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Reading list

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
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 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

Most Used 10 Power BI Charts

Yana Khare
Yana Khare 12 Aug, 2024
8 min read

Introduction

Overview

  • Power BI provides various charts to convert data into visual, easily understandable formats.
  • Charts in Power BI help with data visualization, trend analysis, comparative analysis, decision-making, and interactive reporting.
  • Popular charts include bar/column, line, pie/donut, area, scatter/bubble, TreeMap, waterfall, funnel, gauge, and maps, each serving specific purposes.
  • Choose the best chart based on your data type, purpose, audience, and required features like interactivity and customization.

Table of contents

What are Power BI Charts?

Also Read: What is Power BI? Architecture, Features and Components

How are Power BI Charts Useful?

  1. Data Visualization: They take large volumes of data and transform and present the informative data in a manner that can easily be understood at first glance.
  2. Trend Analysis: A chart can show trends and patterns or different classifications over time.
  3. Comparative Analysis: To derive meaningful insights, users can compare data points, categories, or periods.
  4. Decision Making: By presenting data visually, charts aid in better decision-making based on clear, data-driven insights.
  5. Interactive Reporting: Power BI charts are interactive, allowing users to drill down into specifics, filter data dynamically, and interact with the visualizations to explore different aspects of their data.
  6. Communication: They effectively communicate data insights to stakeholders, making presentations and reports more engaging and understandable.

Most Used Power BI Charts

Let us now look at the top Power BI charts.

1. Bar and Column Charts

Bar and column charts are among the most fundamental visualizations in Power BI. They compare values across different categories. Bar charts display data with horizontal bars, while column charts use vertical bars.

Key Features:

  • Comparative Analysis: Ideal for comparing discrete categories.
  • Trend Identification: Effective for showcasing trends over time.
  • Categorical Display: Easily distinguish different categories with varying bar heights/lengths.
  • Interactive Elements: Can include tooltips, legends, and data labels for enhanced interactivity.

Types:

Stacked Bar/Column Charts: Show the composition of different categories within a single bar/column.

Clustered Bar/Column Charts: Display multiple series of data for comparison.

100% Stacked Bar/Column Charts: Illustrate the percentage composition of each category.

Limitations:

  • Space Constraints: It can become cluttered with too many categories.
  • Limited Detail: This may not effectively show minor variations or detailed trends.
  • Overlapping Issues: Overlapping bars or columns can make interpretation difficult.
Bar Chart
Column Chart

2. Line Charts

Line charts are essential for visualizing data trends over a continuous period. These Power BI Charts connect data points with lines, making it easy to see changes over intervals such as days, months, or years.

Key Features:

  • Trend Visualization: Excellent for showing trends over time.
  • Multiple Series: Can display various lines to compare different data series.
  • Continuous Data: Suitable for continuous datasets.

Types:

  • Simple Line Charts: Show a single data series.
  • Multi-Line Charts: Display multiple data series for comparison.
  • Stepped Line Charts: Represent data points with steps instead of straight lines.

Limitations:

  • Overlapping Lines: Multiple lines can overlap, making it difficult to distinguish between series.
  • Data Density: Dense data points can make the chart cluttered.
  • Time Series Focus: Primarily useful for time series data, less so for categorical data.
Line Chart

3. Pie and Donut Charts

Pie and donut charts represent data as circle segments, illustrating parts of a whole. Specifically, pie charts in Power BI are best for showing proportions and percentages in a single data series. While donut charts can handle multiple data series.

Key Features:

  • Proportion Visualization: Ideal for showing parts of a whole.
  • Intuitive and straightforward: Easy to understand at a glance.
  • Multiple Data Series: Donut charts can represent various data series.

Types:

  • Standard Pie Charts: Display a single series of data.
  • 3D Pie Charts: Add a three-dimensional effect to standard pie charts.
  • Donut Charts: Similar to pie charts but with a blank center to represent multiple series.

Limitations:

  • Limited Categories: Not suitable for datasets with many categories.
  • Comparative Difficulty: It is hard to compare slices accurately.
  • Overemphasis: Can overemphasize minor differences in data.
Pie Chart

4. Area Charts

Area charts are similar to line charts, but the area under the line is filled with color. This Power BI Chart helps display cumulative totals over time and compare categories.

Key Features:

  • Cumulative Display: Shows cumulative totals over time.
  • Magnitude Emphasis: Emphasizes the magnitude of change.
  • Stacked Format: This can be stacked to show multiple data series.

Types:

  • Standard Area Charts: Display a single data series with filled areas.
  • Stacked Area Charts: Show multiple data series stacked on each other.
  • 100% Stacked Area Charts: Display the percentage composition of each category.

Limitations:

  • Overlapping Areas: This can make it difficult to distinguish between series.
  • Data Clutter: Dense data points can make the chart cluttered.
  • Focus on Magnitude: Less effective for showing exact values.
Area Chart

5. Scatter and Bubble Charts

Scatter charts plot individual data points on the x and y axes to show relationships between variables. Bubble charts add a third dimension by using the size of the bubbles to represent another variable.

Key Features:

  • Correlation Identification: Effective for identifying correlations and relationships.
  • Outlier Detection: Can easily spot outliers in the data.
  • Multi-Dimensional Analysis: Bubble charts add a third dimension for deeper analysis.

Types:

  • Simple Scatter Charts: Plot data points on the x and y axes.
  • Bubble Charts: Use bubble size to represent an additional variable.
  • Matrix Scatter Charts: Display multiple data series in a matrix layout.

