Python Basics: Official Python Documentation: Python.org Codecademy Python Course: Codecademy Python Libraries for Data Science: NumPy: numpy.org Pandas: pandas.pydata.org Matplotlib: matplotlib.org Data Visualization: Seaborn: seaborn.pydata.org Plotly: plotly.com Data Visualization with Matplotlib Tutorial: matplotlib.org/3.3.4/tutorials/index.html Statistical Analysis: SciPy: scipy.org Statsmodels: statsmodels.org Statistics for Data Science with Python: realpython.com Machine Learning: Scikit-learn: scikit-learn.org TensorFlow: tensorflow.org PyTorch: pytorch.org Advanced Topics in Data Science: Natural Language Processing with NLTK: nltk.org Time Series Analysis with Python: statsmodels.org/dev/examples/index.html#time-series-analysis Deep Learning with TensorFlow: tensorflow.org/tutorials Deep Learning with PyTorch: pytorch.org/tutorials Data Science Communities and Platforms: Kaggle (Data Science Competitions and Datasets): kaggle.com Data Science Stack Exchange: datascience.sta...
Week 1: Introduction to Python and Data Manipulation Resources: Python Crash Course by Eric Matthes: book Codecademy Python Course: codecademy.com Hands-on Project: Perform basic Python exercises to get familiar with the language and practice data manipulation using Python lists. Week 2: Data Analysis with Pandas Resources: Pandas User Guide: pandas.pydata.org "Python for Data Analysis" by Wes McKinney: book Hands-on Project: Analyze a real-world dataset using Pandas. Practice loading data, performing data cleaning, filtering, and basic exploratory data analysis (EDA). Week 3: Data Visualization with Matplotlib and Seaborn Resources: Matplotlib Documentation: matplotlib.org Seaborn Documentation: seaborn.pydata.org Hands-on Project: Create various visualizations (line plots, bar charts, scatter plots, etc.) using Matplotlib and Seaborn with a dataset of your choice. Week 4: Statistical Analysis with SciPy and Statsmodels Resources: SciPy Documentation: scipy.org Statsmodels D...
Understanding the Basics of Python : Start by learning the fundamentals of Python programming language. Familiarize yourself with variables, data types, loops, conditional statements, functions, and basic syntax. You can find numerous online tutorials, courses, or books for Python beginners. Python Libraries for Data Science : Python has several powerful libraries specifically designed for data science. Begin with three essential libraries: NumPy, Pandas, and Matplotlib. NumPy provides support for numerical operations, Pandas helps with data manipulation and analysis, and Matplotlib allows for data visualization. Exploratory Data Analysis (EDA) : Learn how to explore and analyze data using Pandas. Understand how to load datasets, inspect data frames, clean data, handle missing values, and perform basic statistical calculations. Gain proficiency in data manipulation techniques like filtering, sorting, grouping, and merging. Data Visualization : Dive into data visualization using Matplot...
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