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For Batch - 2017 to 2021 & 2018 to 2022
Note: Only one course has to be opted from course-4 & course-5.
For Batch - 2019 to 2023
Note: Only one course has to be opted from course-2 & course-3.
For Batch - 2023 to 2027
Note: Only one course has to be opted from course-2 & course-4.
Course Outcomes:
COs | Course outcomes |
CO1 | Students will gain comprehensive knowledge of predictive analytics, including key concepts, methodologies, and applications. |
CO2 | Students will develop strong analytical skills and the ability to critically assess and interpret data for predictive modeling. |
CO3 | Students will enhance their problem-solving skills and make informed decisions based on predictive analysis results. |
CO4 | Students will gain hands-on experience with various machine learning tools and techniques, designing and developing predictive models. |
CO5 | Students will be able to conduct research in predictive analytics, utilizing advanced techniques and methodologies to solve complex problems. |
3.1 Introduction to Predictive Analytics:
3.2 Data Preprocessing:
3.3 Supervised Learning Algorithms:
3.4 Model Evaluation and Selection:
3.5 Advanced Machine Learning Techniques:
3.6 Unsupervised Learning:
3.7 Model Deployment:
Text Books:
Course 4: Deep Learning for Image Analytics (0+2+2)
Project/Task: (Choose one among six Tasks)
Concept Required:
4.1 Image Pre-processing:-
4.2 Image Feature Extraction: -
4.3 Creation of Feature Matrix by combining Extracted Features: -
4.4 Classification algorithms: -
Course Outcomes:
COs | Course outcomes |
CO1 | Ability to connect various data sources to Tableau and import data for visualization. |
CO2 | Proficiency in creating a wide range of visualizations including bar charts, scatter plots, and maps using Tableau. |
CO3 | Skill in applying advanced visualization techniques such as heat maps, tree maps, and histograms to analyze complex data sets. |
CO4 | Competence in utilizing Tableau calculations including calculated fields, table calculations, and Level of Detail (LOD) expressions for custom data analysis. |
CO5 | Capability to design interactive dashboards and stories in Tableau that effectively communicate insights and trends to stakeholders. |
Course 6: ML for Spectral Imaging (0+2+2)
Project/Task: (Choose one among four Tasks)
Concept Required:
5.1 Introduction to Remote Sensing: -
5.2 Scientific Principles:
5.2 Hyperspectral Concepts and System Trade-offs:-
5.3 HSI Data Processing Techniques:-
5.4 Classification Techniques:-
5.5 Clustering Techniques:-
Course 7: IoT Analytics (0-2-2)
7.1 Defining IoT Analytics and Challenges IoT Benefits of Deploying IoT End to End IoT architecture IoT challenges7.2 IoT Protocols 7.2.1 Wireless Protocol Connectivity Protocols (when Power is Limited) Bluetooth Low Energy (BLE) Zigbee LoRaWAN NFC 7.2.2 Connectivity Protocols (when Power is Not a problem) Wifi 7.2.3 Data Communication Protocol MQTT Web-Socket HTTP7.2 Sensors Types of Sensors based on communication-I2C, SPI Types of Sensors based on Application7.3 Overview of 32 -bit Controller ESP8266 ESP32 Raspberry Pi7.4 AWS IoT for Cloud AWS IoT Core services AWS IoT Analytics services AWS DynamoDB Services7.5 Thingspeak for IoT Getting and posting Data to IoT Cloud using ESP devices Posting Data to IoT Cloud using Raspberry Pi7.6 ThingWorx for Industrial IoT Building Dashboard on Thingworx platform Binding the senor value to the dashboardText Book:
Reference Books: 2. Geng, Hwaiyu, ed. Internet of things and data analytics handbook. John Wiley & Sons, 2017.
Course 8: Project (0-0-4)
Course 9: Internship (0-0-4)
Session Plan for the Entire Domain:
Data Analysis and Visualisation Using Python (0+1+3) 60 hrs
Course objective, outcome, methodology and assessment.
Why data visualisation
Story telling using Visuals & Infographics
https://venngage.com/blog/9-types-of-infographic-template/#1
https://www.edugrad.com/tutorials/learn-data-visualization-using-python/15
Tips on good visuals
https://statedashboard.odisha.gov.in/
https://www.youtube.com/watch?v=4pymfPHQ6SA
Project Groups:
Students will be divided into groups and assigned projects. Each group will do two projects.
