Master EDA & Data Visualization in Python: Cleaning, Statistical Analysis, Feature Engineering & Interactive Plots.
Sub Category
- Other IT & Software
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Objectives
- Understand the fundamental principles and importance of Exploratory Data Analysis (EDA) in the data science workflow.
- Master data loading, inspection, and manipulation using the Pandas library in Python.
- Effectively identify and handle missing values, outliers, and incorrect data types in various datasets.
- Apply descriptive statistics to summarize data distributions and central tendencies.
- Create a wide range of static, informative visualizations using Matplotlib and Seaborn for univariate and bivariate analysis.
- Develop interactive and dynamic data visualizations using Plotly for enhanced data exploration and presentation.
Pre Requisites
- Basic understanding of Python programming concepts (variables, data types, loops, functions).
- Familiarity with basic data structures in Python (lists, dictionaries).
- No prior experience with data science, machine learning, or advanced statistics is required.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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