BASIC MATHEMATICS AND STATISTICAL TECHNIQUES FOR DATA ANALYSIS

Paper Code: 
DAC 231
Credits: 
2
Periods/week: 
2
Max. Marks: 
100.00
Objective: 

Students will learn basic mathematical and statistical concepts. This foundation will help them in understanding analytical procedures used in Data /Business Analytics.  

Course Outcomes (COs):

   Course outcome  (at course level)

Learning and teaching strategies

Assessment Strategies

Students will be able to:

CO1. Solve problems related to matrix and determinants

 CO2. Identify data and generate discriptive 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

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, Individual and group projects

 

 

6.00
Unit I: 

Matrices and Determinants: 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.

 

6.00
Unit II: 

Statistics: Introduction to Statistics, 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.

 

 

6.00
Unit III: 

Probability : 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,.

 

6.00
Unit IV: 

Statistical Inference and Hypothesis Testing

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.

 

6.00
Unit V: 

Correlation and Regression (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.

 

ESSENTIAL READINGS: 
  1. Sancheti Kapoor, S Chand , “Business Mathematics “
  2. Zameeruddin, Khanna & Bhambri, “Business Mathematics “,Vikas Publishing
  3. Mittal, Satyaprasad & Rao,  “Mathematics and Statistics for Management”, Himalaya Publication
  4. Naval Bajpai ,”Business Statistics” Pearson Publications
  5. Ken Black ,”Business Statistics”, Wiley

 

Academic Year: 
2023-24
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