏰ Free Online Course
🏃♂️ Self paced
🔗 Course link
Python for Beginners
Programming with Python
By Microsoft
Authors: Susan Ibach, GeekTrainer
🎬 44 episodes
⏰ 180 mins
🔗 Link to course
Python Programming MOOC 2022
🆓 Free Online Course
🧮 Problem Sets
⏳ 12 modules
🏃♂️ Self paced
📶 Assignments with Examples
🔗 Link to course
Free Python course by Datacamp
🆓 Free Online Course
🎬 video lessons
✅ Completely free
interactive code exercises
No registration or download needed:
🔗 Link to course
CS50’s Web Programming with Python by Harvard University
⏰ Free Online Course
🏃♂️ Self paced
🔗 Course link
Python course by Google
⏰ Free Online Course
🏃♂️ Self paced
No registration or download needed.
🔗 Course link
NOC:Programming, Data Structures and Algorithms using Python
⏰ Free Online Course
🏃♂️ Self paced
⌛️ 6 weeks
👨🏫 45 lectures
🔗 Link to course
Additional materials
Books
A list of Python books in English that are free to read online or download
Learn Python the Hard Way
python intro notes
An introduction to Python for absolute beginners
python programming notes
Python Data Science Handbook
Cheat sheets
Python Tutorial -> Condensed Cheatsheet
Python Programming Exercises, 2022., gently explained
python matplotlib
python panda
python basics
python seaborn
Useful Python for data science cheat sheets
python data type cheat sheet
python cheat sheets
GitHub Repositories
Machine Learning University: Accelerated Natural Language Processing Class
Hands on ML notebook series
Machine learning cheat sheet with code
#python
❤2👍1
200$ to 20k$ SOL Challenge!
As promised, i will do another challenge for those who missed the previous one!
Last one we completed in 6 days, let’s do this one even quicker!
Join my free group Before closing 👇
https://t.iss.one/+DAKLP7eUy9Y3ZjY0
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As promised, i will do another challenge for those who missed the previous one!
Last one we completed in 6 days, let’s do this one even quicker!
Join my free group Before closing 👇
https://t.iss.one/+DAKLP7eUy9Y3ZjY0
#ad InsideAds