Les say you have a data that is has more noise and now you will need to decide which data analysis technique will be more useful and how do you start data visualization.
Choosing the right data visualization technique requires understanding your data, your audience, and the goals of your analysis.
Key Considerations:
1. Understand the Data
Type: Categorical, Numerical, Temporal, Geographical.
Shape: Distribution, Trends, Correlation, Noise.
2. Define Goals
Exploratory or Explanatory analysis
Technical or Non-technical audience
3. Handle Noise
Preprocessing: Smoothing, Normalization, Filtering
Outlier Detection: Box plots, Z-scores
Dimensionality Reduction: PCA, t-SNE
4. Choose Visualization Techniques
Univariate Analysis: Histograms, Box plots.
Bivariate Analysis: Scatter plots, Pair plots.
Multivariate Analysis: Heatmaps, Parallel Coordinates.
Time Series Analysis: Line plots with Moving Averages.
Categorical Data: Bar plots, Mosaic plots
Usually, I used: box plots to determine outliers (the possibility to visualise all observations by adding glitters), radar plots for clusters, 100% stacked bar charts for patterns, time-sequential plots or adapted bar charts for time,-series analysis, deviation plots for data exploration, Roc curve to determine the right sensitivity or recall of a model, and variable importance to determine which variables contribute the most to affect your dependent variable
1. Identify if your data is categorical, numerical, time-based, etc.
2. Determine if you need to compare values, show distributions, highlight relationships, or track changes over time.
3. Tailor complexity to the audience's expertise.
4. Select the Appropriate Chart:
Bar/Column Charts: For comparisons.
Line Charts: For trends over time.
Pie Charts: For parts of a whole.
Histograms: For distributions.
Scatter Plots: For relationships.
Heatmaps: For data density or correlations.
5. Consider Data Volume: Ensure the chart handles the data size effectively.
6. Highlight Key Insights: Use colors, sizes, and annotations to emphasize important points.
7. Choose the Right Tools: Use tools like Tableau, Power BI, or D3.js for advanced visualizations.
8. Iterate and Refine: Improve your visualization based on feedback and additional analysis.
By following these steps, you can effectively present your data insights.
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