Practice
Environmental setup - Anaconad and Jupyter notebook, Anaconda Navigator and Libraries Installation
Practice
Python Fundamentals, Use Case - Data Analysis, Exploring and learning assignments on Jupyter Notebook
https://towardsdatascience.com/data-visualization-say-it-with-charts-in-python-138c77973a56
https://towardsdatascience.com/plotting-with-python-c2561b8c0f1f
https://towardsdatascience.com/introduction-to-data-visualization-in-python-89a54c97fbed
Project - 1
For Project -1, the student group has to define the objective/s of the study, identify the data that will be needed and the source of such data
Make Presentations groupwise
Practice
Data collection/importing and reading using Python function of different types of files, i.e. CSV, HTML, Excel - Get CSV data files from source and read them, get HTML file and read, Get Excel sheet and read
https://perso.telecom-paristech.fr/eagan/class/igr204/datasets
https://www.youtube.com/watch?v=eWFwe41LyWk
https://towardsdatascience.com/wrangling-data-with-pandas-27ef828aff01
https://www.youtube.com/watch?v=ndwuUzgAiPY
https://www.youtube.com/watch?v=Ycq3sDg6ji0
Sorting data, Missing values & Munging data
https://www.youtube.com/watch?v=-dwjEfv2R50
https://www.youtube.com/watch?v=EaGbS7eWSs0
Project - 1
Data collection and sorting for the assigned project
Pandas Tutorial 1. What is Pandas python? Introduction and Installation- https://www.youtube.com/watch?v=CmorAWRsCAw
Pandas Tutorial2. Dataframe and Series Basics- Selecting row and column- https://www.youtube.com/watch?v=zmdjNSmRXF4
Pandas Tutorial 3: Different Ways Of Creating DataFrame - https://www.youtube.com/watch?v=3k0HbcUGErE
Python Pandas Tutorial 4: Read Write Excel CSV File- https://www.youtube.com/watch?v=-0NwrcZOKhQ
Importing data in python - Read excel file - https://www.youtube.com/watch?v=lco-r5CgvhY
Pandas Tutorial 8 | How to import HTML data in Python | Importing HTML data in Python - https://www.youtube.com/watch?v=ndwuUzgAiPY
Pandas Tutorial 13, Crosstabs - https://www.youtube.com/watch?v=I_kUj-MfYys
Practice
Basics of Numpy
https://www.youtube.com/watch?v=xECXZ3tyONo
Complete Python NumPy Tutorial (Creating Arrays, Indexing, Math, Statistics, Reshaping) - https://www.youtube.com/watch?v=GB9ByFAIAH4
Practice
Basic of Matplotlib
https://www.youtube.com/watch?v=MbKrSmoMads&pbjreload=10
Matplotlib Tutorial 1 - Introduction and Installation- https://www.youtube.com/watch?v=qqwf4Vuj8oM
Matplotlib Tutorial 2 - format strings in plot function - https://www.youtube.com/watch?v=zl5qPnqps8M
Matplotlib Tutorial 3 - Axes labels, Legend, Grid- https://www.youtube.com/watch?v=oETDriX9n1w
Matplotlib Tutorial 4 - Bar Chart - https://www.youtube.com/watch?v=iedmZlFxjfA
Matplotlib Tutorial 5 - Histograms - https://www.youtube.com/watch?v=r75BPh1uk38
Matplotlib Tutorial 6 - Pie Chart - https://www.youtube.com/watch?v=GOuUGWGUT14
Matplotlib Tutorial 7 - Save Chart To a File Using savefig - https://www.youtube.com/watch?v=XLJHkCn48lM
Plotting real-time data using Python - https://www.youtube.com/watch?v=GIywmJbGH-8
Project (work on Project -1)
Work on the projects assigned using Python Libraries
Pandas Tutorial 1. What is Pandas python? Introduction and Installation- https://www.youtube.com/watch?v=CmorAWRsCAw
Pandas Tutorial 3: Different Ways Of Creating DataFrame - https://www.youtube.com/watch?v=3k0HbcUGErE
Python Pandas Tutorial 4: Read Write Excel CSV File
https://www.youtube.com/watch?v=-0NwrcZOKhQ
Importing data in python - Read excel file - https://www.youtube.com/watch?v=lco-r5CgvhY
