Coming soon
Basic Mathematics and Statistical Techniques for Data Analysis
The course will enable the students to
- To study the concept of basic mathematical and statistical methods.
- This will help students in understanding analytical procedures used in Data Analytics.
- To study the scaling of data.
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Course |
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Learning and teaching strategies |
Assessment Strategies |
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Paper Code |
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BAC231 |
Basic Mathematics and Statistical Techniques for Data Analysis
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Students will: 1)Solve problems related to matrix and determinants 2) Identify data and generate discriptive statistics of data 3) Apply discrete and continuous probability distributions to various problems 4) Create hypothesis and Apply regression ,correlation and F-test on real life problems 5) 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.
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Assessment Strategies Class test, Semester end examinations, Quiz, Solving problems in tutorials, Assignments, Presentation, Individual and group projects
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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.
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.
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,.
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.
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.
- Sancheti Kapoor, S Chand , “Business Mathematics “
- Zameeruddin, Khanna & Bhambri, “Business Mathematics “,Vikas Publishing
- Mittal, Satyaprasad & Rao, “Mathematics and Statistics for Management”, Himalaya Publication
- Naval Bajpai ,”Business Statistics” Pearson Publications
- Ken Black ,”Business Statistics”, Wiley
