16 Websites for Coders 📍

1. HTML & CSS:- htmlcheatsheet.com

2. JavaScript :- https://lnkd.in/dfSvFuhM

3. Jquery - https://oscarotero.com/jquery/

4. Bootstrap 5:- https://lnkd.in/dNZ6qdBh

5. Tailwind CSS:- https://lnkd.in/d_T5q5Tx

6. React:- https://lnkd.in/de55QGBg

7. Python :- https://lnkd.in/dmZa39rE

8. MongoDB:- https://lnkd.in/dBXXCQ43

9. SQL:- https://lnkd.in/dEFY_jAk

10. Nodejs :- https://lnkd.in/dwry8BKH

11. Expressjs:- https://quickref.me/express

12. Django :- https://lnkd.in/dYWQKZnT

13. PHP- https://quickref.me/php

14. Google Dork:- https://lnkd.in/dKej3-42

15. Linux:- https://lnkd.in/dCgH_qUq

16. Git:- https://lnkd.in/djf9Wc98

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How AI Search Actually Works 👆

Tableau Cheat Sheet ✅

This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting to Data
- Use Connect pane to connect to various data sources (Excel, SQL Server, Text files, etc.).

2. Data Preparation
- Data Interpreter: Clean data automatically using the Data Interpreter.
- Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
- Union Data: Stack data from multiple tables with the same structure.

3. Creating Views
- Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
- Show Me: Use the Show Me panel to select different visualization types.

4. Types of Visualizations
- Bar Chart: Compare values across categories.
- Line Chart: Display trends over time.
- Pie Chart: Show proportions of a whole (use sparingly).
- Map: Visualize geographic data.
- Scatter Plot: Show relationships between two variables.

5. Filters
- Dimension Filters: Filter data based on categorical values.
- Measure Filters: Filter data based on numerical values.
- Context Filters: Set a context for other filters to improve performance.

6. Calculated Fields
- Create calculated fields to derive new data:
- Example: Sales Growth = SUM([Sales]) - SUM([Previous Sales])

7. Parameters
- Use parameters to allow user input and control measures dynamically.

8. Formatting
- Format fonts, colors, borders, and lines using the Format pane for better visual appeal.

9. Dashboards
- Combine multiple sheets into a dashboard using the Dashboard tab.
- Use dashboard actions (filter, highlight, URL) to create interactivity.

10. Story Points
- Create a story to guide users through insights with narrative and visualizations.

11. Publishing & Sharing
- Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.

12. Export Options
- Export to PDF or image for offline use.

13. Keyboard Shortcuts
- Show/Hide Sidebar: Ctrl+Alt+T
- Duplicate Sheet: Ctrl + D
- Undo: Ctrl + Z
- Redo: Ctrl + Y

14. Performance Optimization
- Use extracts instead of live connections for faster performance.
- Optimize calculations and filters to improve dashboard loading times.

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Master Data Science & Machine Learning

1️⃣ Basics of Data Science

🏷 Statistics and Probability
◾️ Statistics & Probability (https://www.khanacademy.org/math/statistics-probability)
🏷 Linear Algebra
◽️ Essence of Linear Algebra
(https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
🏷 Unlimited Resources
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2️⃣ Programming Language
🏷 Python
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🏷 Unlimited Resources
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3️⃣ Data Analysis and Manipulation
🏷 Pandas Library
◾️ Pandas Documentation
(https://pandas.pydata.org/pandas-docs/stable/)🏷 Data Preparation with Pandas
◾️ Data Wrangling with Pandas
(https://realpython.com/pandas-dataframe/)🏷 NumPy Library
◾️ NumPy Documentation
(https://numpy.org/doc/stable/)4️⃣ Data Visualization
🏷 Matplotlib and Seaborn Library
◾️ Matplotlib (https://matplotlib.org/stable/users/index.html) / Seaborn (https://seaborn.pydata.org/)
🏷Tableau Public Platform
◾️ Tableau Public
(https://public.tableau.com/app/discover)5️⃣ Principles of Machine Learning
🏷 scikit-learn Library
◾️ scikit-learn
(https://scikit-learn.org/stable/index.html)
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6️⃣ Learning Algorithms
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🏷 Unlimited Resources
◽️ https://t.me/techpsyche
7️⃣ Deep Learning
🏷 TensorFlow Library
◾️ TensorFlow Tutorial
(https://www.tensorflow.org/tutorials)
🏷 PyTorch Library
◾️ PyTorch Documentation
(https://pytorch.org/docs/stable/index.html)
🏷 Unlimited Resources
◽️ https://t.me/mlresourcestp
8️⃣ Big Data Technologies
🏷 Spark Framework Course
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9️⃣ Advanced Topics
🏷 Natural Language Processing Course in Python
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🏷 Unlimited Resources
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1️⃣ Share Your Projects on Kaggle and GitHub
🏷 Kaggle Platform
◾️ Kaggle (https://www.kaggle.com/)
🏷 GitHub Platform
◾️ GitHub (https://github.com/)

