DPS(Data Processing and Data Statistics)

This repository contains practical exercises and mini-projects related to data preprocessing and data statistics.

📂 Structure

📂Data-Processing/

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​ 📂Data Practice/
​ Exercises focused on data preprocessing techniques.

• Titanic_Data Cleaning: Data cleaning with Pandas, handling missing values, outliers, duplicates, text and datetime processing.
• Salary_Data Transformation: Data transformation with Pandas, merging datasets, summarizing, handling missing values and outliers, aggregation, pivot tables, log transformation, one-hot encoding, scaling, PCA.
• Speed Dating_Feature Engineering: Feature engineering with Pandas using the Speed Dating dataset to derive additional insights from the provided data.

​ 📁 Data Project/
​ Projects that apply data preprocessing techniques to real-world datasets.

• Segmentation_Project: Analysis based on RFM (Recency, Frequency, Monetary) segmentation.
• TaxiFare_Project: Analysis based on data cleaning techniques.
• Used Car Prices_Project: Analysis based on data transformation techniques.
• Credit Transaction Anomaly Detection_Project: Analysis based on feature engineering techniques.

📂Data-Statistics/

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• AARRR & Statistical Analysis: Analysis based on the AARRR framework
• Basic Statistics: Understanding the fundamentals of statistics.

✨ Skills Covered

🛠️ Tech Stack
Pandas, Numpy, SQL, BigQuery, Jupyter Notebook, google colab

📊 Techniques

• Missing value handling, Outlier detection, Duplicate removal, One-hot encoding, Scaling, Principal Component Analysis (PCA), feature engineering
• Acquisition, Activation, Retention, Revenue(ARPU), Revenue(CLV), Distribution Visualization, One-Sample t-Test, Independent Sample t-Test, Paired Sample t-Test, Sampling, Confidence Interval, Hypothesis Testing, A/B Test