UCS654: Predictive Analytics using Statistics
(Jan to June 26 - EVEN2526)
Table of Content Join WhatsApp Group | Click Here
01 - Syllabus
02 - Lecture Resources
03 - LDP
04 - Lab Experiments
05 - Evaluation Scheme
06 - Assignments
Recommended Books
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Peter Dalgaard, Introductory Statistics with R, Springer, Second Edition
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Brett Lantz, Machine Learning with R (2nd Edition), www.PacktPub.com.
Reference Books
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Online Resource| Click Here
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Introduction to Machine Learning in R| Click Here
01 - Syllabus
UCS654 (Scheme-2023) | Syllabus | Link
Syllabus for MST
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Topic-1(Dr. Suresh): Random variable and its properties,
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Topic-2(Dr. Suresh): PDF, PMF, Distributions (their descriptive statistics like mean, variance, median and mode)
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Topic-3: Topsis
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Topic-4: Data Generation using Modeling and Simulation + Finding Outliers using IQR and Z-Score Methods
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Topic-5: Sampling
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Topic-6: Hypothesis-Testing
Course Learning Outcomes (CLOs) / Course Objectives (COs)
CO1: Demonstrate the ability to use basic probability concepts with descriptive statistics. [Covered for MST]
CO2: Visualize the patterns in the data. [Covered for MST]
CO3: Demonstrate the use of statistical methods to estimate characteristics of the data. [Covered for MST]
CO4: Explain and demonstrate the use of predictive analytics in the field of data science. [Covered before EST]
Instruction(s)
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The MST exam will be a blend of the mathematical, numerical and derivation based question.
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The exam will contain question with equal distribution of the marks. The maximum marks may be 25 marks.
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There will be negative marking, if answers are not found in the sequence.
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The cutting of the answer and any answer using pencil, will be awarded zero.
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The answers will be evaluated with reference to the ideal solution.
02 - Lecture Resources
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Topic-1(Dr. Suresh)
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Random variable and its properties
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Topic-2(Dr. Suresh)
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PDF, PMF, Distributions (their descriptive statistics like mean, variance, median and mode)
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Study Material (Including Topic1 and Topic2)
03 - Lecture Delivery Plan [till MST]
Week 1: Data Distribution & Identity [CODE Link]
L1: Random variable, Probability, PMF, and PDF. Application: Using PDFs to detect data drift and reason the normalization of input data.
L2: Random Variables & Different Distributions (Gaussian, Bernoulli). Application: Initializing Neural Network weights.
Week 2: Relationships between Features
L3: Statistical Independence. Application: Identifying redundant features in a dataset to reduce model complexity.
L4: Variance, Co-variance, and Correlation. Application: Constructing correlation heatmaps to prevent Multi-collinearity in training.
04 - Lab Experiments
Experiment-1: Probability Distribution and PDF Fitting [Click Here]
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Objective: To generate data and fit a probability density function to understanding data assumptions.
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Theory: PDFs describe continuous random variables. ML models assumed data to be distributed as Gaussian distributions for stable learning.
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Tasks to be performed
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Generate synthetic data
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Plot histogram
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Fit Gaussian PDF
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Expected Output: Histogram with overlaid Gaussian PDF.
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Data Visualization and Statistical Analysis using Python [Guided Project]: Select one numerical feature from a real dataset, analyze its distribution, fit a Gaussian PDF, and justify whether normalization is required before applying a Machine Learning model.
Experiment-2: Importance of the Variance [Click Here]
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Objective:
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To understand how variance represents information content in data
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To analyze correlation and redundancy among features
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To demonstrate dimensionality reduction using variance (PCA)
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To justify normalization before ML models based on feature distribution.
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Instructions
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Guided projects must be of average to high difficulty level. The project completion certificate must be submitted through the link provided by the coordinator.
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Guided projects must be submitted only during the scheduled lab session. Any submission after the scheduled date and time will be awarded zero marks.
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Lab experiment details and the manual will be shared through the course page.
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Each lab experiment must be performed within the first 30 minutes of the lab session. A viva-voce may be conducted during the evaluation to assess conceptual understanding.
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Independent work is mandatory. Students must be able to explain their approach, logic, and results. Inability to justify the work may result in reduced or zero marks, even if the output is correct.
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Use of AI tools, code generators, or online assistance during lab evaluation is strictly prohibited, unless explicitly permitted by the instructor.
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Absence from the lab, irrespective of the reason, will not be considered for awarding marks in the lab experiment.
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Latecomers will not be permitted to attend the lab under any circumstances.
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Any form of unethical practice (plagiarism, copying, proxy attendance, or impersonation) will be dealt with strictly as per institute academic policies.
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Evaluation will emphasize conceptual clarity, methodology, and interpretation, not merely producing correct results.
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Students are advised to come well prepared by reviewing the lab manual and relevant theory in advance to ensure meaningful learning during the lab.
06 - Assignments
Assignment-01 [Click Here]
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Deadline: 21 Jan 2026 till 11.59PM
Assignment-02 [Click Here] [Submission Link]
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Deadline: 28 Jan 2026 till 11.59PM