Happy Learning! 🌟

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Best Resources to learn Data Science 👇👇

Python Tutorial (http://pythontutorial.net/)

Data Science Course (http://kaggle.com/learn) by Kaggle

Machine Learning Course (http://developers.google.com/machine-learning/crash-course) by Google

Best Data Science & Machine Learning Resources (https://topmate.io/learning_resources/1406977)

Interview Process for Data Science Role at Amazon (https://t.me/datascienceresourcestp/85)

Python Interview Resources (https://t.me/pythonresourcestp/40)

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Java Summary

🔰List of Decompilers🔰

JVM-Based Languages
• Krakatau (https://github.com/Storyyeller/Krakatau) - the best decompiler I have used. Is able to decompile apps written in Scala and Kotlin into Java code. JD-GUI and Luyten have failed to do it fully.

• JD-GUI (https://github.com/java-decompiler/jd-gui)

• procyon (https://bitbucket.org/mstrobel/procyon/wiki/Java%20Decompiler)

◦ Luyten (https://github.com/deathmarine/Luyten) - one of the best, though a bit slow, hangs on some binaries and not very well maintained.

• JAD (http://varaneckas.com/jad/) - JAD Java Decompiler (closed-source, unmaintained)

• JADX (https://github.com/skylot/jadx) - a decompiler for Android apps. Not related to JAD.

.NET-Based Languages
◦ dotPeek (https://www.jetbrains.com/decompiler/) - a free-of-charge .NET decompiler from JetBrains

◦ ILSpy (https://github.com/icsharpcode/ILSpy/) - an open-source .NET assembly browser and decompiler

◦ dnSpy (https://github.com/0xd4d/dnSpy) - .NET assembly editor, decompiler, and debugger

Native Code
◦ Hopper (https://www.hopperapp.com/) - A OS X and Linux Disassembler/Decompiler for 32/64-bit Windows/Mac/Linux/iOS executables.

◦ cutter (https://github.com/radareorg/cutter) - a decompiler based on radare2.

◦ retdec (https://github.com/avast-tl/retdec)

◦ snowman (https://github.com/yegord/snowman)

◦ Hex-Rays (https://www.hex-rays.com/products/decompiler/)

Python
◦ uncompyle6 (https://github.com/rocky/python-uncompyle6) - decompiler for the over 20 releases and 20 years of CPython.

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ML Crash Course by Google
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IBM Free Courses with Certification
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Important Coding Concepts 👆

Practice these 5 intermediate SQL interview questions today!

1. Write a SQL query for cumulative sum of salary of each employee from Jan to July. (Column name – Emp_id, Month, Salary).

2. Write a SQL query to display year on year growth for each product. (Column name – transaction_id, Product_id, transaction_date, spend). Output will have year, product_id & yoy_growth.

3. Write a SQL query to find the numbers which consecutively occurs 3 times. (Column name – id, numbers)

4. Write a SQL query to find the days when temperature was higher than its previous dates. (Column name – Days, Temp)

5. Write a SQL query to find the nth highest salary from the table emp. (Column name – id, salary)

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Web Developer Project Ideas

🔰What is CTF? 🔰

CTF (Capture The Flag) is a kind of information security competition that challenges contestants to solve a variety of tasks ranging from a scavenger hunt on wikipedia to basic programming exercises, to hacking your way into a server to steal data. In these challenges, the contestant is usually asked to find a specific piece of text that may be hidden on the server or behind a webpage. This goal is called the flag, hence the name! Like many competitions, the skill level for CTFs varies between the events. Some are targeted towards professionals with experience operating on cyber security teams. These typically offer a large cash reward and can be held at a specific physical location.

How to Solve CTF: https://t.me/zerotrusthackers/76

Data Analyst in One Page

AI Tools for Content Research:

1. BuzzSumo
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4. Moz
5. Google Trends
6. Ubersuggest
7. Answer The Public
8. Social Animal
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10. Brand24
11. Mention
12. Feedly
13. Quora
14. Reddit
15. Trendspottr
16. BuzzStream
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