Data wrangling involves cleaning, transforming, and organizing raw data into a usable format. Techniques include handling missing values, correcting inconsistencies, merging datasets, and reshaping data structures. It ensures data quality and prepares datasets for analysis. Visualization techniques help communicate insights effectively using charts, graphs, and plots. Common tools include bar charts for comparisons, line graphs for trends, scatter plots for correlations, and heatmaps for patterns. Tools like Python (pandas, matplotlib, seaborn), R, and Tableau support these processes. Together, data wrangling and visualization enable better decision-making by making complex data understandable and revealing underlying trends and relationships.
Read more: Data Science course in Delhi
Read more: Data Science course in Delhi