Data Analytics with Statistical Software Notes
Download free chapter-wise PDF study materials, module summaries, and revision notes for Data Analytics with Statistical Software (COM3MN210) under Calicut University FYUGP. COM3MN210 - Data Analytics with Statistical Software study notes and resources.
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About Data Analytics with Statistical Software (Calicut University BCom Sem 3)
Data Analytics with Statistical Software (COM3MN210 • 4 Credits • 60 Hours) constitutes an essential academic pillar of the Bachelor of Commerce curriculum under the University of Calicut Four-Year Undergraduate Programme (CUFYUGP Regulations 2024 / 2025). The course delivers comprehensive instruction in core commercial theories, statutory regulatory environments, and practical corporate workflows. Undergraduates transition from introductory commercial overviews to rigorous operational analysis, examining how modern financial enterprises, commercial establishments, and regulatory authorities formulate policies, ensure statutory compliance, and optimize resource allocations. The curriculum integrates historical administrative and financial doctrines with contemporary market realities, exploring quantitative modeling, legal jurisprudence, and computerized financial reporting systems. Through structured case studies, problem-solving workouts, and theoretical evaluations, scholars cultivate the analytical competence required for corporate accounting, financial consultancy, banking services, and higher research in commerce. The subject is evaluated under a 100-mark assessment model consisting of Continuous Internal Evaluation (30 Marks) and the university End-Semester Examination (70 Marks).
Data Analytics with Statistical Software Module-by-Module Curriculum Breakdown
Official syllabus unit modules prescribed by the University of Calicut FYUGP framework:
Theoretical Foundations & Principles
Examines the core principles and analytical methods of Theoretical Foundations & Principles in Data Analytics with Statistical Software (COM3MN210). Key areas of study encompass Introduction to statistical software environments: variable view, data view, data types, and coding scales (Nominal, Ordinal, Scale); Descriptive statistics in software: frequencies, mean, standard deviation, skewness, and exploratory data screening, focusing on conceptual rigor, practical business applications, and institutional procedures under the Calicut University curriculum. Students master quantitative calculations, procedural documentation, and theoretical models essential for scoring high marks in university end-semester evaluations.
Operational Frameworks & Methodologies
Examines the core principles and analytical methods of Operational Frameworks & Methodologies in Data Analytics with Statistical Software (COM3MN210). Key areas of study encompass Data visualization: generating histograms, boxplots, scatter plots, and error bars; Hypothesis testing concepts: null vs. alternative hypothesis, Type I and Type II errors, p-value decision rules, focusing on conceptual rigor, practical business applications, and institutional procedures under the Calicut University curriculum. Students master quantitative calculations, procedural documentation, and theoretical models essential for scoring high marks in university end-semester evaluations.
Regulatory Compliance & Quantitative Analysis
Examines the core principles and analytical methods of Regulatory Compliance & Quantitative Analysis in Data Analytics with Statistical Software (COM3MN210). Key areas of study encompass Parametric testing: One-sample t-test, Independent samples t-test, and Paired samples t-test execution and output reading; Analysis of Variance (ANOVA): One-way ANOVA, post-hoc tests (Tukey HSD), and F-statistic interpretation, focusing on conceptual rigor, practical business applications, and institutional procedures under the Calicut University curriculum. Students master quantitative calculations, procedural documentation, and theoretical models essential for scoring high marks in university end-semester evaluations.
Strategic Applications & Contemporary Trends
Examines the core principles and analytical methods of Strategic Applications & Contemporary Trends in Data Analytics with Statistical Software (COM3MN210). Key areas of study encompass Non-parametric tests: Chi-Square test of independence, contingency tables, and Mann-Whitney U test; Correlation and Multiple Linear Regression: interpreting R-squared, ANOVA table, beta coefficients, and multicollinearity (VIF), focusing on conceptual rigor, practical business applications, and institutional procedures under the Calicut University curriculum. Students master quantitative calculations, procedural documentation, and theoretical models essential for scoring high marks in university end-semester evaluations.