Pandas Tutorial 8 | How to import HTML data in Python | Importing HTML data in Python - https://www.youtube.com/watch?v=ndwuUzgAiPY
Pandas Tutorial 13, Crosstabs - https://www.youtube.com/watch?v=I_kUj-MfYys
Practice
Plotly
https://plotly.com/python/basic-charts/
Graphing Library - https://plotly.com/python/
Plotly Python - Plotly multi line chart| Plotly Python data visualization- https://www.youtube.com/watch?v=pfhBbJ2MnMI
Plotly Data Visualization in Python | Part 13 | how to create bar and line combo chart - https://www.youtube.com/watch?v=AQG4RQolUC8
Plotly Web based visualisation - https://www.youtube.com/watch?v=B911tZFuaOM
Project (Project-1)
Work on Project
Interim Presentation
Practice
Seaborn - Scatter Chart, Bubble Chart, Gapminder
https://www.youtube.com/watch?v=MGOcVAOuXxo
https://www.tutorialspoint.com/seaborn/seaborn_tutorial.pdf
Interactive Data Visualization - https://www.youtube.com/watch?v=VdWfB30QTYI
Practice
Web Scrapping
https://www.youtube.com/watch?v=mKxFfjNyj3c
Web scraping in Python (Part 4)_ Exporting a CSV with pandas - https://www.youtube.com/watch?v=Zh2fkZ-uzBU
Web scraping in Python (Part 2)_ Parsing HTML with Beautiful Soup - https://www.youtube.com/watch?v=zXif_9RVadI
Webscraping - Mode, Median, Mean, Range, and Standard Deviation- https://www.youtube.com/watch?v=mk8tOD0t8M0
Web Scraping Dynamic Graphs to CSV Files using Python - https://www.youtube.com/watch?v=NYK_1bVoBfU
Practice
Solve the 10 problem (started from session 7)
-students will submit the assignment (in groups)
(upload the assignment ...done by Prof. Ramana)
Practice
Dashboard Basics
https://www.youtube.com/watch?v=e4ti2fCpXMI
Create Presentation Slides from Jupyter - https://www.youtube.com/watch?v=utNl9f3gqYQ
Dash and Python 1_ Setup -https://www.youtube.com/watch?v=Ldp3RmUxtOQ
Dash and Python 2_ Dash Core Components - https://www.youtube.com/watch?v=NM8Ue4znLP8
Dash and Python 3_ Using CSS - https://www.youtube.com/watch?v=x9mUZZ19dl0
Practice
Dash (plotly) and Python
https://www.youtube.com/watch?v=Ldp3RmUxtOQ
Dash in 5 Minutes - https://www.youtube.com/watch?v=e4ti2fCpXMI
How to Create a Slideshow using Jupyter+Markdown+Reveal.js- https://www.youtube.com/watch?v=EOpcxy0RA1A
ipython dashboard - https://www.youtube.com/watch?v=LOWBEYDkn90
Plotting real-time data using Python - https://www.youtube.com/watch?v=GIywmJbGH-8
Practice
Interactive charts/Maps using Bokeh,
Dash board using Dash
https://www.youtube.com/watch?v=o4TB6LTPDaY
Practice
IPython-Dashboard
Live graphs
https://pypi.org/project/IPython-Dashboard/
https://pythonprogramming.net/live-graphs-data-visualization-application-dash-python-tutorial/
Project
Work on Project - 1 to make dash boards
Project
Final presentation of Project -1
Project -2
Start Project - 2 (ERP Dash Board)
Define the objective and prepare the flow chart
Project
Make presentations on the objective and flow chart of Project-2
Project
Work on Project - 2
Project
Make interim presentation on Project - 2
Project
Work on Project - 2
Project
Final Presentation on Project -2
Project
Make final changes on Project -1 & Project -2 to make it ready for External Evaluation
Machine Learning using Python (1+2+1) 56 hrs
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
ML for Predictive Analysis (0+2+2) 52 hrs
Lect. No | TOPIC |
1 | Tableau Tutorial for Beginners | What is Tableau? : https://www.youtube.com/watch?v=NLCzpPRCc7U&pp=ygUzbwlUYWJsZWF1IFR1dG9yaWFsIGZvciBCZWdpbm5lcnMgfCBXaGF0IGlzIFRhYmxlYXU_ |
Demo: Concept through PPT + Implementation | |
2 | Getting Started with Tableau | Tableau Software: https://www.youtube.com/watch?v=3GT_TLIeCbE&pp=ygUxbwlHZXR0aW5nIFN0YXJ0ZWQgd2l0aCBUYWJsZWF1IHwgVGFibGVhdSBTb2Z0d2FyZQ%3D%3D |
