Domain Track Title : Data Analytics - Visualisation

Code (Track Total Credits): DACU2200 ( 0-14-6)

Courses Division:

 

Course-1 (Code: CUDA2200): STORY TELLING USING VISUALISATION AND DATA REQUIREMENTS (0-2-0)

 

Course-2 (Code:CUDA2201): PYTHON LIBRARIES: PANDAS (0-4-0)

 

Course-3 (Code: CUDA2202): VISUALISATION WITH MATPLOTLIB, PLOTLY AND SEABORN (0-3-0)

 

Course-4 (Code: CUDA2203): INTERACTIVE DATA VISUALISATION WITH BOKEH (0-2-2)

 

Course-5 (Code: CUDA2204): INFOGRAPHICS AND DASHBOARD (0-3-0)

 

Course-6 (Code: CUDA2205): PROJECT (0-0-4)

Domain Track Objectives:

1. How to tell a story from data
2. How to marshal the data for the story line
3. The ability to develop visualisation to tell the story
4. The focus is on analysis of data using visualisation as a tool

Domain Track Learning Outcomes:

COURSE OUTCOMES:

CO1: Create impactful visualisation

PO5, PO10

Session Plan for the Entire Domain:

 

1.  STORY TELLING USING VISUALISATION AND DATA REQUIREMENTS (0-2-0) 25 hrs

 

Session 1 & 2: Story board development- Objective finalisation and flow of the story

Why a story board?

https://www.youtube.com/watch?v=G0HCqSe__3Q

https://www.slideshare.net/mobile/AmandaMakulec/storyboarding-for-data-visualization

Create an Interactive Slide Deck in PowerPoint

https://www.youtube.com/watch?v=waSuDm0CXpk

Impactful visualisation: Hans Rosling's 200 Countries, 200 Years, 4 Minutes - The Joy of Stats - BBC Four

https://www.youtube.com/watch?time_continue=15&v=jbkSRLYSojo&feature=emb_logo

 

Session 3 & 4: Data Requirements, Sources of data, Collection of data

https://serc.carleton.edu/NICHE/resources_data_analysis.html

 

Session 5 & 6: Working with Python Libraries - Setting up Environment and Python Libraries

Anaconda Tutorial 2019 - Python Virtual Environment Manager-

https://www.youtube.com/watch?v=mIB7IZFCE_k

How to Download & Install Python 3.8.2 on Windows 10/8/7

https://www.youtube.com/watch?v=O5nHFBhCfFo

 

Session 7 & 8: Working with Python Libraries - Pandas, Numpy, Matplotlib, Seaborn, Plotly

https://www.edureka.co/blog/python-pandas-tutorial/

https://www.edureka.co/blog/install-numpy/

https://www.edureka.co/blog/python-numpy-tutorial/

 

Session 9 & 10: Working with Python Libraries - Pandas, Numpy, Matplotlib, Seaborn, Plotly

https://www.edureka.co/blog/python-matplotlib-tutorial/

https://www.edureka.co/blog/python-seaborn-tutorial/

https://medium.com/swlh/plotly-beautiful-data-visualization-made-easy-3f7e48864706

 

Session 11 & 12: Project -1

Take up a project in groups of two students- Finalise the objective and flow of the story board

 

Session 13 & 14: Project -1

Take up a project in groups of two students- Finalise the  flow of the story board

How to create a story board-  https://youtu.be/eSGkeXsaXSY

https://www.dummies.com/programming/big-data/big-data-visualization/data-visualization-choosing-a-color-scheme-without-branding-guidelines/

 

Session 15 & 16: Presentation on Project

Students presentation in groups on the objective of the project selected

 

2. PYTHON LIBRARIES: PANDAS  (0-4-0) 50 hrs

 

Session 1 & 2: Review of pandas DataFrames – Introduction

https://campus.datacamp.com/courses/pandas-foundations/data-ingestion-inspection?ex=1

 

Session 3 & 4: Inspecting your data, DataFrame data types

https://www.shanelynn.ie/using-pandas-dataframe-creating-editing-viewing-data-in-python/

https://www.geeksforgeeks.org/python-basics-of-pandas-using-iris-dataset/

 

