What are the most important statistical concepts for Data Visualization?

I believe it is critical to understand not only the type of data but also the implications of that type for analysis. For example, treating ordinal data as categorical could cause the data's inherent order to be overlooked. In the same way, applying numerical encoding to nominal data may result in inaccurate statistical analysis. Therefore, identifying data types involves more than just visualization; it also involves maintaining the integrity of the entire analytical process.

First things first, understanding your data types is like knowing the building blocks of your analysis. For example, when you're dealing with customer data, you might have categorical data (like product types), numerical data (like age or income), or even date-time data (like when purchases were made). Knowing what you've got helps you choose the right visualizations.

??In marketing, here are the **must-know** stats for data: 1. Descriptive Stats: Basic numbers, easy peasy! 2. Correlation: Connect the dots between data. 3. Regression Analysis: Predict like a wizard. 4. Segmentation: Group customers for targeted magic. 5. Time Series Analysis: Time travel for trends. 6. A/B Testing: Test, visualize, conquer! 7. Customer Lifetime Value (CLV): See big spenders. 8. Geospatial Analysis: Explore markets on a map. 9. Funnel Analysis: Spot drop-offs, boost sales. 10. Cohort Analysis: Compare customer squads. Use these essentials!

Don't over complicate. What's the question being answered? Who's your audience? More specifically, what visuals are they used to?

Ask first why do you want to have your dataset visualized? Then understand your audience and think about what their background is in regard to both the data and how they process information. Lastly, if seeking to influence or maybe alter your audience’s work by helping them understand their outcomes as measured against their production use visuals to gradually inform them of their success meeting objectives.

From my point of view, understanding how data is distributed is more than just picking the right way to show it. It's also important to understand the behaviors and events that led to this distribution. For instance, a bimodal distribution in sales data could mean that there are two different types of customers or two different times of the year when people buy things. Seeing these patterns can give you business insights that you can use, not just illustrations.

Think of this as the way your data is spread out. Imagine you're looking at the scores of students in a class. Are the scores bunched up close to each other, or are they all over the map? This can affect how you visualize and interpret your data.

Some datasets exhibit a uniform distribution, where data points are evenly spread across a range of values. This type of distribution may indicate randomness or equal likelihood of any value occurring. In some cases, datasets may exhibit multiple peaks or modes. A bimodal distribution has two distinct peaks, while a multimodal distribution has more than two. Identifying and understanding the modes can provide insights into underlying patterns or subpopulations within the data.

While creating a predictive model, or figuring out an analytically challenging concept, visualizing the distributions for each feature / attribute is crucial. Understanding the distributions in a visual manner will help you understand the details, dominant values, outliers, even help you segment it in a simple way. While this process is crucial and can't be emphasized less, it may be very misleading if it is not done properly. Manipulating the axis, scale for a specific attribute would lead an improper view or bias towards data. Picking the correct type of visualization, e.g. pie chart is upmost importance in understanding the details in your data.

Data distribution refers to how data values are spread or arranged within a dataset. It is a fundamental concept in statistics and data analysis, providing insights into the patterns and characteristics of the data. Common data distributions include: 1. Normal Distribution 2. Uniform Distribution 3. Skewed Distribution 4. Exponential Distribution 5. Log-Normal Distribution 6. Poisson Distribution 7. Binomial Distribution 8. Multimodal Distribution 9. Power Law Distribution Understanding data distribution helps in selecting appropriate statistical methods and drawing meaningful conclusions from data. Visualization tools like histograms and box plots are commonly used to explore data distributions.

To me, correlations and relationships are important, but it's also important to remember the old saying, "correlation does not imply causation." It's easy to think that two variables moving together are directly linked to each other. But these kinds of conclusions can be wrong if they aren't based on careful research or results from experiments. Always dig deeper to find out what's really going on or what factors could be making things more complicated.

Imagine you're analyzing data on movie ratings. You might wonder if there's a relationship between a film's budget and its box office success. Understanding data relationships helps you uncover patterns and connections like this.

In my experience, it start your understanding of basics (descriptive statistics) which will be handy in your data cleaning and processing, after that the knowledge of relationships and associations between your features is a blast. Your visualisation is as good and useful as the quality of features that made it to your final modelling and visuals.

