Data exploration is a critical initial step in the data analysis process, involving the examination, cleaning, and transformation of data to uncover patterns, anomalies, and relationships. This process not only helps in understanding the dataset's structure and content but also in formulating hypotheses for further analysis and predictive modelling.

In this tutorial, we will be using a specific dataset called "Example dataset.xls." This dataset contains typical information for data exploration exercises in the Healthcare sector.

Our example dataset contains a subset of data collected for a study that investigated the association between post-partum mortality (death within the first 6 months) of women with peripartum cardiomyopathy (PPCM) and several factors. These factors include maternal age, maternal haemoglobin levels, weight, heart rate, systolic blood pressure (SBP), and C-reactive protein (CRP) levels. To access the dataset and follow along with this tutorial, please click on the link provided *here*.

Please find below a detailed, step-by-step walkthrough and explanation of how to perform data exploration in Julius AI. Screenshots have been provided for each step to help guide you through the process.

The first step in data exploration with Julius AI is loading your dataset. You can easily upload your dataset to Julius by clicking the [ðŸ“Ž] button in the chat interface and selecting your file (See the screenshots).

Note: The previous steps assume that you have already downloaded the example dataset into your PC.

After loading the dataset, it is essential to get an overview of its structure and content. This can include a description of the size of the dataset, the number of columns, and the types of data contained within. You can ask Julius to display the first few rows of the dataset and provide a description of the data types. When you ask Julius for something, it feels like talking to a friend. You don't need to use complicated phrases or prompts; simple language is the most effective. For example in the following screenshot, we asked Julius to display a preview of our dataset.

Prompt: "Can you display a preview of the dataset attached". (See Julius' response below).

Note: Julius displayed the data preview and explained that the dataset contains health-related measurements and patient information, including age, hypertension status, height, weight, blood pressure (SBP and DBP), heart rate (HR), cholesterol levels, glucose levels, and more. The dataset also includes columns for medical assessments and outcomes, such as mortality, NYHA functional classification (NYHAFC), and echocardiography results (e.g., EFonEcho for ejection fraction).

Additionally, you can ask Julius for information on the structure of the data in the dataset.

Prompt: "Can you describe this dataset i.e. the size of the dataset, the number of columns, and the types of data contained within." (see Julius' response below).

Note: Julius has revealed that the dataset contains 100 rows and 31 columns. He also mentions that the dataset is primarily composed of numerical data types, with the majority being integers and the rest being floating-point numbers. Julius has also provided information on which variables fall into each data type.

You can add a follow-up prompt such as; "What are integers and floating-point numbers. Use examples from the dataset"

Julius explained that integers are whole numbers that can be positive, negative, or zero, but they do not have decimal points. For example, the variable "Age," contains integer values such as 32,46 and so on. On the other hand, floating-point numbers are numbers that have a decimal point, allowing them to represent fractions and real numbers. Floating-point numbers can represent a wide range of values, from very small to very large, with varying degrees of precision. For example, the variable "cholesterol" contained values such as 5.0, 4.6 and so on.

You can also ask Julius to summarize the key variables of the dataset

Prompt: "Provide a summary of the key variables in the dataset?"

Note: Julius has summarized the meaning of each variable and explained how it was coded in the dataset. The great thing about Julius is that it does not just rely on your prompts for a conversation. Instead, it takes the initiative by suggesting relevant questions that can help you better understand your data or determine the next steps you should take (See bottom of every response). This feature is particularly beneficial for those who lack experience in data analysis or may be unsure of what they should do.

Based on the initial overview, you might need to clean your data. For example, in the next screenshot, we asked Julius to assist us in cleaning and preparing the dataset for analysis.

Prompt: "Julius can you help me clean this dataset and prepare it for further analysis"

Note: As you can see, with just a simple prompt, Julius was able to suggest the necessary steps for dataset cleaning and preparation. This is crucial in guiding our subsequent analysis.

However, in this tutorial, we will focus on exploring and familiarizing ourselves with the data, rather than diving deeply into data cleaning. By getting to know our data better, we can make informed decisions on how to prepare it for analysis. Data cleaning will be covered in the next tutorial. However, Julius can help identify missing values, outliers, or incorrect data types and suggest ways to address these issues. This can be seen in the screenshots below.

Note: Julius helped identify variables that had missing values. He also provided a summary of the amount of missing data and suggested possible ways to handle them, such as imputation or removing rows/columns with missing data. These decisions are important and need to be made during the data-cleaning process.

Next, we asked Julius to identify any outliers for specific key variables in the dataset. Julius then proceeded to explain what outliers are, how to detect them, and how to handle them.

Prompt: "Can we explore the distribution of the numerical variables to identify potential outliers?"

Note: Firstly, Julius explained that outliers are data points that greatly differ from the rest of the observations. They are represented as points that fall outside the whiskers of the boxplots. He then plotted boxplots to show potential outliers in the numerical variables from our dataset. He further explained that variables such as CRP, Gluc, K, Na, and IL6av had many outliers, indicating that these measurements vary greatly among the patients. This variability could be attributed to natural differences in the population, measurement errors, or other factors.