Demo: Concept through PPT + Implementation | |
3 | Connecting to Data Sources in Tableau: https://youtu.be/vRu_2nZXBPI?si=uv5j0f8syLQZP1b7 |
Demo: Concept through PPT + Implementation | |
4 | How to Connect Excel Files to Tableau: https://youtu.be/8pN5lK3-LxA?si=boMyWS_WkuNtgKa_ |
Demo: Concept through PPT + Implementation | |
5 | Tableau Data Connection Overview: https://youtu.be/XJqErDNTFRQ?si=GlGzBgmszrzBYbpA |
Demo: Concept through PPT + Implementation | |
Connecting Tableau to SQL Database: https://youtu.be/kuDgfwGTeuY?si=vgG7yNU7PPGKU5zR | |
Demo: Concept through PPT + Implementation | |
6 | Connecting to Multiple Data Sources in Tableau |
https://youtu.be/RavphFZTqTg?si=8P0ZsnYLQ1Y82cXa | |
Demo: Concept through PPT + Implementation | |
Data Blending in Tableau https://youtu.be/75HV2OziWGM?si=zF_mTAc0mmCH04Ot | |
Demo: Concept through PPT + Implementation | |
7 | Data Visualization Principles: Best Practices in Data Visualization, Types of Visualizations |
Data Visualization Best Practices | |
https://youtu.be/OVKbdxF2Czs?si=l_11BNm8tkKm10Xt | |
Demo: Concept through PPT + Implementation | |
Tips for Better Data Visualizations | |
https://youtu.be/t3cAUt7sOQg?si=BjbWsmhKpruzDGfm | |
Demo: Concept through PPT + Implementation | |
8 | Types of Data Visualizations |
https://youtu.be/csXmVBw8cdo?si=C1pmHDlnP9BMxav0 | |
Demo: Concept through PPT + Implementation | |
Choosing the Right Chart Type | |
https://youtu.be/C07k0euBpr8?si=Z6zUzAZA1fcSBvYW | |
Demo: Concept through PPT + Implementation | |
9 | Data Visualization with Tableau |
https://youtu.be/C7VemYmy3rA?si=fZ6uKPsVozy5p8yJ | |
Demo: Concept through PPT + Implementation | |
Tableau Best Practices for Dashboard Design | |
https://www.youtube.com/live/-VENXLh2Lh0?si=MO6g18r43rhwisyq | |
Demo: Concept through PPT + Implementation | |
10 | Data Visualization: Effective Design |
https://youtu.be/HuZiJ44_71M?si=oIVRwpexCKmdthd0 | |
Demo: Concept through PPT + Implementation | |
Tableau Visualization Types | |
https://youtu.be/J91tW6egjDw?si=gFDNJK_UCKrp_CTX | |
Demo: Concept through PPT + Implementation | |
11 | Creating Bar Charts in Tableau |
https://youtu.be/lyNYPMw-ANs?si=M4P5EA65-O0bbA6L | |
Demo: Concept through PPT + Implementation | |
How to Create Line Charts in Tableau | |
https://youtu.be/XNSB3COfIZU?si=VoL23Cs2AmwGGzOU | |
Demo: Concept through PPT + Implementation | |
12 | Tableau Pie Chart Tutorial |
https://youtu.be/dLhojoAuiEI?si=kggiTlOdtJPoBJI2 | |
Demo: Concept through PPT + Implementation | |
Creating Scatter Plots in Tableau | |
https://youtu.be/WNK0PlUlCRw?si=QowqMj94pcgSTI9J | |
Demo: Concept through PPT + Implementation | |
13 | Building Maps in Tableau |
https://youtu.be/r7eRXu9mHns?si=NqCLT8Y7YzSea9-J | |
Demo: Concept through PPT + Implementation | |
Tableau Map Tutorial for Beginners | |
https://youtu.be/fHe0jFgVPJU?si=5670lXYVduD6uheI | |
Demo: Concept through PPT + Implementation | |
14 | Customizing Bar Charts in Tableau |
https://youtu.be/x46lB4iPPcA?si=q6lbzxwaroIuF1P2 | |
Demo: Concept through PPT + Implementation | |
Line Chart Formatting in Tableau | |
https://youtu.be/XNSB3COfIZU?si=muotdviweYGBfVH2 | |
Demo: Concept through PPT + Implementation | |
15 | Creating Advanced Pie Charts in Tableau |
https://youtu.be/dLhojoAuiEI?si=0_AIQ1TSgOeDVJw5 | |
Demo: Concept through PPT + Implementation | |
Enhancing Scatter Plots in Tableau | |
https://youtu.be/yB3vg9uIZ-E?si=VPtnK07BO_aegqSA | |
Demo: Concept through PPT + Implementation | |
16 | Advanced Map Techniques in Tableau |
https://youtu.be/aKyxMDBPXn8?si=pi_-7e8OpefDGEi2 | |
