People often ask about the best programming languages to learn.

Ever notice why interviewers say you can code in any language of you choice in the general interviews?

Because programming language doesn’t matter.

What matters most is your ability to break down a problem into smaller parts, build logic in simple English, and solve it.

Learn logic building first! You can learn the syntax of various programming languages online in 5 minutes.

Learn Python in One day book pdf

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Javascript interview Question book pdf

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VS code course

The day you plant the seed is not the day you eat the fruit.

#motivation @javascript_resources @python_assets

Reason(with examples):

all(): Returns True if all elements of the iterable are true (or if the iterable is empty). If any element is false, it returns False.

any(): Returns True if at least one element of the iterable is true. If the iterable is empty or all the false, it returns False.

Learn Python in a structured way !!!

Here's a FREE ROADMAP & RESOURCES to learn them 🚀🚀🚀

Websites to Learn Data Analytics for free.

Learn Data Analytics for free with these resources:

📶1. Excel: excel-practice-online.com

📶2. Tableau: tableau.com/learn/starter-…

📶3. PowerBi: powerbi.microsoft.com/en-us/learning/

📶4. SQL: w3schools.com

📶5. Python: freecodecamp.org/

Complete Data Science Road Map🔥

with resources👇

1.Math and Statistics:
• Linear Algebra
• Calculus
• Probability
• Statistics

2.Languages:
• Python (
• NumPy,
• Pandas,
• Matplotlib,
• Seaborn )
• R

3. Data skills:
• Data Cleaning
• Exploratory Data Analysis
• Feature Engineering

4. Data Visualization:
• Matplotlib
• Seaborn
• Plotly
• Tableau

5.Machine Learning Basics:
• Supervised Learning
• Unsupervised Learning
• Regression
• Classification
• Clustering

6. ML Libraries:
• Scikit-Learn
• TensorFlow
• Keras
• PyTorch

7.Model Evaluation and Validation:
• Cross-Validation
• Hyperparameter Tuning
• Evaluation Metrics

8.Big Data Technologies:
• Apache Hadoop
• Apache Spark

9.Database:
• SQL Basics
• MySQL
• PostgreSQL

10.Deep Learning:
• Neural Networks
• CNN
• RNN
• Transfer Learning

11.Natural Language Processing (NLP):
• Tokenization
• Named Entity Recognition (NER)
• Sentiment Analysis

12.Time Series Analysis:
• Time Series Components
• Seasonal Decomposition
• Forecasting Methods

13.Model Deployment:
• Flask (for Python)
• Django (for Python)
• Docker

14.Version Control:
• Git
• GitHub

15. Cloud Platforms:
• AWS
• Azure
• GCP

16. Data Ethics and Privacy:
• Ethical Considerations
• Privacy Protection

17.Communication and Reporting:
• Data Storytelling
• Reporting Tools e.g.
- Jupyter Notebooks
- R Markdown

18.Continuous Learning:
• Stay Updated with Industry Trends
• Participate in Online Communities
• Join online Conferences

------------------- END --------------------

Some good resources to learn Data Science

Books:
• Python for Data Analysis
- by Wes McKinney
• Hands-On Machine Learning
- by Aurélien Géron
• The Art of Data Science
- by Roger D. Peng and Elizabeth M.
• Data Science from Scratch
-by Joel Grus

Blogs:
• Towards Data Science
• KDnuggets
• R-bloggers
• Flowingdata
• Analytics Vidhya

YouTube Channel
❯ Python ➟ Corey Schafer
❯ SQL ➟ Joey Blue
❯ Excel ➟ ExcelIsFun
❯ PowerBI ➟ Guy in a Cube
❯ Tableau ➟ Tableau Tim
❯ Mathematics ➟ 3Blue1Brown
❯ Statistics ➟ statquest
❯ Data Analyst ➟ AlexTheAnalyst
❯ ML, DL ➟ sentdex

Podcasts:
• Data Science at Home
• Talking Machines
• O'Reilly Data Science Podcast
• Linear Digressions
• DataFramed

Community and Forums:
Stack Overflow
Reddit - r/datascience:

Documentation and Guides:
1.Scikit-Learn Documentation:
Official documentation for the Scikit-Learn library.
2.Pandas Documentation: Official documentation for the Pandas library.

Book 📖 📕 📘

Basic Math for Game Development with Unity 3D

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