Understanding data relationships is the crucial thing in creating insights, visualization or a data model. They are important to choose the right way to depict the data

In data visualization, grasping data relationships is paramount. These relationships reveal how variables interact and influence each other. For instance, a positive correlation indicates that as one variable rises, so does another, while a negative correlation shows an inverse relationship. Visual tools like scatter plots or heat maps can vividly depict these connections, enabling viewers to discern patterns or trends at a glance. Moreover, understanding relationships can guide the choice of visualization type and scaling, ensuring clarity and accuracy. Ignoring these relationships risks oversimplifying or misrepresenting data. In essence, recognizing and accurately representing data relationships is crucial for crafting visualizations.

Think of this as combining data to see the bigger picture. For example, you might have sales data for every hour of the day, but you want to know the total daily sales. That's data aggregation - summing up the details to get a broader view.

Data aggregation is a process in which raw data is collected and transformed into a summarized or consolidated form. This technique is widely used in data analysis and reporting to simplify complex data sets, reduce redundancy, and make data more manageable for interpretation and decision-making. Here are some key aspects and methods of data aggregation: 1. Summarization 2. Grouping 3. Time Aggregation 4. Hierarchical Aggregation 5. Data Reduction 6. Visualization 7. Rolling Aggregation 8. Data Cube 9. SQL Aggregation Functions 10. Pivot Tables Data aggregation simplifies complex datasets, enhances data analysis efficiency, and supports informed decision-making.

Data aggregation is the process of collecting and combining data from multiple sources into a single, unified view. This can be done manually, but it is often automated using software tools. Data aggregation is important because it allows organizations to: 1)Gain a better understanding of their busines 2)Improve efficiency 3)Reduce costs

Familiarity with common probability distributions (e.g., normal, binomial, Poisson) is essential for visualizing data and making inferences. Knowledge of hypothesis testing concepts, such as p-values, confidence intervals, and effect sizes, can help you create visualizations that communicate the results of statistical tests effectively. Understanding sampling methods and their implications is crucial for creating visualizations that accurately represent the population from which the data is drawn. Techniques like log transformation, z-score normalization, or standardization can be used to make data more suitable for visualization, especially when dealing with data of varying scales.

In my experience, the most misleading aggregation type is the average (mean). When you condense multiple data points into a one value, you lose insight about the story they told. This is true of averages taken in K-12 education, which are often used to summarize data from test scores to daily attendance. The impact of this can be masking inequities between groups included in the average (such as differences in student outcomes by economic advantage or transportation access), and failing to identity group-specific root causes. To mitigate these risks, consider using percentages against a relevant benchmark (such what % of students scored higher than the state average), and exploring different views by groups that best visualize reality.

Data is important, as much as the lack of data. In certain cases, or rather quite often, available datasets only capture a portion of the phenomenon. Being able to visualise the absence of accurate data is just as crucial as the ability to visualise existing data accurately. A statistical concept that can help assess the exhaustiveness and completeness of a dataset is the concept of representativity. If we can draw accurate conclusions about a population from a sample, we refer to it as representativeness. In a narrower sense, a sample is representative if all the elements in a population had an equal chance of being part of the sample. It's useful to question whether the datasets we are visualising are based on representative samples.

Data uncertainty refers to the lack of complete or precise knowledge about data values, accuracy, or reliability. It arises from various sources such as measurement errors, missing data, ambiguity in definitions, and more. Dealing with data uncertainty involves: 1. Quantifying Uncertainty 2. Data Imputation 3. Sensitivity Analysis 4. Probabilistic Models 5. Data Quality Assessment 6. Expert Judgment 7. Risk Management Acknowledging, quantifying, and managing data uncertainty are vital for accurate data-driven insights and informed decision-making.

Data isn't always crystal clear. Imagine you're tracking the number of people who visit a website. Some days, you might have a spike in visitors, but you're not sure why. That's data uncertainty - when you're not entirely sure what's causing the fluctuations.

Data uncertainty is the degree to which data is inaccurate, imprecise, untrusted, and unknown. It can arise from a variety of sources, including: 1) Measurement errors 2) Sampling variability 3)Missing data 4)Ambiguity 5)External factors

When communicating data uncertainty in visualizations, transparency is key. Include error bars, confidence intervals, or shaded regions to represent uncertainty. Use clear labels and legends to explain these elements. Additionally, consider adding tooltips or annotations to highlight specific data points affected by uncertainty. Document your methodology and data sources in a caption or footnote, and if possible, provide alternative scenarios or sensitivity analyses to convey the range of possibilities. Finally, be mindful of the audience's statistical literacy and strive for simplicity without oversimplifying the complexity of uncertainty.