Demo: Concept through PPT + Implementation | |
Creating Geo Maps in Tableau | |
https://youtu.be/fHe0jFgVPJU?si=cO8ZUQCgd3F_H70g | |
Demo: Concept through PPT + Implementation | |
17 | Creating Heat Maps in Tableau |
https://youtu.be/VVH0l1qytAg?si=54_UgJwWWpaPxBEr | |
Demo: Concept through PPT + Implementation | |
Advanced Heat Map Techniques | |
https://youtu.be/nHgLJ_SKkNQ?si=eVXW85-aLm2Vup00 | |
Demo: Concept through PPT + Implementation | |
18 | Creating Tree Maps in Tableau |
https://youtu.be/4Sx3VQg7LgI?si=vuOF_frCbRQPvpOk | |
Demo: Concept through PPT + Implementation | |
Advanced Tree Map Techniques | |
https://youtu.be/50vqQrid1mQ?si=_YcTXtPdXjBgOvwY | |
Demo: Concept through PPT + Implementation | |
19 | Creating Bullet Charts in Tableau |
https://youtu.be/zGpfxdArx-o?si=BBCb4Djx-Cq4EQ0r | |
Demo: Concept through PPT + Implementation | |
20 | Bullet Charts for KPI in Tableau |
https://youtu.be/1mx78l8Uw3E?si=8DVxs3Z5LV-52z0K | |
Demo: Concept through PPT + Implementation | |
21 | Creating Histograms in Tableau |
https://youtu.be/THJcN4qr00U?si=sNNh0jkYJqo9AqS2 | |
Demo: Concept through PPT + Implementation | |
22 | Advanced Histogram Techniques |
https://youtu.be/lUjZa4YBixI?si=WKk2YXCXslZoDn7k | |
Demo: Concept through PPT + Implementation | |
23 | Creating Box Plots in Tableau |
https://youtu.be/_NzllWQDuBY?si=8pH59vVLw123gNwJ | |
Demo: Concept through PPT + Implementation | |
24 | Advanced Box Plot Techniques |
https://youtu.be/nV8jR8M8C74?si=QzzLDR2HwGd499cF | |
Demo: Concept through PPT + Implementation | |
25 | Combining Advanced Visualizations in Tableau |
https://youtu.be/x46lB4iPPcA?si=Xtj7_GZMnk0vNwVR | |
Demo: Concept through PPT + Implementation | |
26 | Best Practices for Advanced Visualizations |
https://youtu.be/ZUeWXNK-9yA?si=fEelTOiMjMqNc6vr | |
Demo: Concept through PPT + Implementation | |
27 | Creating Calculated Fields in Tableau |
https://youtu.be/jp_enyU__4Y?si=Ra2X9uNIvcZLA50z | |
Demo: Concept through PPT + Implementation | |
28 | Advanced Calculated Fields Techniques |
https://youtu.be/b-K1jUY3jDs?si=mP-KjU9Asud2__KP | |
Demo: Concept through PPT + Implementation | |
29 | Introduction to Table Calculations in Tableau |
https://youtu.be/2f7Sl5WEExw?si=ofP-k3eU7Lqbtpb3 | |
Demo: Concept through PPT + Implementation | |
30 | Advanced Table Calculations in Tableau |
https://youtu.be/XPYtenDXCdI?si=3RDFFUXtSokC0Vrk | |
Demo: Concept through PPT + Implementation | |
31 | Using Level of Detail (LOD) Expressions in Tableau |
https://youtu.be/X-fMb2g0Oho?si=LE-P5b69ruOimYbd | |
Demo: Concept through PPT + Implementation | |
32 | Advanced LOD Expressions in Tableau |
https://youtu.be/X-fMb2g0Oho?si=oK2ZjkYtNBEVEhPy | |
Demo: Concept through PPT + Implementation | |
33 | Combining Calculated Fields and Table Calculations |
https://youtu.be/jp_enyU__4Y?si=2xDpL2dwMSZQk9bh | |
Demo: Concept through PPT + Implementation | |
34 | Best Practices for Calculations in Tableau |
https://youtu.be/jp_enyU__4Y?si=HQZbo_4l9SYsaIrY | |
Demo: Concept through PPT + Implementation | |
35 | Using Nested Calculations in Tableau |
https://youtu.be/mu-4m9-NH6o?si=mSYVy6ceEkBlDaQa | |
Demo: Concept through PPT + Implementation | |
36 | Dynamic Calculations with Parameters |
https://youtu.be/ETP5X0OWNi8?si=wf_s6SVtMSgMEr66 | |
Demo: Concept through PPT + Implementation | |
37 | Combining LOD Expressions and Table Calculations |
https://youtu.be/gLYV1Wy4VKk?si=ZeICRNzxQ2bNZvXK | |
Demo: Concept through PPT + Implementation | |
38 | Advanced Dynamic Calculations |
https://youtu.be/ead__ySyefo?si=sKOxfW8-EyNjcvn_ | |
Demo: Concept through PPT + Implementation | |
