Data Visualization Using Python PDF

BOS Computer Science Class S.Y.B.Sc.C.S Semester III Course Name Data Visualization using Python Course Code PUIDC308 Level of the Subject Medium Credit points 2 Course...

BOS Computer Science Class S.Y.B.Sc.C.S Semester III Course Name Data Visualization using Python Course Code PUIDC308 Level of the Subject Medium Credit points 2 Course Objectives: 1. To expose students to visual representation methods and techniques that increase the understanding of complex data 2. To introduce students to Python packages that will allow them to create easy to read and understand graphs, charts and other visual representations of data using Python. Unit Name of Unit Topic Content No. No. Hours 1 Introduction to 1.1 Introduction: Data Visualization and 10 Data Its Importance, Need of Data Visualization Visualization in Businesses, Future of and Python Data Visualization, Use of Data libraries for Data Visualization in Business Decision Visualization Making 1.2 Data Visualization Techniques: Loading libraries, Popular libraries for data visualization in python, introduction to plots in python, Types of Data required for plot, Installing python libraries. 1.3 Defining plot types : bar, line and stacked charts, Drawing a simple sine and cosine plot, Defining axis lengths and limits, Defining plot line styles, properties and format strings 2 Drawing 2.1 10 Customizing plots: Setting ticks, labels, Plots & and grids, Adding a legend and Customizing them annotations, Moving spines to the center, Setting the transparency and  size of axis labels. 2.2 Making bar charts with error bars, Making pie charts count, Plotting with filled areas, Drawing scatter plots with colored markers 2.3 Advanced Customization : Adding a shadow to the chart line, Adding a data table to the figure, Using subplots, Customizing grids, Creating contour plots, Timelines 3 Matplotlib , 3.1 Matplotlib: Line Plot, Bar Plot, 10 Seaborn Scatter plot, Histogram plot, Stack Plotting and Plotly Plot, Pie chart Plotting. 3.2 Seaborn Plotting: Strip plot, Box Plot, Swarm plot, Joint plot,relational plot, HeatMap, Violin Plot, Facet_grid 3.3 Plotly Plotting: Gnatt Chart , Waterfall Chart , Funnel Chart 4 Making 3D 4.1 3D Visualization and Animations: 10 Visualization Creating 3D bars, Creating 3D and histograms, Animating in matplotlib Animations., Plotting 4.2 Plotting with images and maps: Charts with Plotting Data on a map using Basemap, Images and Plotting data on a map using Google Maps Map API, Generating Captchas 4.3 Animation with Plotly : Bubble Chart, Bar Charts, Adding Control Buttons to Animations, Race Bar Chart Total No. of Lectures 40  Course Outcomes: 1. Explain the need of Data Visualization and the use of Python 2. Describe plotting of data using graphs and charts 3. Create 3D Visualizations , animations and generate Captchas 4. Analyze data and use appropriate graphs and charts 5. Apply different customization techniques to the graphs to make data more meaningful 6. Compare different plotting techniques References: 1. Dr. Ossama Embarak, “Data Analysis and Visualization using Python”, APress 2. Igor Milovanović , Dimitry Foures , Giuseppe Vettigli, “Python Data Visualization 3. Cookbook”, Packt Publishing 4. Healy, K. (2019). Data Visualization: A Practical Introduction. Princeton University Press. 5. Kandel, S., Heer, J., Plaisant, C., & Kennedy, J. (2012). Designing Interactive Visualization Tools to Support Creativity and Insight. ACM. 6. Murray, S. (2017). Interactive Data Visualization for the Web: An Introduction to Designing with D3. O'Reilly Media. Practicals Practical Details No. 1 Plot a Simple histogram and bar plot and apply various customization techniques. 2 Create a simple plot and add ticks, labels, axes 3 Plot Strip plot, Box Plot, Swarm plot, Joint plot, on Tips dataset. 4 Plot relational plot, HeatMap, Violin Plot, Facet_grid on Tips dataset. 5 To add legends and annotations to the graph 6 Create an exploded pie chart and stack plot. 7 Create a TimeLine on Date time column from Sample Superstore dataset. 8 Create a 3D bar for a sample data 9 Demonstrate some matplotlib and Plotly animations with Bar Race Chart and Bubble chart. 10 To add an annotation to a chart using images and text  11 To plot data on a map using GoogleMap API To create a simple Captcha Generator 12 Use the Plotly Library to show the use of Waterfall and Gnatt charts. Total No. of Lectures 20