Session 5 & 6: NumPy and pandas working together- Building DataFrames from scratch, Zip lists to build a DataFrame, Labeling your data, Building DataFrames with broadcasting

https://towardsdatascience.com/numpy-essentials-for-data-science-25dc39fae39

 

Session 7 & 8: Importing & exporting data, Reading a flat file, Delimiters, headers, and extensions

https://datatofish.com/import-csv-file-python-using-pandas/

https://medium.com/@kasiarachuta/importing-and-exporting-csv-files-in-python-7fa6e4d9f408

https://www.pluralsight.com/guides/importing-data-from-excel-with-python

 

Session 9 & 10: Plotting with pandas- Plotting series using pandas, Plotting DataFrames, Extracting and transforming data

https://www.datacamp.com/courses/manipulating-dataframes-with-pandas

https://towardsdatascience.com/data-visualization-using-matplotlib-16f1aae5ce70

 

Session 11 & 12: Plotting with pandas- Plotting series using pandas, Plotting DataFrames, Extracting and transforming data

https://sergilehkyi.com/extracting-and-transforming-data-in-python/

 

 

Session 13 & 14: Indexing DataFrames, Index ordering, Positional and labeled indexing, Indexing and column rearrangement

https://www.youtube.com/watch?v=B7M1Ni_ngc4

Reindexing dataframes - https://www.youtube.com/watch?v=zg2b4GQd2NA

 

Session 15 & 16: Slicing DataFrames, Slicing rows, Slicing columns

https://www.youtube.com/watch?v=2vFu4YtzfgE

 

Session 17 & 18: Subselecting DataFrames with lists, Filtering DataFrames, Thresholding data, Filtering columns using other columns, Filtering using NaNs, Transforming DataFrames

https://campus.datacamp.com/courses/intermediate-python/logic-control-flow-and-filtering?ex=14

 

Session 19 & 20: Pivoting Dataframes

https://campus.datacamp.com/courses/manipulating-dataframes-with-pandas/rearranging-and-reshaping-data?ex=1

 

Session 21 & 22: Pivoting and the index, Pivoting a single variable, Pivoting all variables, Stacking & unstacking DataFrames, Stacking & unstacking I, Stacking & unstacking II

https://www.youtube.com/watch?v=xPPs59pn6qU

https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html

Stacking and Unstacking- https://www.youtube.com/watch?v=BUOy4RUUepg

 

Session 23 & 24: Restoring the index order, Melting DataFrames, Adding names for readability

https://pandas.pydata.org/pandas-docs/version/0.15.2/reshaping.html

 

Session 25 & 26: Pivot tables -

https://campus.datacamp.com/courses/manipulating-dataframes-with-pandas/rearranging-and-reshaping-data?ex=13

 

Session 27 & 28:  Setting up a pivot table, Using other aggregations in pivot tables, Using margins in pivot tables, learn how to interact with and extract data from them.

 

Session 29 & 30: Index objects and labeled data

https://campus.datacamp.com/courses/manipulating-dataframes-with-pandas/advanced-indexing?ex=1

 

Session 31 & 32:  Index values and names, Changing index of a DataFrame, Changing index name labels

https://www.youtube.com/watch?v=zg2b4GQd2NA

 

Session 33 & 34: Building an index, then a DataFrame, Hierarchical indexing, Extracting data with a MultiIndex, Setting & sorting a MultiIndex

Hierarchical indexing - https://www.youtube.com/watch?v=nE21ZlXiByY

https://www.datacamp.com/community/tutorials/pandas-multi-index

Sorting a Multi Index - https://www.youtube.com/watch?v=eu_pS_NWGD0

 

Session 35 & 36: Categoricals and groupby

https://campus.datacamp.com/courses/manipulating-dataframes-with-pandas/grouping-data?ex=1

 

Session 37 & 38: Advantages of categorical data types, Grouping by multiple columns, Grouping by another series

https://www.youtube.com/watch?v=Wb2Tp35dZ-I

 

Session 39 & 40: Groupby and aggregation, Computing multiple aggregates of multiple columns, Aggregating on index levels/fields, Grouping on a function of the index, Groupby and transformation

https://www.youtube.com/watch?v=u9nftlpCdXQ

 