39 | Creating Dashboards in Tableau |
https://youtu.be/6oFTdbrugUs?si=iZXvxcaNG0gloSa5 | |
Demo: Concept through PPT + Implementation | |
40 | Dashboard Design Best Practices |
https://youtu.be/t3cAUt7sOQg?si=X6Phyv9bl4917dVD | |
Demo: Concept through PPT + Implementation | |
41 | Dashboard Interactivity in Tableau |
https://youtu.be/Aql27yGqHkE?si=SBcGTJIV7Jm6_TF0 | |
Demo: Concept through PPT + Implementation | |
42 | Interactive Dashboards with Filters |
https://youtu.be/MTlQvyNQ3PM?si=PNZ_7jsgbq8M9scf | |
Demo: Concept through PPT + Implementation | |
43 | Storytelling with Data in Tableau |
https://youtu.be/9IIR7-09rz0?si=Ahkxd9HDwuubL8Sz | |
Demo: Concept through PPT + Implementation | |
44 | Creating Data Stories in Tableau |
https://youtu.be/K7sGGWtZpZc?si=3_PmFfKJsyLNhPFh | |
Demo: Concept through PPT + Implementation | |
45 | Advanced Dashboard Design Techniques |
https://youtu.be/t3cAUt7sOQg?si=JlvT9-AREhIf48Ux | |
Demo: Concept through PPT + Implementation | |
46 | Creating Professional Dashboards in Tableau |
https://youtu.be/oAIubTqg-Kw?si=dWQTU0WMoWGD5t9- | |
47 | Using Actions for Dashboard Interactivity |
https://youtu.be/BnYCwgX7hZQ?si=JgPjaEvdiX7uvbl9 | |
Demo: Concept through PPT + Implementation | |
48 | Dashboard Design Tips and Tricks |
https://youtu.be/nCrD5g8d3ow?si=blRsGWpfUe_MYHTX | |
Demo: Concept through PPT + Implementation | |
49 | Using Parameters and Filters in Tableau |
https://youtu.be/y5P90Qme13k?si=L7qAmUNpER27xftr | |
Demo: Concept through PPT + Implementation | |
Advanced Parameter Usage in Tableau | |
https://youtu.be/sa2VlsLk98k?si=BDFoyFTMFEoHi4fo | |
Demo: Concept through PPT + Implementation | |
50 | Forecasting in Tableau |
https://youtu.be/JdkWZN-7JYk?si=dz58B5xuQWVHp7F0 | |
Advanced Forecasting Techniques | |
https://youtu.be/gCiUcpnR3p4?si=JKFS6ZILtj1PIZVq | |
Demo: Concept through PPT + Implementation | |
Cohort Analysis in Tableau | |
https://www.youtube.com/live/hnt167n_bdE?si=nl64T5fdh04Oa99x | |
Advanced Cohort Analysis | |
https://youtu.be/vbg4Je1tuis?si=p7K6qu4TT-l_7zL0 | |
Demo: Concept through PPT + Implementation | |
51 | Sharing Visualizations with Tableau Public |
https://youtu.be/UM-bYKrHVbo?si=Hb1fFHGZRYBJmhff | |
Demo: Concept through PPT + Implementation | |
52 | Tableau Case Study: Sales Analysis |
https://youtu.be/_qReGTOrKTk?si=JlsnvTdppRKqgzwVDemo: Concept through PPT + Implementation | |
Real-World Application: Financial Dashboard | |
https://youtu.be/jeYjtEX3RAE?si=xuMOdSyS1fSfKKKY | |
Demo: Concept through PPT + Implementation |
ML for Image Analytics (0-4-2) 86hrs
Session-1,2,3
Accessing individual pixels using matrix in imagehttps://www.youtube.com/watch?v=j-ZLDEnhT3Q
Session-4,5,6
Image resize and splitting of imagehttps://www.youtube.com/watch?v=cWHW9MnX_F4
Session-7,8,9,10
Grey scale conversion and mathematical implementationhttps://www.youtube.com/watch?v=sTxGKae0Fck
Session-11,12,13
Colour channel splittinghttps://www.youtube.com/watch?v=PHIyhdcLsL8
Session-14,15,16,17
Histogram equalisation (CLACH)https://www.youtube.com/watch?v=jWShMEhMZI4
Session-18,19,20,21
Edge detection (Sobel, Canny)https://www.youtube.com/watch?v=h8Yp3M8SX2M
Session-22,23,24
Morphological operationshttps://www.youtube.com/watch?v=xSzsD4kXhRw
Session-25,26,27,28
Image segmentationhttps://www.youtube.com/watch?v=kIVk0IhDMwY
Session-29,30,31
Image Thresholdinghttps://www.youtube.com/watch?v=Zf1F4cz8GHU
Session-32,33,34
Binary conversion of segmented imagehttps://www.youtube.com/watch?v=X4icNTQSCss
Session-35,36,37,38
Cluster based segmentationhttps://www.youtube.com/watch?v=6CqRnx6Ic48
