7 Best GitHub Repositories to Break into Data Analytics and Data Science

If you're diving into data science or data analytics, these repositories will give you the edge you need. Check them out:

1️⃣ 100-Days-Of-ML-Code
🔗 https://github.com/Avik-Jain/100-Days-Of-ML-Code
⭐️ Stars: ~42k

2️⃣ awesome-datascience
🔗 https://github.com/academic/awesome-datascience
⭐️ Stars: ~22.7k

3️⃣ Data-Science-For-Beginners
🔗 https://github.com/microsoft/Data-Science-For-Beginners
⭐️ Stars: ~14.5k

4️⃣ data-science-interviews
🔗 https://github.com/alexeygrigorev/data-science-interviews
⭐️ Stars: ~5.8k

5️⃣ Coding and ML System Design
🔗 https://github.com/weeeBox/coding-and-ml-system-design
⭐️ Stars: ~3.5k

6️⃣ Machine Learning Interviews from MAANG
🔗 https://github.com/arunkumarpillai/Machine-Learning-Interviews
⭐️ Stars: ~8.1k

7️⃣ data-science-ipython-notebooks
🔗 https://github.com/donnemartin/data-science-ipython-notebooks
⭐️ Stars: ~27.2k

Free GitHub Resources: https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43

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Data Science Roadmap ✅

Top 10 machine learning algorithms

Roadmap for Learning Machine Learning (ML)

Here’s a concise and point-wise roadmap for learning ML:

1. Prerequisites
- Learn programming basics (e.g., Python).
- Understand mathematics:
1 - Linear Algebra (vectors, matrices).
2 - Probability and Statistics (distributions, Bayes’ theorem).
3 - Calculus (derivatives, gradients).
4 - Familiarize yourself with data structures and algorithms.

2. Basics of Machine Learning
-Understand ML concepts:
Supervised, unsupervised, and reinforcement learning.
Training, validation, and testing datasets.
- Learn how to preprocess and clean data.
- Get familiar with Python libraries:
NumPy, Pandas, Matplotlib, and Seaborn.

3. Supervised Learning
- Study regression techniques:
Linear and Logistic Regression.
- Explore classification algorithms:
Decision Trees, Support Vector Machines (SVM), k-NN.
- Learn model evaluation metrics:
Accuracy, Precision, Recall, F1 Score, ROC-AUC.

4. Unsupervised Learning
- Learn clustering techniques:
k-Means, DBSCAN, Hierarchical Clustering.
- Understand Dimensionality Reduction:
PCA, t-SNE.

5. Advanced Concepts
- Explore ensemble methods:
Random Forest, Gradient Boosting, XGBoost, LightGBM.
- Learn hyperparameter tuning techniques:
Grid Search, Random Search.

6. Deep Learning (Optional for Advanced ML)
- Learn neural networks basics:
Forward and Backpropagation.
- Study Deep Learning libraries:
TensorFlow, PyTorch, Keras.
Explore CNNs, RNNs, and Transformers.

7. Hands-on Practice
- Work on small projects like:
1 - Predicting house prices.
2 - Sentiment analysis on tweets.
3 - Image classification.
4 - Explore Kaggle competitions and datasets.

8. Deployment
- Learn how to deploy ML models:
Use Flask, FastAPI, or Django.
- Explore cloud platforms: AWS, Azure, Google Cloud.

9. Keep Learning
- Stay updated with new techniques:
Follow blogs, papers, and conferences (e.g., NeurIPS, ICML).
- Dive into specialized fields:
NLP, Computer Vision, Reinforcement Learning.

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