Visualization is not the art of reading the lines of data and demonstrating it in pictures, it is the art of reading between the lines and understanding what kind of visualizations might help uncover the mysteries of valuable insights that must be hiding in the data. More often than not, I have seen people visualize obvious data points in aesthetic ways that look good on a dashboard but don't really add value to the analysis. It is important to chase the variables that seem to have an obscure relationship with other important factors in the data. That is where the story will begin. That will inevitably become the hook of your storytelling stint.

When telling the story keep in mind to contextualize it. In many statistical analyses, especially in social and political scenarios, lack contextualization, disseminate prejudice, blame individuals for systemic issues, and reinforce stereotypes. It is key to understand the limits of your story and the responsibility when telling it. We tend to believe that our work is infallible because we have great statistics. It is not. Even if your data is great and your technique is flawless, make sure to bring in extra data and context that out your own statistical work into perspective. Good or bad is always a matter of comparison. Make sure to incorporate other perspectives, not just your own.

?? Is the sample size statistically representative of the group? When evaluating any chart, statement with numbers, or news headlines involving statistics, my initial step is to assess the sample size. Using my own experience as an example: 90% of the individuals I interact with on social media discuss AI. But does this imply that 90% of all internet users are discussing AI? Certainly not. This observation stems from the 100 individuals I frequently engage with or follow on social media (sample size = 100). It's crucial to recognize that my sample size isn't statistically representative of the broader internet population.

All fireworks aside, one simple thing can bring you a long way towards an effective storytelling - making sure you know your intended message, that it's clear to you, and that is aligned with stakeholders. After that, data presentation becomes an effortless, natural exercise to any capable data professional

Keep it simple. What's the audience used to seeing? What do you want to highlight/or call out? Build your storytelling around those 2 questions.

In my view, one of the most crucial statistical concepts for data visualization is data storytelling. It's paramount because your ability to convey recommendations to your audience depends on how effectively you narrate the story from the initial data extraction and transformation to its visualization. Being able to unveil a compelling narrative through visuals is key. For instance, I've found that highlighting how Andrew emerged as the top-performing staff at the beginning of the year, backed by specific KPIs like deliverables and timestamps, exemplifies the power of data storytelling. It bridges the gap between raw data and actionable insights, making it a cornerstone in the realm of data visualization.

1. Summary Statistics: These provide a quick overview of the data, especially useful for financial and business data 2. Outliers: Identifying data points that deviate significantly from the norm, which can be represented using box plots or scatter plots. 3. Trends and Patterns: Detecting patterns and trends in the data through line charts, time series plots, or heatmaps. 4. Data Transformation: Applying mathematical transformations like logarithms or percentiles to make data more suitable for visualization.

If you don’t understand how your data was collected, you don’t understand your data. Before diving into data analysis, and the technical components of it, take time to understand the collection processes, systems, and human elements that are involved of gathering the data in the first place. For example - In K-12 education, if you’re looking at student attendance data, do you understand how and when attendance data is collected? Elementary schools often gathered once a day at the beginning of school, whereas secondary schools, take it every class period. Attendance systems also default to marking students as present if there is no attendance taken, which can lead to misleading attendance results if you don’t know how to separate it out.

Understanding key statistical concepts is essential for creating effective visualizations. Here are some of the most important statistical concepts for a good data visualization: - Summary statistics - Frequency distribution - Correlation - Probability and percentiles - Hypothesis testing - Outliers - Confounding variables - Data transformation - Sampling bias - Statistical Significance: Be mindful of statistical significance and avoid making unwarranted conclusions based on visual patterns that may occur by chance.

Missing data should also be considered. Imputation can be effective if there is a clear answer. If a pattern already exists or the volume of data is large enough that using a mean doesn’t compromise the quality, those are options. In some cases, leaving missing data in the dataset is a valid way to proceed. In other cases you’ll have too many missing values to reasonably impute the data. Typically, when there are missing values of more than 5% of the whole, it is best to consider other methods. The bottom line is to consider the goal of the analysis, whether missing values are part of the story, and whether the data is still valid if missing data is imputed, omitted or retained.