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UCS654-2026: Predictive Analytics using Statistics (Jan to June 2026 - Even 2526)

Table of Content
Join WhatsApp Group | Click Here
Marking Scheme and General Instructions
  1. MST = 25 Marks (5 Questions*)
  2. EST = 35 Marks (7 Questions*)
  3. Sessional = 40 Marks (5 Components)
    • Assignments = 8 marks
      • Eight assignments on the lecture/non-lecture topics.
      • Upload the assignment on the GitHub and submit in the give google form (mandatory).
      • Explain the solution in brief in ReadMe file.
      • Sample ReadMe file: Click Here
      • Separate submission for every assignment.
        ​
    • Kaggle Hack = 8 marks
      • Internal Hacks: We will organize a total of 10 Kaggle hackathons, conducted weekly.
      • External Hacks: Join and participate other Kaggle hacks to increase participation, ranking, competitions and submissions.
      • Team size: 1-3.
      • Higher marks for maximum participations, ranking, competitions and submissions.
         
    • Quiz = 8 marks (two quizzes)
      • 1st Quiz before MST on LMS (MCQ Type).
      • 2nd Quiz before EST on LMS (MCQ Type).
         
    • Woyage AI - Placement Preparation​ Platform = 8 Marks
      • Practice the interviews of different companies and different subjects.
      • Higher marks for maximum interviews and different activities on the platform.
      • You can complete the interview in lab, hostel, home, library, etc
      • Woyage AI - Detailed Instructions: Click Here​
         
    • Guided Projects = 8 marks
      • Complete the total of min 24 Guided Projects.
      • You can also go for higher number to make your profile stronger.
      • After completion of the guided project a certificate will be generated, make it public, share it on LinkedIn with HashTags use in the sample post (Click Here) and submit to google form (mandatory).
      • Surprise viva will be schedule on the completed projects at any time in the lab.
      • You can complete the guided projects in the lab, hostel, home, library, etc
         
  4. Lecture Schedule
    • Thursday: 10:30 am to 12:10 pm
    • Rooms: (L1 = LT102; L2 = LT403; L3 = LT402).
    • Don't change the room.
       
