End-to-End Python Course

Starting a Python programming journey for Data Science as a beginner involves building a solid foundation in both Python and data science concepts.
Familiarize Yourself with Python Libraries for Data Science
NumPy: Learn NumPy for numerical operations and working with arrays. It’s the foundation for many other libraries.
Pandas: Master Pandas for data manipulation and analysis. Focus on DataFrames, Series, and operations like filtering, grouping, and merging data.
Matplotlib/Seaborn: Start with Matplotlib for basic data visualization, then move to Seaborn for more advanced and aesthetically pleasing plots.
Develop a Strong Understanding of Data Science Concepts
Statistics: Learn the basics of descriptive and inferential statistics, including concepts like mean, median, standard deviation, correlation, and hypothesis testing.
Probability: Understand probability theory, distributions, and Bayes’ theorem, which are crucial for data science.