🔈 list of top 50 data science cheat sheets

🔘 From the day I started summarizing data science topics on LinkedIn, I decided to summarize each topic in a few pages. I finally came up with a list of 50 cheat sheets from various areas of data science. This list covers pretty much everything a data person might need, from how to plot with Matplotlib to using ChatGPT.

⏺ Python: link

⏺ Pandas library: link

⏺ NumPy library: link

⏺ Matplotlib library: link

⏺ seaborn library: link

⏺ scikit-learn library: link

⏺ TensorFlow library: link

⏺ Keras library: link

⏺ PyTorch framework: link

⏺ SQL language: link

👀 GeoPandas project: link

👀 Git version control system: link

👀 AWS cloud platform: link

✅ Azure cloud platform: link

✅ Google Cloud Platform cloud computing: link

✅ Docker platform: link

✅ Kubernetes platform: link

✅ The Linux Command Line training: link

✅ Jupyter notebook: link

✅️ Data preparation: link

✅️ Data Visualization: Link

✅️ Statistical inference: link

✅️ possibility: link

✅️ Linear Algebra: Link

✅️ Differential calculation: link

✅ Time series: link

✅ Natural language processing: link

✅ Neural network: link

✅ Deep Learning: Link

✅ Machine learning: link

✅ Apache Spark Framework: Link

✅ Apache Hadoop framework: link

✅ Big O Notation tool: link

✅ Regular Expression training: link

✅ Unix / Linux Permissions training: link

✅ Python String Formatting tutorial: link

✅ Flask framework: link

✅ Django framework: link

✅ plotly library: link

✅ PostgreSQL database: link

✅ MySQL database: link

✅ MongoDB database: link

✅ TensorFlow Probability library: link

✅ Chatbot GPT-3: link

✅ Training GPT-3 API Reference: link

✅ SciPy library: link

✅ ChatGPT chatbot: link

✅ Training Colors in Data Viz: link

✅ Geospatial DS in Python training: link

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🔄 The largest data visualization tools with Python
🔥 The most powerful data visualization ecosystem

⭐️ The PyViz ecosystem, with nearly 150 different libraries in 12 categories , is one of the most powerful tools to facilitate learning and using data visualization in Python. This ecosystem includes from the main visualizations to the graphic and location libraries and the creation of the dashboard.

✅ To access these 150 top and unique Python libraries, you can use the following link:👇🏼


┌ 🏷 Data visualization in Python
└ 🚀 PyViz


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🎁 205+ free data science and ML courses
✅ from the Udacity platform

✅ Udacity platform It has a wide range of machine learning and data science courses, some of these courses are free and some are paid.

✅ I collected all the free Udacity courses on machine learning, data science, etc. and put them inside the PDF with an active link. Just click on the link of each course. So easily!👌🏼

🗂 +205 Udacity FREE Courses

🪄 https://xn--r1a.website/codeprogrammer 🖼

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6 of the best cloud notebooks for data science

⏺ Cloud notebooks are analytical tools that can be accessed only through an Internet browser without the need to install special software, and provide the possibility of running codes, analyzing data, and creating reports in an online environment.

🔃 In the following, I have provided you with 6 of the best cloud notebooks for data science projects , each of which has its own applications and capabilities in data analysis, programming, and data science project management.


┌ 🏷 6 Free Cloud Notebooks for DS
├ ✅ Deepnote
├ ✅ Kaggle
├ ✅ Hex
├ ✅ Colab
├ ✅ Naas
└ ✅ Datalore

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🇦🇪 Complete set of data science interview questions
📂 with comprehensive answers

🇩🇿 Have you ever been in a situation where the interviewer asked you a theoretical or technical question in the field of data science and you couldn't answer it? Is it just because you were not fully prepared? It happens to many. For example, I have the weakness of mental locking in front of new questions during technical interviews.

🇪🇬 But to overcome this problem, I started looking at sample data science interview questions and collected a collection of the most complete and best data science interview questions with answers from various sources to help you for all data science related jobs. Be prepared and don't repeat my mistakes during interviews!

