In this repository, you will find information about my data analysis work, including key features, skills, technologies used, and how to get in touch with me.
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Data Cleaning: I have experience in cleaning and preprocessing large datasets, ensuring data integrity and removing any inconsistencies or errors that may affect the analysis.
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Exploratory Data Analysis (EDA): I excel at performing EDA to gain insights into the data. I utilize various statistical techniques, visualizations, and data mining methods to uncover patterns, correlations, and trends.
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Statistical Analysis: I have a strong background in statistics and can apply a wide range of statistical techniques to analyze data. From hypothesis testing to regression analysis, I can provide robust statistical insights.
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Data Visualization: I believe in the power of effective data visualization. I can create compelling visualizations using tools like Python's matplotlib and seaborn libraries, Tableau, or Power BI to present data-driven stories in an intuitive and engaging manner.
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Feature Selection: I employ various statistical techniques, such as correlation analysis, information gain, and mutual information, to identify features with high predictive power.
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Feature Engineering: I apply various techniques, such as logarithmic transformations, scaling, and one-hot encoding, to ensure that features are in the appropriate format and have the desired statistical properties.
- Data cleaning and preprocessing
- Exploratory data analysis
- Statistical analysis
- Data visualization
- Machine learning
- SQL and database management
- Programming languages: Python (NumPy, Pandas, matplotlib, scikit-learn), SQL
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Python: I utilize Python's programing language for data analysis, including libraries such as NumPy, Pandas, matplotlib, and scikit-learn.
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SQL: I have experience working with SQL databases to extract, transform, and analyze data.
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Jupyter Notebook: I work extensively with Jupyter Notebook to document my analysis process, share code, and showcase results.
I am always eager to receive feedback and collaborate on interesting data analysis projects. If you have any questions or would like to get in touch, please feel free to reach out to me:
Thank you for visiting my portfolio, and I look forward to connecting with you!