- Understanding Data Analysis Basics: Learn the key concepts of data analysis, including data types, data cleaning, and exploration techniques to prepare datasets for analysis.
- Data Wrangling and Cleaning: Gain proficiency in cleaning and transforming raw data using tools like Pandas to handle missing values, outliers, and data inconsistencies.
- Statistical Analysis and Testing: Apply statistical methods and hypothesis testing to analyze data distributions, trends, and relationships between variables.
- Data Visualization: Learn how to create effective data visualizations using tools like Matplotlib, Seaborn, or Power BI to present data insights clearly and effectively.
- Interpreting Data and Reporting Insights: Develop the ability to interpret analysis results, draw conclusions, and present actionable insights to stakeholders through clear and impactful reports.
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