Limitations:

  • Complex Interpretation: It can be complex to interpret many data points.
  • Overlapping Points: Overlapping data points can obscure information.
  • Requires Understanding: Users need a good understanding of the data to draw meaningful insights.
Scatter Plot
Bubble Plot

6. TreeMap

TreeMaps display hierarchical data as nested rectangles, with each branch of the hierarchy represented by a rectangle. The size of each rectangle is proportional to its data value.

Key Features:

  • Hierarchical Representation: Shows hierarchical data effectively.
  • Proportional Sizing: Rectangle size is proportional to the data value.
  • Pattern Recognition: Useful for spotting patterns and anomalies within the hierarchy.
  • Color Coding: Can use different colors to represent various categories or values.

Types:

  • Standard TreeMap: Displays a single layer of hierarchical data.
  • Multi-Layer TreeMap: Can represent multiple layers of hierarchy by nesting rectangles.

Limitations:

  • Limited Detail: Small rectangles can be hard to interpret.
  • Complexity: Can become cluttered with massive datasets.
  • Static Hierarchy: Not ideal for datasets that require frequent reorganization.
Treemap

7. Waterfall Charts

Waterfall charts show the cumulative effect of sequential positive and negative values. They help understand the cumulative impact on a particular metric, such as profits and losses.

Key Features:

  • Sequential Analysis: Displays the cumulative effect of sequential values.
  • Intermediate Values: Shows how individual values contribute to the overall total.
  • Clear Visualization: Highlights increases and decreases clearly.

Types:

  • Standard Waterfall Chart: Displays positive and negative values sequentially.
  • Stacked Waterfall Chart: Shows multiple categories within each step.

Limitations:

  • Limited Use Cases: Best suited for specific scenarios like financial analysis.
  • Complex Interpretation: This can be complex if there are too many steps.
  • Data Requirement: Requires a precise sequence of positive and negative values.
Waterfall Chart

8. Funnel Charts

Funnel charts are perfect for visualizing stages in a process, such as a sales pipeline. This Power BI chart show values as progressively decreasing proportions, helping to identify bottlenecks or drop-off points in a process.

Key Features:

  • Process Visualization: Ideal for visualizing stages in a process.
  • Proportional Representation: Shows values as decreasing proportions.
  • Bottleneck Identification: Helps identify drop-off points or bottlenecks.

Types:

  • Standard Funnel Chart: Displays a simple funnel with progressively decreasing stages.
  • Segmented Funnel Chart: Shows segments within each stage for more detail.

Limitations:

  • Limited Detail: This does not provide detailed insights into individual stages.
  • Fixed Shape: The funnel shape may not accurately represent all types of processes.
  • Data Requirement: Requires an apparent sequential process.
Funnel Chart

9. Gauge Charts

Gauge charts, also known as speedometer charts, display a single value within a range. These charts measure performance against a target, such as key performance indicators (KPIs).

Key Features:

  • Performance Measurement: How well a metric performs relative to its goal.
  • Quick Insights: Provides a quick visual representation of performance.
  • Target Indicators: Target markers can be included for easy reference.

Types:

  • Simple Gauge Chart: Displays a single value within a range.
  • Multi-Needle Gauge Chart: Shows multiple needles to compare different values.

Limitations:

  • Limited Data Representation: Only displays a single value.
  • Over-Simplification: May oversimplify complex data.
  • Space Requirement: It can take up a lot of space for a single metric.

10. Maps

Power BI offers several map visualizations, including filled, bubble, and shape maps. These Power BI charts represent data geographically, providing insights into regional patterns and trends.

Key Features:

  • Geographical Representation: Visualizes data based on geographic locations.
  • Multiple Map Types: Includes filled, bubble, and shape maps.
  • Insightful Patterns: Helps uncover regional trends and patterns.

Types:

  • Filled Maps: Color regions based on data values.
  • Bubble Maps: Place bubbles of varying sizes on a map to represent data values.
  • Shape Maps: Use predefined shapes to display data.

Limitations:

  • Geographical Limitations: Requires accurate geographic data.
  • Complex Data Handling: This can be complex to set up and interpret large datasets.
  • Performance Issues: You may have performance issues with massive datasets.
Map plot

How to Choose the Best Power BI Chart?

Choosing the best Power BI chart depends on your data type and the insights you wish to derive. Here are some guidelines:

Understand Your Data

  • Categorical Data: Data divided into categories (e.g., sales by region).
  • Time-Series Data: Data collected over time (e.g., monthly revenue).
  • Hierarchical Data: Data with multiple levels (e.g., organizational structure).
  • Geographical Data: Data linked to geographical locations (e.g., sales by country).

Define Your Purpose

  • Comparison: Use bar, column, or line charts to compare different categories or time periods.
  • Trend Analysis: Line charts or area charts are suitable for showing trends over time.
  • Distribution: Use histograms or scatter plots to show data distribution.
  • Proportion: Pie charts and donut charts effectively show the proportions of a whole.
  • Relationship: Scatter plots or bubble charts show relationships between variables.
  • Hierarchy: TreeMaps or sunburst charts are ideal for hierarchical data.

Consider Your Audience

  • Simplicity: Choose simple charts (e.g., bar or line charts) for a general audience.
  • Detail: For a technical audience familiar with data analysis, use more complex charts (e.g., scatter plots, and tree maps).

Evaluate Chart Features

  • Interactivity: Ensure the chart allows for interactive features like drilling down, filtering, and highlighting.
  • Scalability: Choose charts that can handle the size and complexity of your data.
  • Customization: Look for charts with customization options to fit your needs.

Conclusion

Data visualisation through chartsPower BI Charts
Yana Khare
Yana Khare 12 Aug, 2024

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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