Data Cleaning and Analysis

Data Cleaning (Data Preprocessing):

• Error Detection & Correction: Identify and fix data entry errors, inconsistencies, and duplicates.

• Missing Data Handling: Implement strategies to manage or impute missing values (e.g., mean imputation, interpolation).

• Standardization: Harmonize formats (e.g., dates, currency, measurements) to ensure consistency across datasets.

• Outlier Detection: Spot and address outliers using statistical methods or domain-specific rules.

• Data Validation: Ensure data accuracy, integrity, and adherence to business rules.

2. Data Analysis:

• Descriptive Statistics: Summarize data using mean, median, mode, standard deviation, etc., to understand key trends.

• Exploratory Data Analysis (EDA): Visualize distributions, correlations, and patterns using tools like matplotlib, seaborn, or Power BI/Tableau.

• Trend Analysis: Discover historical patterns and predict future behavior through time-series analysis or regression modeling.

• Segmentation & Clustering: Group data points based on similarity using clustering algorithms (e.g., k-means).

• Custom Reporting: Deliver actionable insights through interactive dashboards or custom reports tailored to stakeholder needs.