Data Science Learning Path.md

Learning Plan to Become a Data Scientist in 30 Days

Understanding Data Science Fundamentals (5 days)

• Introduction to Data Science (YouTube: https://www.youtube.com/watch?v=8DvywoWv6fI)
• Statistics for Data Science (YouTube: https://www.youtube.com/watch?v=xxpc-HPKN28)
• Linear Algebra for Data Science (YouTube: https://www.youtube.com/watch?v=ZumgfOei0Ak)
• Probability for Data Science (YouTube: https://www.youtube.com/watch?v=KqokXm8EMm8)
• Machine Learning Fundamentals (YouTube: https://www.youtube.com/watch?v=_Z9TRANg4c0)

Data Collection and Preprocessing (5 days)

• Data Cleaning and Processing with Pandas (GitHub: https://github.com/wesm/pydata-book)
• Web Scraping with Beautiful Soup (YouTube: https://www.youtube.com/watch?v=XQgXKtPSzUI)
• Data Wrangling with Python (GitHub: https://github.com/datawrangling)
• Data Visualization and Exploration (5 days)
• Data Visualization with Matplotlib (GitHub: https://github.com/rougier/matplotlib-tutorial)
• Data Exploration with Pandas (GitHub: https://github.com/jakevdp/PythonDataScienceHandbook)

Machine Learning Algorithms (10 days)

• Supervised Learning (YouTube: https://www.youtube.com/watch?v=UzxYlbK2c7E)
• Unsupervised Learning (YouTube: https://www.youtube.com/watch?v=J5bXOOmkopc)
• Deep Learning (YouTube: https://www.youtube.com/watch?v=trWrEWfhTVg)
• Ensemble Learning (YouTube: https://www.youtube.com/watch?v=Q5W5k5yZJmY)
• Recommendation Systems (GitHub: https://github.com/aymericdamien/TopDeepLearning)

Big Data Tools and Technologies (5 days)

• Hadoop and Spark (YouTube: https://www.youtube.com/watch?v=fOp-txL90xc)
• NoSQL Databases (YouTube: https://www.youtube.com/watch?v=qI_g07C_Q5I)
• Distributed Systems (YouTube: https://www.youtube.com/watch?v=TlB_eWDSMt4)

Practical Tasks:

Explore and Visualize the Data using Matplotlib and Pandas:

• "Data Analysis with Pandas and Matplotlib" (Kaggle tutorial: https://www.kaggle.com/learn/data-visualization)
• "Pandas Data Visualization Tutorial" (Medium article: https://towardsdatascience.com/pandas-data-visualization-8df1a246d58c)

Implement Supervised and Unsupervised Learning Algorithms such as Linear Regression, Random Forest, and K-Means on the Dataset:

• "Scikit-Learn Tutorial" (Scikit-Learn website: https://scikit-learn.org/stable/tutorial/index.html)
• "Introduction to Machine Learning with Python" (book by Andreas Müller and Sarah Guido)
• "Unsupervised Learning with K-Means Clustering" (Kaggle tutorial: https://www.kaggle.com/vjchoudhary7/kmeans-clustering-in-python)

Use Spark to Process and Analyze Big Data:

• "PySpark Tutorial" (Medium article: https://towardsdatascience.com/apache-spark-and-pyspark-tutorial-for-beginners-5c5f57e17b24)
• "Apache Spark Machine Learning Tutorial" (Spark website: https://spark.apache.org/docs/latest/ml-guide.html)

Build a Recommendation System using Deep Learning:

• "Building a Recommendation System with Keras" (Medium article: https://towardsdatascience.com/building-a-recommendation-system-using-neural-network-embeddings-1ef92e5c80c9)
• "Collaborative Filtering with PyTorch" (PyTorch tutorial: https://towardsdatascience.com/collaborative-filtering-with-pytorch-7eb45a3c7269)