  5. Course Home Page: Click Here (www.psrana.com/ucs654-2026)
     
  6. Lab Schedule: Click Here​
     
  7. Group Joining Link: Click Here
     
  8. Issue/Query Form: Click Here​
     
  9. Academic Calendar: Click Here​
     
  10. Bring the Laptop and earphone in the Lab.
     
  11. Note: Extra marks for higher attendance (Lecture + Lab).​​
     
01 - Lecture Resources
01 - Lecture Resources
​
  • Topic01 - Topsis - Multiple Criteria Decision Making  |  Link
  • Topic02 - Sampaling | Link
  • Topic03 - Distribution | Link
  • Topic04 - Machine Learning using Pycaret | Link
  • Topic05 - Data Generation using Modelling and Simulation for Machine Learning | Link
  • Topic06 - Association Mining - Apriori | Link
  • Topic07 - Association Mining - ECLAT | Link
  • Topic08 - Multi-Threading using Python | Link
  • Topic09 - Hypothesis Testing and Parameter Estimation | Link
  • Topic10 - Parameter Optimization | Link
  • Topic11 - Nonliner Modelling | Link
  • Topic12 - Measuring Data Similarity and Dissimilarity | Link
  • Topic13 - Ensemble Technique | No PPT
02 - Guided Projects and Lab Experiments
02 - Guided Projects and Lab Experiments
General Instructions
​
1. Guided Project
  • Create login with Thapar email id on Coursera (coursera.org).
  • Explore all "Guided Projects" on Coursera | Click Here
  • Complete Two "Guided Project" per week of your choice and interest.
  • You need to submit "Completion Certificate" and "LinkedIn post"
  • Explore the Sample Certificate | Click Here
  • Explore the Sample Post on the LinkedIn: Click Here
​
  • Lab-01
    1. Guided Project- 01 and 02 | Due Date: 19-01-2026, 8:00 am
    2. Basics of R - Part 1: Install R and R Studio and Practice "Basics of R" | Link
    3. Solve n Kaggle Problems [n=2,3,4....]
​​
  • Lab-02
    1. Guided Project- 03 and 04 | Due Date: 19-01-2026, 8:00 am
    2. Basics of R - Part 2: Practice Ch 5 and Ch 6 from "05 - R for Everyone - Advanced Analytics and Graphics" book | Link
    3. Solve n Kaggle Problems [n=2,3,4....]
​​​
  • Lab-03
    1. Guided Project- 05 and 06 | Due Date: 26-01-2026, 8:00 am
    2. Basics of R - Part 3 - Explore and Practice Chapter 6, 7, 8, 9, and 10 from "05 - R for Everyone" book | Link
    3. Solve n Kaggle Problems [n=2,3,4....]
​
  • Lab-04
    1. Guided Project- 07 and 08 | Due Date: 02-02-2026, 8:00 am
    2. Basics of R - Part 4 - Practice Chapter 11, 12, and 13 from "05 - R for Everyone" book | Link
    3. Solve n Kaggle Problems [n=2,3,4....]
​​
  • Lab-05
    1. Guided Project- 09 and 10 | Due Date: 09-02-2026, 8:00 am
    2. Advance of R - Part 1 - Practice Chapter 14, 15, and 16 from "05 - R for Everyone" book | Link
    3. Solve n Kaggle Problems [n=2,3,4....]
​​
  • Lab-06
    1. Guided Project- 11 and 12 | Due Date: 16-02-2026, 8:00 am
    2. Advance of R - Part 2 - Practice Chapter 17, and 18 from "05 - R for Everyone" book | Link
    3. Solve n Kaggle Problems [n=2,3,4....]
​
  • Lab-07
    1. Guided Project- 13 and 14 | Due Date: 23-02-2026, 8:00 am
    2. Advance of R - Part 3 - Practice Chapter 19, 20, 21, 22, and 23 from "05 - R for Everyone" book | Link
    3. Solve n Kaggle Problems [n=2,3,4....]
​
  • Lab-08
    1. Guided Project- 15 and 16 | Due Date: 30-03-2026, 8:00 am
    2. Advance of R - Part 4 - Practice Chapter 24, 25, and 26 from "05 - R for Everyone" book | Link
    3. Solve n Kaggle Problems [n=2,3,4....]
​​
  • Lab-09
    1. Guided Project- 17 and 18 | Due Date: 06-04-2026, 8:00 am
    2. Advance of R - Part 5 - Practice Chapter 30 from "05 - R for Everyone" book | Link
    3. Solve n Kaggle Problems [n=2,3,4....]
​
  • Lab-10
    1. Guided Project- 19 and 20 | Due Date: 13-04-2026, 8:00 am
    2. Advance of R - Part 6 - Practice "Descriptive statistics in R" | Link
    3. Solve n Kaggle Problems [n=2,3,4....]
​
  • Lab-11
    1. Guided Project- 21 and 22 | Due Date: 20-04-2026, 8:00 am
    2. Solve n Kaggle Problems [n=2,3,4....]
​
  • Lab-12
    1. Guided Project- 23 and 24 | Due Date: 27-04-2026, 8:00 am
    2. Solve n Kaggle Problems [n=2,3,4....]
​​​
03 - Assignments
03 - Assignments
  • Assignment01 - Topsis
    • Due Date: For L1: 16 Jan 2026 | 07:59:59
    • Due Date: For L2: 26 Jan 2026 | 07:59:59
    • Due Date: For L3: 02 Feb 2026 | 07:59:59
    • Assignment Link | Submission Link
  • Assignment02 - Sampling
    • Due Date: For L1: 16 Jan 2026 | 07:59:59
    • Due Date: For L2: 26 Jan 2026 | 07:59:59
    • Due Date: For L3: 02 Feb 2026 | 07:59:59
    • Assignment Link | Submission Link
  • Assignment03 - Topsis for Pretrained Models
    • Due Date: For L1: 16 Jan 2026 | 07:59:59
    • Due Date: For L2: 26 Jan 2026 | 07:59:59
    • Due Date: For L3: 02 Feb 2026 | 07:59:59
    • Assignment Link | Submission Link
  • Assignment04 - Clustering
    • Due Date: For L1: 16 Jan 2026 | 07:59:59
    • Due Date: For L2: 26 Jan 2026 | 07:59:59
    • Due Date: For L3: 02 Feb 2026 | 07:59:59
    • Assignment Link | Submission Link
  • Assignment05 - Parameter Optimization of SVM
    • Due Date: For L1: 16 Jan 2026 | 07:59:59
    • Due Date: For L2: 26 Jan 2026 | 07:59:59
    • Due Date: For L3: 02 Feb 2026 | 07:59:59
    • Assignment Link | Submission Link
  • Assignment06 - Ensemble of Pre-trained models for Deep Fake Detection
    • Due Date: For L1: 16 Jan 2026 | 07:59:59
    • Due Date: For L2: 26 Jan 2026 | 07:59:59
    • Due Date: For L3: 02 Feb 2026 | 07:59:59
    • Assignment Link | Submission Link​​
  • Assignment06 - Mashup
    • Due Date: For L1: 16 Jan 2026 | 07:59:59
    • Due Date: For L2: 26 Jan 2026 | 07:59:59
    • Due Date: For L3: 02 Feb 2026 | 07:59:59
    • Assignment Link | Submission Link
  • Assignment07 - Parameter Estimation
    • Due Date: For L1: 16 Jan 2026 | 07:59:59
    • Due Date: For L2: 26 Jan 2026 | 07:59:59
    • Due Date: For L3: 02 Feb 2026 | 07:59:59
    • Assignment Link | Submission Link
  • Assignment08 - Multi Threading
    • Due Date: For L1: 16 Jan 2026 | 07:59:59
    • Due Date: For L2: 26 Jan 2026 | 07:59:59
    • Due Date: For L3: 02 Feb 2026 | 07:59:59
    • Assignment Link | Submission Link​
Assignment01 - Marks Analysis | 05 Marks | Due Date: 30 Jan 2022 | 23:59:59 | Assignment Link | Submission Link
Assignent02 - Feature Extraction | 05 Marks | Due Date: 06 Feb 2022 | 23:59:59 | Assignment Link | Submission Link
Assignment03 - Classification | 05 Marks | Due Date: 20 Feb 2022 | 23:59:59 | Assignment Link | Submission Link
​​Assignment05 - Google Data Studio | 05 Marks | Due Date: 10 March 2022 | 23:59:59 | Assignment Link | Submission Link
04 - Kaggle
04 - Kaggle Hack
General Instructions
​​
 