Session-39,40,41,42
Feature extraction based on size, shape and colourhttps://www.youtube.com/watch?v=IG3UkAqHnQI
Session-43,44,45,46
Feature extraction using predefined functions: SIFT, SURF, STAR, ORBhttps://www.youtube.com/watch?v=USl5BHFq2H4
Session-47,48,49,50
Feature Extraction using convolutional neural network (CNN).https://www.youtube.com/watch?v=Q21fWIdHGFMhttps://www.youtube.com/watch?v=WvoLTXIjBYU
Session-51,52,53,54
Matrix flattening in Pythonhttps://www.youtube.com/watch?v=6XLXmzv6ajQ
Session-55,56,57
Horizontal stacking and Vertical stackinghttps://www.youtube.com/watch?v=ksqPbrS-b78
Session-58,59,60,61
Creation of Feature matrix by padding required number of zeros and ones.https://www.youtube.com/watch?v=94ZmuGhuBIk
Session-62,63,64
Splitting the feature matrix (training/testing)https://www.youtube.com/watch?v=fwY9Qv96DJY
Session-65,66,67
Labelling the datasetshttps://www.youtube.com/watch?v=V2e0cygY9Vg
Session-68,69,70
Support vector machine (SVM) in image analysis.https://www.youtube.com/watch?v=Y6RRHw9uN9o
Session-71,72,73,74
Different kernels of SVM (linear, polynomial, radial basis function)https://www.youtube.com/results?search_query=Different+kernels+of+SVM+%28linear%2C+polynomial%2C+radial+basis+function%29
Session-75,76,77
Gradient Boosting (GB)https://www.youtube.com/watch?v=jxuNLH5dXCs
Session-78, 79, 80
Multi-layer Perceptron (MLP)https://www.youtube.com/watch?v=AZEfmoWBXwg
Session-881,82,83
Deep learning for Image classification using CNNhttps://www.youtube.com/watch?v=AACPaoDsd50
Session-84,85,86
Deep learning for Image classification using RNNhttps://www.youtube.com/watch?v=iMIWee_PXl8
ML for Hyperspectral Imaging (0-4-2) 86 hrs
Session-1,2,3
Multi-Spectral Imagery (MSI)https://www.youtube.com/watch?v=b0webdvlySo
Session-4,5,6
Hyperspectral Imagery (HSI)https://www.youtube.com/watch?v=e6hbmSfPnMQ
Session-7,8,9
Hyperspectral remote sensing and its applicationshttps://www.youtube.com/watch?v=2LNXeUS25Vw
Session-10,11,12,13
Physics of imaging spectroscopyhttps://www.youtube.com/watch?v=PMk85e90p6s
Session-14,15,16
Electromagnetic propagationhttps://www.youtube.com/watch?v=lTjSdnEcJV8
Session-17,18,19
Sensor physicshttps://www.youtube.com/watch?v=3iaFzafWJQE
Session-20,21,22,23
Atmospheric Correctionshttps://www.youtube.com/watch?v=4VVxuQI5yqQ
Session-24,25,26
Signal-to-Noise ratio (SNR)https://www.youtube.com/watch?v=ZOchEgeuobM
Session-27,28,29
Spectral resolutionhttps://www.youtube.com/watch?v=Hu1T_rEb7D0
Session-30,31,32,33
Image sampling and quantization in digital image processinghttps://www.youtube.com/watch?v=0_255tTnhLQ
Session-34,35,36,37
Spectral angle mapping using different methodshttps://www.youtube.com/watch?v=xq8bOmWQXqU
Session-38,39,40,41
Principal Component Analysis (PCA) in HSI Imaginghttps://www.youtube.com/watch?v=VaiolMYETmE
Session-42,43,44,45
Minimum Noise Fraction (MNF) using different methodshttps://www.youtube.com/watch?v=6sEx9cUvLtw
Session-46,47,48,49
Spectral feature fittinghttps://www.youtube.com/watch?v=5hX7kU52cEU
Session-55,51,52,53
Support Vector Machine (SVM) for HSIhttps://www.youtube.com/watch?v=F5C6FexFfjE
Session-54,55,56,57
Partial Least Squares Regression (PLSR)https://www.youtube.com/watch?v=WKEGhyFx0Dg
Session-58,59,60,62
Spectral Clustering using Pythonhttps://www.youtube.com/watch?v=Z10BXWPFnas
Session-58,59,60,61
K-mean clustering in hyperspectral imaginghttps://www.neonscience.org/classification-kmeans-pca-python#:~:text=KMeans%20is%20an%20iterative%20clustering,to%20classify%20unsupervised%20data%20(eg.&text=Each%20pixel%20in%20the%20image,pixels%20assigned%20to%20the%20cluster.