Session 41 & 42: Detecting outliers, Filling missing data (imputation) by group, Groupby and filtering

https://www.youtube.com/watch?v=2Qrost474lQ

https://www.youtube.com/watch?v=EgSM6gA1Qjg

working with missing data- https://www.youtube.com/watch?v=_bl9fogd05c

 

Session 43 & 44: Indexing time series – Reading and slicing times

https://campus.datacamp.com/courses/pandas-foundations/time-series-in-pandas?ex=1

 

Session 45 & 46: Creating and using a Datetime Index - Partial string indexing and slicing, Reindexing the Index

https://www.youtube.com/watch?v=lMQRjpgJslI

 

Session 47 & 48: Resampling time series data, Resampling and frequency, Separating and resampling, Rolling mean and frequency, Resample and roll with it

https://www.youtube.com/watch?v=r0s4slGHwzE

https://www.youtube.com/watch?v=RzycSAnfW5M

 

Session 49 & 50: Manipulating time series data - Method chaining and filtering, Missing values and interpolation

https://www.youtube.com/watch?v=EaGbS7eWSs0&t=1s

 

Session 51 & 52: Time zones and conversion

https://www.youtube.com/watch?v=mHezNgNBnuA

https://www.youtube.com/watch?v=yCgJGsg0Xa4

 

Session 53 & 54 : Presentation by students

Case Study - Sunlight in Austin  https://www.kaggle.com/niyatijasani/sunlight-in-austin-texas-over-the-years

Reading and cleaning the data –

https://campus.datacamp.com/courses/pandas-foundations/case-study-sunlight-in-austin?ex=1

STEPS

• • • • What method should we use to read the data?
      • Reading in a data file
      • Re-assigning column names
      • Cleaning and tidying datetime data
      • Cleaning the numeric columns
      • Statistical exploratory data analysis
      • Signal min, max, median
      • Signal variance
      • Sunny or cloudy
      • Visual exploratory data analysis
      • Weekly average temperature and visibility
      • Daily hours of clear sky
      • Heat or humidity
      • Probability of high temperatures
      • Final Discussions

3. VISUALISATION WITH MATPLOTLIB AND SEABORN (0-3-0)

 

Session 1 & 2: Plotting multiple types of graphs

https://www.youtube.com/watch?v=aS4WlOJQ4mQ

 

Session 3 & 4: Multiple plots on single axis, Using Axes, using subplots, customising axes, using x lim, using y lim, using axis

https://www.youtube.com/watch?v=6r5JZjokp-0

 

Session 5 &6: Using legends, annotations and modifying styles

https://www.youtube.com/watch?v=oETDriX9n1w

 

Session 7 & 8: Visualising Regressions- Simple Linear regression, Higher order regression

https://www.youtube.com/watch?v=7NW4EouQDes

https://365datascience.com/linear-regression/

 

Session 9 & 10: Grouping Linear Regressions – by hue, by row or column

https://datascience103579984.wordpress.com/2019/08/11/14-introduction-to-data-visualization-with-python-datacamp/

 

Session 11 & 12: Visualising univariate distributions – strip plot, Swarm plot, Violin Plot

https://www.youtube.com/watch?v=10UMeXWj0YE

https://www.youtube.com/watch?v=cLHwwRgny5g

 

Session 13 & 14: Visualising multivariate distributions – plotting joint distributions, plotting distributions pairwise

https://www.youtube.com/watch?v=i6iUCs0D9nk

https://www.youtube.com/watch?v=ap4mfGvgDsM&t=43s

 

Session 15 & 16: Visualising correlation with heatmaps

https://www.youtube.com/watch?v=m7uXFyPN2Sk

 

Session 17 & 18: Visualising time series, Multiple time series on common axes, multiple time series slices

https://www.youtube.com/watch?v=p1FQa0SUKnk

 

Session 19 & 20: Timeseries with moving windows

https://www.youtube.com/watch?v=0fWa9-Vj89g

 

Session 21 & 22: Plotting moving averages, plotting moving standard deviations, interpreting moving statistics

https://www.youtube.com/watch?v=T2mQiesnx8s

https://www.youtube.com/watch?v=OwnaUVt6VVE

 

Session 23 & 24: Time series visualization- Plotting time series, datetime indexing, Plotting date ranges, partial indexing

https://www.youtube.com/watch?v=yCgJGsg0Xa4

 

