Department Events
Basic Mathematics and Statistical Techniques for Data Analysis (Theory)
Students will learn basic mathematical and statistical concepts. This foundation will help them in understanding analytical procedures used in Data /Business Analytics.
Course | Course outcome (at course level) | Learning and teaching strategies | Assessment Strategies | |
Course Code | Course Title | |||
24DAC231 |
Basic Mathematics And Statistical Techniques For Data Analysis -I (Theory) | CO1. Solve problems related to matrix and determinants CO2. Identify data and generate descriptive statistics of data CO3. Apply discrete and continuous probability distributions to various problems CO4. Create hypothesis and Apply regression, correlation and F-test on real life problems CO5. Interpret and analyze the results. CO6. Contribute effectively in course-specific interaction | Approach in teaching: Interactive Lectures, Discussion, reading assignments, Demonstrations, Group activities, Teaching using advanced IT audio-video tools Learning activities for the students: Self-learning assignments, Effective questions, Seminar presentation, Giving tasks. | Assessment Strategies Class test, Semester end examinations, Quiz, Solving problems in tutorials, Assignments, Presentation
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Definition of a Matrix, Addition & Subtraction of Matrices Multiplication of Matrices, Transpose of a Matrix, Determinants, Determinants of order one and more, Properties of Determinants, Multiplication of two Determinants, Minors and Cofactors System of linear equations, Inverse of a Matrix, Cramer's rule for solution of linear equations, Adjoint of a Matrix, Rank of a Matrix.
Scale of Measurement, Nominal, Ordinal, Interval & Ratio. Frequency Distribution, Histogram, Frequency Polygon, Ogive, Measure of Central Tendency: Mean, Median & Mode, Properties, Advantages and Disadvantages. Measure of Dispersion: Range, Interquartile Range, Standard Deviation, Quartiles, Deciles, Percentiles
Introduction to Probability, Types of probability ,Experiment, Sample Space Random Experiment, Event, Conditional Probability, General Rule of Addition (without proof), General Rule of Multiplication (without proof), Concept of Baye's Theorem.
Probability Distribution, Discrete Probability Distributions: Binomial, Poisson, Continuous Probability Distribution, Normal Distribution, Central Limit Theorem &t-distribution.
Population and Sample, Null and Alternate Hypothesis, Level of Significance, Type I and Type II Errors, Confidence Intervals, Sampling Distribution :application of t test(single mean independent two mean, paired) ,Chi Square Test (goodness of fit, independence of attributes),F test. large sample test:One Sample Proportion Test, Two Sample Proportion Tests, Analysis of Variance.
Theoretical Concepts and Inferences, Analysis of Relationship, Positive and Negative Correlation, Perfect Correlation, Correlation Matrix, Scatter Plots, Simple Linear Regression, R Square, Adjusted R Square, Testing of Slope, Standard Error of Estimate, Assumptions of Linear Regression, Multiple Regression, Coefficients of Partial Determination, Durbin Watson Statistics, Variance Inflation Factor.
- Sancheti kapoor, S Chand , “Business mathematics”
- Zameeruddin, Khanna & bhambri, “Business Mathematics”, Vikas Publishing
- Naval bajpai ,”Business Statistics” Pearson Publications
Suggested Readings:
1. Mittal, SatyaPrasad & Rao, “Mathematics and Statistics for Management”, Himalaya Publication
2. Ken black ,”Business Statistics”, Wiley
e-Resources:
Journals:
1. https://www.springer.com/journal/41060
2. Computational Statistics & Data Analysis by International Association for Statistical Computing (IASC)
3. https://www.igi-global.com/journal/international-journal-data-analytics/225843
