🔗 "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

🤩 https://t.me/codeprogrammer 🥰

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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://t.me/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

✅ https://t.me/codeprogrammer ✅

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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://t.me/codeprogrammer ✅

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🐻Plotting with Pandas series 🐼

✅ https://t.me/codeprogrammer ✅

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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/

✈️ http://t.me/codeprogrammer ✅

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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/

✈️ http://t.me/codeprogrammer ✅

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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://t.me/codeprogrammer ✅

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This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer

♾ 32 essential datasets for data science projects

🔴 The Data Science Dojo website has created a collection of 32 datasets with different levels of difficulty to practice and improve your skills as a data scientist.

✅ This collection includes a diverse range of topics that are classified according to the level of difficulty so that it is suitable for everyone and challenges your theoretical knowledge and practical experience in different parts of data science, such as data cleaning, data visualization, machine learning, etc.


🟢 Basic Level

┌ 🏷 Datasets
├ 🟡 Abalone
├ 🟡 User Knowledge Modeling
├ 🟡 Real Estate Valuation
├ 🟡 Wireless Indoor Localization
├ 🟡Car Evaluation
├ 🟡 Fertility
└ 🟡 Qualitative Bankruptcy


🟡 Intermediate Level

┌ 🏷 Datasets
├ 🔸 Auto MPG
├ 🔸 Heart Disease
├ 🔸 Daily Demand Forecasting Orders
├ 🔸 Blood Transfusion Service Center
├ 🔸 Beijing PM2.5
├ 🔸 Echocardiogram
├ 🔸 Concrete Compressive Strength
├ 🔸 Liver Disorders
├ 🔸Dow Jones Index
├ 🔸Energy Efficiency
├ 🔸 Glass Identification
├ 🔸 Hepatitis
├ 🔸 Wholesale Customers
└ 🔸Travel Reviews
├ 🔸 Istanbul Stock Exchange
├ 🔸 Bike Sharing
├ 🔸 Occupancy Detection
├ 🔸 Census Income
└ 🔸 Coronavirus


🔴 Advanced Level

┌ 🏷 Generative AI for Beginners
├ 🔸 Autism Screening Adult
├ 🔸 Default of Credit Card Clients
├ 🔸 Banknote Authentication
├ 🔸 Individual Household Electric Power
├ 🔸 Online News Popularity
└ 🔸 Amazon Product Reviews

✈️ http://t.me/codeprogrammer ✅

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🙈 8 free NVIDIA artificial intelligence courses

🇸🇾 The NVIDIA site has released a series of artificial intelligence courses that are extremely practical and free for students who want to learn more about this field and are still at the beginning of the journey. 💯


1️⃣ Generative AI Explained course
📝 Definition of productive intelligence and how it works
🗂 Link: LINK


2️⃣ Building A Brain course
📝 How neural networks use data for learning.
🗂 Link: LINK


3️⃣ Augment your LLM Using RAG course
📝 Familiarity with RAG recovery process.
🗂 Link: LINK


4️⃣ Intro to AI in the Data Center course
📝 What is artificial intelligence and its applications.
🗂 Link: LINK


5️⃣ Accelerate Data Science Workflows course
📝 Understand the benefits of integrated workflow between CPUs and GPUs for data science tasks.
🗂 Link: LINK


6️⃣ Recommender Systems course
📝 Strategies used to excel in a data science competition focused on building a recommendation system.
🗂 Link: LINK


7️⃣ Introduction to Networking course
📝 What is the network and why it is needed.
🗂 Link: LINK


8️⃣ Large-Scale Image Classification course
An introduction to large-scale image classification.
🗂 Link: LINK

✈️ http://t.me/codeprogrammer ✅

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📚 10 of the best free books in the field of data science
📑 that you should include in your learning program!

1️⃣ Python Data Science Handbook
📝 Using unique Python libraries with a focus on data science.
📕 Link: PDF


2️⃣ Hands-On Machine Learning book
📝 This book explains the concepts of machine learning with practical examples and Python code and is suitable for novice programmers.
📕 Link: PDF


3️⃣ Deep Learning book
📝 The best book for machine learning and deep learning written by 3 active and top researchers in these fields.
📕 Link: PDF


4️⃣ R for Data Science book
📝 This book reflects the best ways to use R in data science.
📕 Link: PDF


5️⃣ Data Science from Scratch book
📝 This book starts from the simplest possible level and provides you with all the necessary tools and skills to become a great data scientist.
📕 Link: PDF


6️⃣ Machine Learning Yearning book
📝 Error detection in machine learning projects.
📕 Link: PDF


7️⃣ Bayesian Methods for Hackers book
📝 Practical applications of Bayesian inference and probabilistic programming.
📕 Link: PDF


8️⃣ The Elements of Statistical Learning book
📝 Investigating the mathematics of ML algorithms and statistical learning methods.
📕 Link: PDF


9️⃣ DATA SMART book
📝 Implementing complex data science problems using Excel and the tips and tricks of this process.
📕 Link: PDF


🔟 Intro to Statistical Learning with Python book
📝 Examples and practical applications of Python language in data science projects.
📕 Link: PDF

✈️ http://t.me/codeprogrammer ✅

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✅ +70 machine learning projects with Python with data set

🔴 With a simple search on the Internet, you can understand that many datasets are available for use in machine learning projects. I tried to collect more than 70 machine learning projects with Python along with datasets for machine learning beginners and experts with real world implementation.

🔻 You can access all these projects through the following link: 👇

┌ 🏷 70+ ML Datasets & Project Ideas
└ 📚 ML Projects + Datasets


✈️ http://t.me/codeprogrammer ✅

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🟢 5 free courses of productive artificial intelligence
📣 Comprehensive introduction to Generative AI and LLM
🔄 2024 update

⭐️ Google has updated its free artificial intelligence course, and now this course includes 5 sections , and it is extremely useful for students who want to learn more about the field of artificial intelligence and are still in the beginning. 💯

To access these free courses, you can use the following link:


┌ 🏷 Introduction to GenAI
└ 🌐 Website

✈️ http://t.me/codeprogrammer ✅

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🔥 Large selection of interview questions on DS, AI, ML, DL, NLP, computer vision.

A selection of interview questions will help you in interviews in the fields of data science, artificial intelligence, machine learning, deep learning, natural language processing, computer vision.

🟢 100 machine learning interview questions in 2024

🟢 50 computer vision interview questions in 2024

🟢 50 Deep Learning Interview Questions in 2024

🟢 50 interview questions on NLP (natural language processing) in 2024

🟢 100 Data Science interview questions

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