  1. Kaggle Ranking: Click Here
    • Explore - Competition Ranking; Dataset Ranking, Code Ranking; Grandmasters; Awards
       
  2. Kaggle Resources​
    • Five days short and intensive course on Kaggle (1hr each; total = 5hr) | Click Here
    • Kaggle Grand Master Talks
    • Getting started with Kaggle by Mr. Raghav Garg, Mr. Aadil Garg, Mr. Pratham Garg | Click Here
    • Kaggle Resources by Mr. Eishkaran Singh | Click Here
    • Kaggle Notebooks:
      • ​Latest Playground Series Basic's Notebook for Beginners | Click Here
      • Latest Playground Series Advanced Approach | Click Here
      • XGB HyperParameter Tuning Notebook | Click Here
  3. Students Achievement 2024 Batch @ Kaggle | Click Here
​Kaggle Hacks
  1. [Live] Kaggle-Hack-1 | Due Date: 12 Jan 2026 08:00 am | Click Here to participate | Click Here to submit team info.
  2. Kaggle-Hack-2 | Due Date: 19 Jan 2026 08:00 am
  3. Kaggle-Hack-3 | Due Date: 26 Jan 2026 08:00 am
  4. Kaggle-Hack-4 | Due Date: 02 Feb 2026 08:00 am
  5. Kaggle-Hack-5 | Due Date: 09 Feb 2026 08:00 am
  6. Kaggle-Hack-6 | Due Date: 16 Feb 2026 08:00 am
  7. Kaggle-Hack-7 | Due Date: 23 Feb 2026 08:00 am
  8. Kaggle-Hack-8 | Due Date: 30 March 2026 08:00 am
  9. Kaggle-Hack-9 | Due Date: 06 April 2026 08:00 am
  10. Kaggle-Hack-10 | Due Date: 20 April 2026 08:00 am
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