Session 25 & 26: Web scraping libraries

https://www.youtube.com/watch?v=hWUqPUJWp3k

 

Session 27 & 28:  Project work- Web scrapping of Flipkart website

https://www.youtube.com/watch?v=auDx0aVWn-s

 

Session 29 & 30: Presentation of Project work

 

STEPS:

• • • Objective of the project
    • Data Requirements
    • Source of data
    • Collection of data
    • Arranging data
    • Plotting graphs and charts
    • Interpretation & Discussion

 

4. INTERACTIVE DATA VISUALIZATION WITH BOKEH (0-2-2)

 

Session 1 & 2: Plotting with glyphs – simple scatter plot, scatter plot with different shapes, Customising scatter plots

 Python Tutorial: Plotting with glyphs-  https://www.youtube.com/watch?v=vGX0TtzrWE8

https://www.youtube.com/watch?v=q64iiFepBcU

 

Session 3 & 4: Additional glyphs, Lines, lines and markers, patches, data formats

https://www.youtube.com/watch?v=eeC_kYxVlgg

 

Session 5 & 6: Plotting data from NumPy arrays

https://www.youtube.com/watch?v=X-ptGOFwckA

 

Session 7 & 8: Plotting data from Pandas dataframes

https://www.youtube.com/watch?v=qV_8bMsRq1s

 

Session 9 & 10: Bokeh column data source

https://www.youtube.com/watch?v=TyMZUVRiju0

 

Session 11 & 12: Customising glyphs – selection and non-selection glyphs, Hover glyphs

Python Tutorial: Customising glyphs-  https://www.youtube.com/watch?v=q64iiFepBcU

 

Session 13 & 14: Color mapping

https://www.youtube.com/watch?v=hA39KSTb3dY&t=209s

 

Session 15 & 16: Introduction to layouts, creating rows of plot, creating columns of plot, Nesting rows and columns

https://www.youtube.com/watch?v=Y_UaawEr-wY

 

Session 17 & 18: Advanced layout, creating gridded layout, displaying tabbed layout

https://docs.bokeh.org/en/latest/docs/user_guide/layout.html

 

Session 19 & 20:  Linking plots together, linked axes, linked brushing

https://www.youtube.com/watch?v=Ceg3EIPbOw8

 

Session 21 & 22: Annotations and guides- create legends, positioning and styling legends

https://www.youtube.com/watch?v=NwnW_qOkNRw

 

Session 23 & 24: Adding Hover tooltips for showing details

https://www.youtube.com/watch?v=6APMRxGIAUw

 

Session 25 & 26: Introducing Bokeh servers, understanding Bokeh apps

https://www.youtube.com/watch?v=1DZcPat2ClM

 

Session 27 & 28: Using the current document- add single slider, add multiple slider in one document

https://github.com/wblakecannon/DataCamp/blob/master/16-interactive-data-visualization-with-bokeh/03-building-interactive-apps-with-bokeh/03-multiple-sliders-in-one-document.py

 

Session 29 & 30: Connecting sliders to plots, adding call backs to sliders

https://docs.bokeh.org/en/latest/docs/gallery/slider.html

 

Session 31 & 32: Combine Bokeh models to layouts – Learn widget callbacks, updating plot from dropdown, updating data sources from dropdown callbacks, synchronise two dropdowns

https://github.com/wblakecannon/DataCamp/blob/master/16-interactive-data-visualization-with-bokeh/03-building-interactive-apps-with-bokeh/04-how-to-combine-bokeh-models-into-layouts.py

 

Session 33 & 34: Button – Button widgets, Button styles

https://www.python-course.eu/tkinter_buttons.php

https://www.tutorialspoint.com/python/tk_button.htm

 

Session 35 & 36: Hosting applications for wider audience

https://campus.datacamp.com/courses/interactive-data-visualization-with-bokeh/building-interactive-apps-with-bokeh-3?ex=16

 

Session 37 & 38: Apply all above learnings to a case study- (Sunlight in Austin)

 

Session 39 & 40 : Apply all above learnings to a case study- (Sunlight in Austin)

 