🔥 Well, if you agree, let's start this interesting part:

┌ 🏷 Data Science Interviews Resources
└ 📂 GitHub-Repos


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⚡️⚡️⚡️⚡️⚡️⚡️⚡️⚡️

🔗 "Data Analysis for Social Scientists" course

☄️ MIT University's new course "Data Analysis for Social Scientists" which, with an attractive and data-based concept, gives scientists in this field the opportunity to benefit from data analysis tools and answer important questions in the fields of economics. , body politics and culture.

➡️ In these 24 educational videos , you will get to know the basic principles of statistics and probability, and then modern data analysis techniques will be discussed, and you will learn topics such as regression analysis, machine learning, and data visualization.


Everything in this course is free ! To access the educational videos of this course + slides and assignments, you can use the following links: 👇

┌ 🏷 Data Analysis for Social Scientists
├ 🌐 Homepage
└ 🎬 Lecture Videos

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⚡️⚡️⚡️⚡️⚡️⚡️⚡️⚡️

The missing link of a professional data scientist!
✅ "Data Storytelling" skill

☄️ One of the skills that I always advise children to work on strengthening in data science courses is the skill of "data storytelling" !

🎯 As a data scientist, you should be able to present your project after completion, through "data visualization, attractive report writing, and various charts and tables, in the form of an attractive story to the employer and other members of the organization, and this information to convey the truth to them!

🗂️ I have put here a collection of pamphlets, files and reports that can help you a lot to strengthen this skill!

┌ 🏷 Data Storytelling
├ 📚 Storytelling with data
├ 📚 Storytelling with data practice
├ 📚 Guide to becoming a Data Storyteller
├ 📚 The Data Storytelling Handbook
├ 📚 Data Storytelling Report 2021
├ 📚 A Guide to Effective Data Storytelling
├ 📚 8 rules for better data storytelling
└ 📚 Data Storytelling in SDG Reporting

✅ https://xn--r1a.website/codeprogrammer ✅

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🟢 7 valuable resources that I used to prepare for data science interviews!

🟢 One of the most important factors to get data science jobs in the best companies is success in job interviews.

🗂 I have put here 7 valuable resources that helped me a lot while preparing for data science interviews. I hope these resources can help you succeed in data science interviews


1️⃣ machine learning
📕 Link: Machine Learning


2️⃣ Python programming language
📕 Link: Python Programming Language


3️⃣ SQL programming language
📕 Link: SQL Programming Language


4️⃣ R programming language
📕 Link: R Programming Language


5️⃣ Pandas library
📕 Link: Pandas Python Library


6️⃣ NumPy library
📕 Link: NumPy Python Library


7️⃣ Matplotlib library
📕 Link: Matplotlib Python Library

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👨‍💻 My data science training source

👀 I have been taking courses related to data science, such as Python programming, machine learning, deep learning, natural language processing, etc. for several years, and after reviewing online courses, reading various books, educational blogs, etc. .. I decided to put all the content that made me progress during this time in the Github repo that I put the link below.

📂 Also, I have designed the scripts both in the presentation format (such as PowerPoint) and in the course notes format.

┌ 🏷 Teaching Data Science
└ 📂 GitHub-Repos

✅ https://xn--r1a.website/codeprogrammer ✅

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🐻Plotting with Pandas series 🐼

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🚓 Deep Learning Roadmap 2024- Step-by-Step Career Path

Are you looking for a step-by-step Deep Learning Roadmap?… If yes, this article is for you. This article will provide a complete Deep Learning Roadmap from scratch. Along with that, you will also find some best resources to learn Deep Learning concepts.

Now without any further ado, let’s get started 👇

🔗 https://www.mltut.com/deep-learning-roadmap/

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☄️ 46 Best Resources to Learn Data Structures and Algorithms including Online Courses, Tutorials, Books, and YouTube Videos 👇

⛓ https://www.mltut.com/best-resources-to-learn-data-structures-and-algorithms/

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🟢 The source of all Python libraries for data science!

🌹 I could not see this complete package of the main Python libraries for data science and not share it with you.

✅ This wonderful GitHub repository covers all Python libraries, packages and tools that are essential for learning and doing data science projects

🔖 Repository access link:

┌ 🏷 Awesome Python Data Science
└ 🗃 GitHub-Repos

✈️ http://xn--r1a.website/codeprogrammer ✅

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