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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

All About AI-powered Data Analysis with Vizly

Sami Sahnoune
Sami Sahnoune Last Updated : 18 Jul, 2024
5 min read

Introduction

Generative AI has been at the forefront of recent advancements in artificial intelligence. It has become a part of every major sector, from tech and healthcare to finance and entertainment, and continues transforming our work. It has enabled us to create high-quality content and perform complex tasks in minutes.

Now, imagine a world where you can use simple text prompts to harness the power of generative AI, allowing you to extract insights from your data in seconds. Welcome to Vizly, an AI-powered data analysis tool that academia and industry alike use to perform advanced data science easily. Vizly allows you to visualize data with interactive visualizations, chat with your data to uncover actionable insights, and perform complex analyses and predictive insights, including ANOVA models and support for R and SPSS.

Vizly

Learning Objectives

  • Demonstrate how to effectively use Vizly to perform complex data analysis tasks using text prompts and automated suggestions.
  • Explain the process of uploading and managing data within Vizly, including supported data formats and data privacy measures.
  • Create and customize interactive visualizations using Vizly’s out-of-the-box support for Plotly.
  • Apply advanced statistical techniques and predictive insights, such as ANOVA models and other analytical functions typically operated by R and SPSS.
  • Collaborate effectively using sharing features to make the data analysis process more efficient and impactful.

This article was published as a part of the Data Science Blogathon.

Table of contents

What is Vizly?

Vizly is an AI-powered data analysis tool designed to make data exploration and insight extraction effortless. Unlike traditional data analysis tools that require extensive manual effort, Vizly automates tedious tasks and enhances productivity through the power of generative AI to generate actionable insights and interactive visualizations. It has a number of key features:

Key Features of Vizly

  • Actionable Insights: Chat with your data using simple text prompts to uncover actionable insights. Vizly converts your query to code, which it uses to craft a data-driven response.
  • Interactive Visualizations: Create and edit stunning, interactive visualizations with out-of-the-box support for Plotly
  • Automated Suggestions: Automatically generates interesting questions about your dataset upon upload.
  • Complex Analysis and Predictive Insights: Perform advanced analyses, such as ANOVA models and analytical functions supported by R and SPSS, making it ideal for academia and industry professionals.
  • Datasource Support: Supports CSV, Excel, and JSON documents for seamless data analysis. Database support is available on a request basis.
  • Effortless Sharing: Share your analysis with ease by exporting to a PDF, website, Word document, or Python notebook. Alternatively, you can generate a public link to share your insights instantly.

Getting Started with Vizly

To start using Vizly, follow these steps:

Step1: Access Vizly

Visit the Vizly website. Click here to access.

Step2: Sign up by Clicking Get Started

Create an account by signing in via Gmail or GitHub, or by entering your email for a temporary access code. You will immediately get access to Vizly’s advanced AI for data analysis.

Step3: Upload Your Data

Upload your data in CSV, Excel, or JSON format. Vizly will automatically suggest the most insightful questions to ask about your dataset. 

Using Vizly for Data Analysis

Once your data is uploaded, Vizly automatically generates suggestions for interesting questions to ask about your dataset.

Example: Analyzing Adidas Sales Data

Let’s say you have a CSV file containing sales data for Adidas products with the following columns. 

  • Retailer
  • Retailer ID
  • Invoice Date
  • Region
  • State
  • City
  • Product
  • Price per Unit
  • Units Sold
Vizly displays the uploaded datasource preview so that users always maintain context.

Vizly automatically suggests questions to ask, such as:

  • “What are the total sales by region?”
  • “What is the operating profit by retailer?”
  • “How many units were sold per product?”

Generating Insights

You can select one of the suggested questions or type your own prompt to get insights. For example, if I ask:

After I query Vizly, I receive the following insight:

Generating Insights
output

Creating Visualizations

Vizly can also automatically generate interactive visualizations by simply asking in plain English. Here’s an interactive visualization of the analysis shown above:

A bar chart visualization of the analysis represented above. Vizly also supports dark mode, shown here.

Sharing

Sharing your analysis is effortless with Vizly. Simply click a button to export your work to a PDF or Python notebook, or generate a public link to share your analysis instantly. You can view the analysis mentioned in this article here.

Advanced Features

  • Vizly also supports advanced statistical techniques and predictive insights, and we can see Vizly utilize the analysis of variance (ANOVA) method on the sales and operating profit by region of the Adidas dataset here:
advanced features
  • You can also follow up on queries with Vizly and ask it to elaborate on its results – this can help users better understand the AI-powered analysis.
Annova results
  • Other analytical functions typically operated by R and SPSS can also be used within Vizly, such as regression analysis, factor analysis, cluster analysis, and time series forecasting.
  • Vizly ensures users’ privacy and data security by only hosting your data for one hour, while ensuring your conversation with the AI is always accessible to you and you only.

Conclusion

Vizly is a powerful tool that leverages the capabilities of AI to provide quick and insightful data analysis. It simplifies the process of data exploration and visualization, making it accessible to users across various sectors and empowering users to gain valuable insights from their data effortlessly. Throughout this article, we’ve explored how Vizly can help you perform advanced analyses, such as ANOVA models and other statistical techniques typically operated by R and SPSS. These advanced features make it an ideal tool for both academia and industry professionals.

Sharing your analysis is also seamless with Vizly, whether you choose to export your work to a PDF or Python notebook or generate a public link for instant sharing. This ensures that you can easily collaborate with colleagues and stakeholders, making your data analysis process more efficient and impactful.

Key Takeaways

  • Vizly offers AI-powered data analysis with interactive visualizations.
  • It supports various data formats and provides a clean, intuitive analytical interface.
  • You can generate complex insights and visualizations with simple text prompts
  • Vizly supports advanced statistical techniques and predictive insights, making it ideal for academia and industry professionals.
  • Using Vizly can help transform your data analysis workflow and help you get from data to insights in seconds.

Frequently Asked Questions

Q1. What is Vizly?

A. Vizly is an AI-powered data analysis tool that simplifies data exploration and insight extraction with automated tasks and interactive visualizations.

Q2. How does Vizly ensure data privacy?

A. Vizly hosts your data for only one hour and ensures that your conversations with the AI are accessible only to you.

Q3. What types of data formats does Vizly support?

A. It supports CSV, Excel, and JSON documents for seamless data analysis. Database support is available on request.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

AIdata analysisinsightsquestionsvisualizationsvizly
Sami Sahnoune
Sami Sahnoune

Data scientist and software engineer interested in building a future powered by artificial intelligence tools. Previously at Plotly, NASA's Jet Propulsion Laboratory, and Splunk where I specialized in data visualization.

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