Based on these findings, Julius suggested that it may be necessary to determine how to handle these outliers, depending on the goals of the analysis. Options include further investigation to understand their cause, removing them, or applying transformations to minimize their impact. These will be covered in the next tutorial (Data cleaning and preparation).

You may also want to ask Julius to perform descriptive statistics, create visualizations, and identify patterns or correlations on selected key variables to help you better understand your data.

For example, in the next screenshots, we requested Julius to perform descriptive statistics on the key variables in the dataset. Julius provided a table displaying the variables' mean, standard deviation, 25th, 50th, and 75th percentiles, as well as the minimum and maximum values. Additionally, Julius provided an explanation of these results for each variable.

Prompt: "Julius can we generate descriptive statistics for the key variables in this dataset?"

Note: As Julius pointed out, these statistics serve as a basis for comprehending the health status of the cohort and pinpointing areas that require further investigation. This is particularly crucial for variables that exhibit significant variability or potential outliers. Understanding the distribution of your data through these statistics and data visualization will help determine if data transformation is necessary, which tests should be conducted next, and how to report the results effectively.

We asked Julius to create various visualizations to explore the important variables in the dataset. Visualizing the data is essential for gaining insights and identifying patterns. Julius provided well-designed histograms, scatterplots, and heat maps. As always, each plot was accompanied by a description or interpretation.

Prompt: "Can we create different visualizations to further explore the key variables in the dataset?"

Each of these visualizations below provides unique insights into the data, making them invaluable tools for exploratory data analysis. For example: Histograms, scatterplots, and heatmaps are three types of data visualizations that help in understanding the distribution, relationships, and correlations among variables in a dataset.

Note: Histograms are a type of bar chart used to visualize the distribution of a numerical variable. They divide the data into intervals or bins and display the frequency or count of observations in each bin. Histograms provide insights into the shape, spread, and central tendency of the data. They can indicate if the data follows a normal distribution, is skewed, or contains outliers. Julius examined the distribution of age, height, weight, SBP, DBP, and HR, focusing on their range and the presence of peaks in each variable.

Note: The scatterplot shows the relationship between weight and height using a two-dimensional graph. Each point represents a data observation and is located based on the values of the two variables. Scatterplots are useful for identifying trends, patterns, and possible correlations between variables. A positive trend in a scatterplot suggests that as one variable increases, the other variable also tends to increase. In this example, the scatterplot demonstrates a positive correlation between height and weight, indicating that taller individuals generally weigh more.

Note: The heatmap is a graphical representation that uses colours to depict values in a matrix. It is commonly used to visualize the correlation matrix of variables, with colour intensity indicating the strength and direction of the correlation. Heatmaps are useful for quickly identifying strong correlations between variables, which can lead to the discovery of potential relationships worth investigating further. In our example, the heatmap reveals insights into the relationships between important variables, as pointed out by Julius. Notably, there is a positive correlation between height and weight. Additionally, other variables display varying levels of correlation, providing valuable information for analyzing how these health indicators may interact with each other.

To enhance the clarity and comprehension of your graphs, feel free to include additional follow-up prompts, specifically designed for interpretation and understanding of the visualizations. "histograms, scatterplots and heatmaps. what are they and what do they tell us?"

Once you have a clear understanding of your data and your data analysis goals, you can make informed decisions about how to proceed. This may involve further cleaning, manipulating your data and exploring more advanced analysis techniques. If you need assistance with predictive modelling, clustering, or any other machine-learning tasks, Julius is here to help.

Note: This is not the only data exploration Julius can do but this is just a guide to what you can do to get the most out of the AI. Each data analysis and exploration may be unique to individual needs. Just take Julius as that friend you can talk to and explain in detail what you need help with, and he will understand and tailor his responses to your needs or your data needs. Feel free to explore and try out different things.

Here are some tips to make the most out of your data exploration with Julius AI:

Start with a clear question or objective in mind. This will guide your cleaning and analysis steps.

Strive for clarity by using simple and easily understandable language. Provide detailed explanations when necessary.

I recommend breaking down your analysis into individual steps and having Julius present the results of each step. This will allow you to assess whether you are achieving the expected outcomes and if Julius has understood your instructions and made any necessary adjustments.

Use visualizations to get a better grasp of your data. Julius AI can generate a variety of plots to help you see patterns and outliers.

Be mindful of any missing values in your dataset and determine the best strategy for dealing with them, whether that be imputation or exclusion.

Make sure not to overlook any categorical variables in your analysis. Use bar charts or frequency tables to examine their distributions.

Remember that data exploration is typically a step-by-step process. Continuously explore various variables and visualizations to obtain a thorough understanding of the dataset.

Explore different aspects of your data. Julius AI is designed to handle a wide range of data exploration tasks, from simple descriptive statistics to complex predictive models.

Don't hesitate to ask Julius for help with specific tasks, whether it's data cleaning, analysis, or even machine learning predictions.