Data Analytics with Statistical Software Core Curriculum & Key Topics
- Introduction to statistical software environments: variable view, data view, data types, and coding scales (Nominal, Ordinal, Scale)
- Descriptive statistics in software: frequencies, mean, standard deviation, skewness, and exploratory data screening
- Data visualization: generating histograms, boxplots, scatter plots, and error bars
- Hypothesis testing concepts: null vs. alternative hypothesis, Type I and Type II errors, p-value decision rules
- Parametric testing: One-sample t-test, Independent samples t-test, and Paired samples t-test execution and output reading
- Analysis of Variance (ANOVA): One-way ANOVA, post-hoc tests (Tukey HSD), and F-statistic interpretation
- Non-parametric tests: Chi-Square test of independence, contingency tables, and Mann-Whitney U test
- Correlation and Multiple Linear Regression: interpreting R-squared, ANOVA table, beta coefficients, and multicollinearity (VIF)
Data Analytics with Statistical Software Learning Outcomes
- Structure commercial survey data into statistical software defining appropriate measurement scales.
- Generate and interpret descriptive statistics and diagnostic data screening plots.
- Formulate statistical hypotheses and execute independent and paired sample t-tests on business data.
- Conduct One-way ANOVA tests and interpret post-hoc multiple comparison outputs.
- Perform multiple linear regression analysis and evaluate model goodness-of-fit (R-squared) and coefficient significance.
Calicut University Examination Scheme for Data Analytics with Statistical Software
The evaluation framework for Data Analytics with Statistical Software (COM3MN210) under Calicut University FYUGP regulations combines Continuous Internal Evaluation (30 Marks) with the university End-Semester Examination (70 Marks) for a total of 100 Marks:
Objective and definitional questions assessing foundational terms, terminology, and core principles across all syllabus modules.
Paragraph-style conceptual questions requiring theoretical models, comparative explanations, and structured illustrative diagrams.
In-depth essay and workout problems requiring comprehensive synthesis, case study evaluation, or numerical problem-solving.
Data Analytics with Statistical Software Exam Preparation Tips
Under Calicut University FYUGP regulations, the end-semester theory examination for Data Analytics with Statistical Software (COM3MN210) carries 70 Marks (2 or 2.5 hours duration) evaluated across three structured sections: Part A Conceptual Definitions (Short Answers): Prepare concise, textbook-precise definitions for core terminology, statutory clauses, and operational metrics. Write 2 to 3 focused sentences for each prompt and provide one clear illustrative example. Avoid conversational phrasing. Part B Analytical Paragraphs (Models & Typologies): Focus on structured comparative explanations, classification typologies, and step-by-step procedures. Structure responses with numbered subheadings, draw labeled diagrams where applicable, and construct comparative tables with explicit basis-of-distinction columns. Part C Comprehensive Essays & Workout Problems: Follow a formal five-part structure: (1) Conceptual Introduction & Scope, (2) Detailed Theoretical Exposition or Step-by-Step Numerical Computation, (3) Labeled Schematic Diagram or Accounting Ledger Accounts, (4) Statutory / Practical Industry Evaluation, and (5) Concluding Managerial Appraisal. Ensure all ledger balances or formula substitutions are neatly highlighted with double underlines.
Frequently Asked Questions - Data Analytics with Statistical Software
What is Big Data and how do the 4 V's define it in enterprise analytics?
Big Data refers to vast, complex datasets characterized by the 4 V's: Volume (enormous scale of data), Velocity (real-time generation speed), Variety (structured, semi-structured, unstructured data), and Veracity (data quality and trustworthiness).
How does predictive regression modeling assist commercial demand forecasting?
Predictive regression establishes a mathematical equation linking demand to past drivers (price, ad spend, competitor actions), allowing managers to simulate revenue outcomes under varying future commercial scenarios.
What is Data Mining and how do businesses use Association Rule Mining?
Data mining discovers hidden patterns and relationships in massive transaction logs. Association rule mining (Market Basket Analysis) identifies items frequently bought together (e.g., bread and butter), guiding store shelf layout and cross-selling.
What are the ethical responsibilities regarding customer data privacy in analytics?
Enterprises must secure explicit informed consent before data collection, anonymize personal records, prevent unauthorized third-party sharing, and comply with data sovereignty regulations like India's DPDP Act 2023.
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