Session-62,63,64,65
Hyperspectral image classification using multiple spectral and spatial featureshttps://www.youtube.com/watch?v=vxh53StWaUk
Session-67,68,69,70
Models and Algorithms for Hyperspectral Image Processinghttps://www.youtube.com/watch?v=RZu1LHumbiQ
Session-71,72,73,74
Applied Hyperspectral Imaging Fundamentals and Case Studieshttps://www.youtube.com/watch?v=Z_Zub7wJTFs
Session-75,76,77,78
Classification For Hyperspectral Remote Sensing Imaging Using Neural Networkhttps://www.youtube.com/watch?v=Co2Dw-HRFw8
Session-79,80,81,82
Hyperspectral image classification using Deep learning and CNNhttps://www.youtube.com/watch?v=7pdOBBrRqIQ
Session-83,84,85,86
Case study-Implements dimensionality reduction on hyper spectral image(Indian Pines) with classification.Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python
Session-83,84,85,86
Case study-Implements dimensionality reduction on hyper spectral image(Indian Pines) with classification.Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python
IoT Analytics (0+2+2) -60 hrs
Session- 1
Practice 1: Creating Things, Certificates, Policies in AWS IoT core ServicesPractice 2: Connect NodeMCU with AWS IoT Core ServicesPractice 3: Connect ESP32 with AWS IoT Core ServicesPractice 4: Connect Raspberry Pi with AWS IoT Core ServicesPractice 5: Posting Sensor Data to AWS IoT Core ServicesPractice 6: Controlling Devices from AWS IoT Core ServicesPractice 7: Storing Sensor Data into DynamoDB using AWS IoT corePractice 8: Get Raspberry Pi to interact with Amazon Web Services & push data into the DynamoDBPractice 9: Posting Sensor Data to the Thingspeak to aggregate, visualize and analyze live data streams in the cloudPractice 10: Portable IoT Based Fingerprint Biometric Attendance SystemPractice 11: IoT-based Covid Patient Blood Oxygen monitor & calling an ambulance on critical blood oxygen levels
Project (CUML2006)- (0+0+4)
Gate Process for Project
Gate 0: Problem Identification
Gate 1: Data Collection
Gate 2: Model Development
Gate 3: Testing and Validation
Gate 4: Publication, Patent, Product
COs | Course Outcomes |
---|---|
CO1 | Students will gain comprehensive knowledge of predictive analytics, including key concepts, methodologies, and applications. |
CO2 | Students will develop strong analytical skills and the ability to critically assess and interpret data for predictive modeling. |
CO3 | Students will enhance their problem-solving skills and make informed decisions based on predictive analysis results. |
CO4 | Students will gain hands-on experience with various machine learning tools and techniques, designing and developing predictive models. |
CO5 | Students will be able to conduct research in predictive analytics, utilizing advanced techniques and methodologies to solve complex problems. |
Introduction to Predictive Analytics with Python:
https://youtu.be/tdV9L3C-hxQ?si=NY0HHcWFLy9k5cuN
Demo: Concept through PPT + Implementation |
Dr. Sujata Chakravarty is a Senior Member of IEEE. Her research area includes multidisciplinary fields like Application of Computational Intelligence and Evolutionary Computing Techniques in the field of Financial Engineering, Bio-medical data classification, Smart Agriculture, Intrusion Detection System in Computer-Network, Analysis and prediction of different financial time series data. She is a reviewer of many […]
Manoj Kumar Behera, M. tech. in Computer Science, NIT Rourkela, Qualified GATE in 2008. His research area includes application of machine learning and image processing in the fields of smart agriculture and Bio-medical applications. He has published about 20 articles in many international journals and conferences.