Session 41 & 42: Presentation of Case study

      STEPS

• Introducing the project dataset
• Some exploratory plots of the data
• Starting the app
• Beginning with a plot
• Enhancing the plot with some shading
• Adding a slider to vary the year
• Customising based on user feedback
• Adding interactivity to the app
• Adding Hover tool
• Adding dropdowns to the app

 

5. INFOGRAPHICS AND DASHBOARD ( 0-3-0)

 

Session 1 & 2: Interactive Python Dashboards with Plotly

https://www.datacamp.com/community/tutorials/learn-build-dash-python

 

Session 3 & 4: Plotly Charts - Bar Charts, Line Charts

https://www.youtube.com/watch?v=QJPN2J_KGXI&list=PLH6mU1kedUy8fCzkTTJlwsf2EnV_UvOV-&index=3

https://www.youtube.com/watch?v=hCRo_AXzZkU&list=PLH6mU1kedUy8fCzkTTJlwsf2EnV_UvOV-&index=5

 

Session 5 & 6: Scatter Plots, Heat Maps

https://www.youtube.com/watch?v=LtmdwkprcEk&list=PLH6mU1kedUy8fCzkTTJlwsf2EnV_UvOV-&index=7

 

Session 7 & 8: Dashboard Layouts

https://kyso.io/KyleOS/creating-an-interactive-application-using-plotlys-dash?utm_campaign=News&utm_medium=Community&utm_source=DataCamp.com

 

Session 9 & 10: Converting Simple Plotly Plot to Dashboard with Dash

https://www.youtube.com/watch?v=rDodciQhfS8

 

Session 11 & 12: Dash Components - HTML Components, Core Components

https://dash.plotly.com/dash-html-components

https://dash.plotly.com/dash-core-components

 

Session 13 & 14: Markdown with Dash

https://www.youtube.com/watch?v=4Kqvh__ofGE&list=PLH6mU1kedUy8fCzkTTJlwsf2EnV_UvOV-&index=20

 

Session 15 & 16: Using Help() with Dash

https://www.youtube.com/watch?v=Ldp3RmUxtOQ&list=PLCDERj-IUIFCaELQ2i7AwgD2M6Xvc4Slf

 

Session 17 & 18: Interactive components

https://www.youtube.com/watch?v=Ywf4I0qQs58&list=PLCDERj-IUIFCaELQ2i7AwgD2M6Xvc4Slf&index=6

 

Session 19 & 20: Single Callbacks for Interactivity, Dash Callbacks for Graphs

https://www.youtube.com/watch?v=LYcEB5cak7U&list=PLCDERj-IUIFCaELQ2i7AwgD2M6Xvc4Slf&index=5

 

Session 21 & 22: Multiple Inputs, Multiple Outputs

https://www.youtube.com/watch?v=GCGjovcU09E

 

Session 23 & 24: Call backs with State

https://www.youtube.com/watch?v=LYcEB5cak7U

 

Session 25 & 26: Interacting with Visualisation – Hover over data, click data

https://dash.plotly.com/interactive-graphing

 

Session 27 & 28: Updating graphs on interaction

https://community.plotly.com/t/update-graph-as-automatically-as-manually-with-user-interactions/6622

 

Session 29 & 30: Live Updating – Layout updating, Simple live update

https://dash.plotly.com/live-updates

 

Session 31 & 32: Deployment to website- App authorisation, Deploying App to Heroku

https://www.youtube.com/watch?v=MxfxiR8TVNU

 

Session 33, 34, 35 & 36: Will apply all learning on Project - Either Sunlight Austin case study or Flipkart case study

 

6. PROJECT (0-0-4)

Students will do Story telling by analysing data using Visualisation

List of Projects/ papers/jobs/products to be done in domain:

1. Visualisation of COVID 19
2. Visualisations related to World Development Indicators,
3. ERP dashboarding
4. Visualisations to show the details of Social/ Empowerment schemes of Govt. etc
5. Visualisations on Sports data (World Cup/ IPL)
6. Visualisations of NIRF Rankings

Process to be followed

1. Story Boarding -Topic finalisation, Objective finalisation, flow of the study to be finalised
2. Collect data required and compile the data
3. Start visualisation ann make interim presentation
4. Complete the story board with visuals and analysis
5. Final Presentation