Numerical Data Cleaning in Excel

For the next step of our work, we will now enter into analyzing the SDG progress dataset for more insights, and the reading and summary you have completed should build a solid foundation for your understanding on the Asia-Pacific SDG progress. The dataset I want to introduce consists of various indicators related to the United Nations Sustainable Development Goals (SDGs). Each row represents different SDG indicators over the years from 2015 to 2023 (the dataset also includes multiple unnamed columns with numerical values that represent measurements or observations corresponding to the indicators for each year). For this dataset, we need to conduct some basic analysis and try to extract some insights. a. Dataset Cleanup and Preparation Rename the columns to meaningful names based on the content, such as the specific year or indicator. Handle missing values by filling them with appropriate statistics (mean, median) or removing the rows/columns if necessary. Descriptive Statistics Calculate basic statistics for each numerical column, such as mean, median, standard deviation, and range. This will provide a general understanding of the distribution and central tendencies of the data. b. Time Series Analysis Plot the trends of a selected SDG indicator over the years. This will help identify any patterns or significant changes over time. c. Comparative Analysis Compare the values of a specific indicator across different years or different indicators within the same year to draw comparative insights. d. Correlation Analysis Determine if there is any correlation between different SDG indicators. This could be done using correlation coefficients and visualized through a heatmap. e. Data Visualization Create visualizations such as line graphs, bar charts, and heatmaps to better understand the trends and relationships in the data. f. Reporting Prepare a summary report of the findings with graphs and explanations of the trends and anomalies observed in the data. g. Interpretation and Recommendations Provide interpretations based on the analytical findings. Suggest potential areas of focus or action for policy-making based on the trends of SDG indicators. Tasks involved will include: - Identifying and addressing missing values - Cleaning and subsequently formatting the numerical data To be successful in this job, you should have: - Demonstrated proficiency in Excel - A keen eye for detail - Experience in data cleaning, especially detecting and filling in missing numerical values. Your mission will be to create a clean, formatted, and comprehensive numerical data set.