How do you explore and confirm data using visualization?

If it’s difficult to find the right type of visualization, this might be because no data visual is appropriate for the situation or data at hand. Don’t force data into visuals. A simple stat can suffice. A picture is worth a thousand words, but if you can get your point across in a sentence (much less than a thousand words), why the need for the picture? Be deliberate with your data communication!

The right type of visualization is what your non-technical audience can understand. Surely there are certain types of data visualization that can fit certain data types, However, Some complex visualizations do require expertise to actually come up with insights. And mainly in your day-to-day job, you create interactive dashboards for stakeholders to check on numbers. Therefore, Choosing the right visualizations that your audience “can understand” is the best way to do it.

Choosing the right type of visualization is essential for effectively conveying your data's insights and telling a coherent story. Here are some key considerations to help you make the right choice: 1. Data Type and Relationship 2. Message 3. Audience 4. Accuracy vs. Simplicity 5. **Visualization Types:** - Bar Charts and Column Charts - Line Charts - Pie Charts - Scatter Plots - HeatMaps - Histograms - Box Plots - Maps and Geospatial Visualizations - Gantt Chart 6. Color and Design 7. Interactivity 8. Data Density 9. Storytelling 10. Testing Remember, the right type of visualization enhances understanding and insight. Choose wisely based on your data, audience, and the story you want to tell.

The primary step we all need to consider is Data visualization is dependent on data transformations and data modeling. So, the first step is to know the requirements of the business user. Then, make a decision to transform the data on the source or Power BI - which will help us to avoid performance issues, data loss, and inaccuracy. Then, the data modeling- the complex and prominent step of the Power BI report. Now, based on the requirements and data available- choose the correct visualization. Make the visuals interactive, and dynamic. Use minimal colors or monochrome palettes. Then, compare the values and verify them with the data source with all the permutations and combinations of interactions you have provided.

The best way to present our data, is that to understand the audience. Some of them may be Kinaesthetic, Auditory or visual person.

The best way to visualize your data prior to running any machine learning model is to do exploratory data analysis. With this you can see the relationship that exist between the different variables in your data and the target variable (dependent variable y) to streamline which variables you want as your predictor variables i.e independent variables (x).

Exploratory and confirmatory analyses are distinct approaches in data analysis. Exploratory analysis involves initial data exploration to identify patterns, relationships, and potential insights. It's flexible and often used when the goal isn't predefined. Researchers generate hypotheses based on data exploration. In contrast, confirmatory analysis tests predefined hypotheses using established statistical methods. It's more rigid and requires careful planning to avoid bias. Both approaches are crucial for robust conclusions. Exploratory analysis is creative and open-ended, while confirmatory analysis is rigorous and hypothesis-driven. Combining both ensures a comprehensive understanding of data while maintaining statistical rigor.

Most people hates to read long texts or summary. So, to attract people to read and know what you analyse for, use numbers. ??

Exploratory and confirmatory analysis are two different approaches to analyzing data, and they serve distinct purposes in the data analysis process. You have to combine both of them to get the best results. Exploratory analysis contains: Data Exploration,Identifying Patterns, Generating of Hypothesis, Data Cleaning. Confirmatory analysis represents Hypothesis Testing, Model Building, Validation of Hypotheses and Model, Reporting.

Before the data is visualized an understanding the dataset being used and the pipeline it went through would give a good understanding on how clean, accurate and effective the data is.

Data cleaning involves identifying and rectifying errors, inconsistencies, and inaccuracies in the dataset. Data transformation involves converting data into a suitable format for analysis. This includes normalizing numerical data to a common scale, encoding categorical variables, and aggregating data for higher-level insights. Effective data cleaning and transformation lead to more accurate and meaningful results. They improve data integrity and reduce the risk of drawing incorrect conclusions. These processes are essential in ensuring that your data analysis is based on reliable and consistent information, ultimately contributing to better decision-making and insights.

I like to use macro VBA to summarise validate and transform raw data. VBA is one of good tool for data validation. But somehow, Microsoft needs further refinement to their software as it tends to lag when run into long programming language.

Clean and transform data by removing missing values, handling outliers through statistical methods like z-score, normalizing numerical features, encoding categorical variables with label encoding, and aggregating or summarizing data for efficient visualization, reducing dimensionality ensuring optimal data representation.

Data cleaning and transformation are crucial steps in the data preprocessing pipeline, ensuring that your data is accurate, consistent, and ready for analysis. For Data Cleaning - Start by identifying missing data in your dataset. Decide how to handle missing values. You can either remove rows with missing data. Depending on your analysis, you can choose to remove outliers or transform them to reduce their impact. Ensure that data types are appropriate for analysis. Check for inconsistent data entry or naming conventions. For Data Transformation - Normalize numerical features. Create new features that might enhance your analysis. Group data by a specific variable and calculate summary statistics for analysis.

Interactive visualizations engage users by allowing them to explore data dynamically. Filters, tooltips, and zooming enable users to focus on specific subsets or details. Interactive elements foster a deeper understanding as users manipulate the visualizations, making insights more personalized and actionable. However, while interactive visualizations enrich exploration, they require thoughtful design to prevent overwhelming users. Simplicity is key; avoid overcrowding and prioritize user experience. Incorporating multiple and interactive visualizations offers a dynamic and layered understanding of data, catering to different perspectives and enabling users to uncover deeper insights, fostering more impactful decision-making.

Tips: When using multiple visualizations, craft a clear narrative or question. Maintain design consistency for a cohesive look across visuals. Use interactive elements judiciously, like tooltips and filtering options. Organize dashboards logically, considering layout and flow. Provide context through text, titles, and annotations while optimizing for accessibility and performance.

One important consideration is also ensuring the range of inputs is always appropriate in interactive apps. e.g. will too many filters potentially lead to one or a handful of observations being presented, which could expose individual level data you didn't mean to. And if the interactive app contains hypothesis testing outputs (e.g. KM plots with a p-value), is there a danger of conclusions being drawn from random chance in your user base?

To evaluate and refine your visualizations effectively: Review for clarity and accuracy. Seek feedback from others. Test usability and responsiveness. Check for bias and performance. Iterate based on feedback. Document changes for transparency. Final review before presentation or publication.

Consider the context and story. Do the visualizations contribute to the narrative? Refine by removing unnecessary elements that distract from the core insights. Seek feedback from peers to identify areas for improvement. Usability matters. Test interactivity – filters, tooltips, zoom –on different devices to ensure smooth navigation. Adjust for responsive design Optimize aesthetics. Balance colors and use a consistent palette. Limit excessive embellishments that hinder comprehension. Simplify complex visualizations without sacrificing information. Address user engagement. Is it easy to grasp the main points? Refine the visuals to guide viewers smoothly through the data story. Ensure titles, captions, and annotations guide interpretation.

Every visualization serves a purpose. And we always need to ask ourselves : What question is this visualization answering?" If there isn't a clear answer, we might need to change viz or the selection of data that is made of.

The best communication to have about visualisations is to have a template with aligned slicers and navigation page and the KPIs position. Whether we use a Vertical template or a Horizontal, the positioning of visuals portrays the priority of information. For example: we have KPIs(Most important information) at the top, followed by comparison values and representation of data. Also, the color palette for primary and secondary data. Also for neutral, warnings kind of data - these kinds of information can be shared.