What you will learn
-
Important Formulae and Procedures
-
Derivations
-
Engineering relevance of critical concepts
-
Model Question Paper Problems
Welcome to Mathematics-III for CS & Engineering! Elevate your mathematical understanding with our comprehensive course, Mathematics-III (BCS301), designed specifically for Computer Science & Engineering students. Dive into essential topics through Modules 1 to 5, covering Probability Distributions, Joint Probability Distributions & Markov Chains, Statistical Inference, and Design of Experiments & ANOVA. This course blends theory and practical applications, providing you with the mathematical tools crucial for excelling in your field. You'll review basic probability theory, explore binomial, Poisson, normal, and exponential distributions, examine joint probability distributions and Markov chains, learn about sampling distribution and hypothesis testing, study the central limit theorem, use the Chi-square and F-distributions, and understand experimental design and ANOVA techniques. Immerse yourself in an engaging learning experience with captivating visuals, clear lectures, Excel sheets, scientific calculators, and 2D/3D animations, ensuring you grasp the mathematics behind engineering principles and gain the confidence to apply them effectively in real-world scenarios.
Learning Metrix - Set 1
Understanding Probability of an Event/Phenomenon
Understanding Independent Events
Understanding Mutually Exclusive & Exhaustive Events
Conditional Probability vs Bayes' Theorem
Learning Metrix - Set 2
Understanding Discrete Random Variables and Probability Mass Function
Understanding Continuous Random Variables and Probability Density Function
Mathematical Expectation - Concept of Mean
Mathematical Expectation - Concept of Variance
Set 2 - Numericals: (Problem 1)
Set 2 - Numericals: (Problem 2 &3)
Learning Metrix - Set 3
Understanding Normal Distribution - Introduction (Key Takeaways)
Understanding Normal Distribution - What is a bell curve?
Understanding Normal Distribution - Equation & Interpretation
Understanding Normal Distribution - Cummulative probability
Understanding Normal Distribution - Z-Score & Z-Tables
Understanding Normal Distribution - Data Fitting
Understanding Normal Distribution - Understanding moments (Beyond Curriculum)
Set 3 - Normal Distributions - Numericals: (Problem 1)
Set 3 - Normal Distributions - Numericals: (Problem 2)
Understanding Exponential Distribution - Equation & Interpretation
Set 3 - Exponential Distributions - Numericals: (Problem 1)
Set 3 - Exponential Distributions - Numericals: (Problem 2)
Set 3 - Continuous Probability Distributions - Numericals: (Problem 3)
Set 3 - Continuous Probability Distributions - Numericals: (Problem 4)
Litracy for Derivation of Mean & Variance for Binomial & Poisson Distributions
Binomial Distribution - Mean
Binomial Distribution - Variance
Understanding Poisson Distribution - Equation & Interpretation
Poisson Distribution - Mean
Poisson Distribution - Variance
Set 3 - Binomial Distributions - Numericals: (Problem 1)
Set 3 - Binomial Distributions - Numericals: (Problem 2)
Set 3 - Poisson Distributions - Numericals: (Problem 3)
Set 3 - Poisson Distributions - Numericals: (Problem 4)
Introducing the Learning Components/Metrix
Purpose Definition - Why Joint Probability Distribution?
Understanding Joint Probability Distribution
Understanding Marginal Probability, Covariance & Correlation
Ideal Case of Correlation - Fact Discussion
Understanding Independent Random Variables
Set 1 - Numericals: (Problem 1)
Set 1 - Numericals: (Problem 2)
Set 1 - Numericals: (Problem 3)
Set 1 - Numericals: (Problem 5)
Introduction - What to expect from this Set?
Set 2 - Numericals: (Problem 1 - What are Probability Vectors?)
Set 2 - Numericals: (Problem 2 - Regular Probability Matrix)
Set 2 - Numericals: (Problem 3 - Unique Probability Vector)
Introduction - What to expect from this Set?
Set 3 - Numericals: (Problem 1)
Set 3 - Numericals: (Problem 3)
Introducing the Curriculum and Expectations
Introducing the Learning Components/Metrix
Understanding Random Sampling & Simple Sampling Attributes
Example: Sampling Distribution & Standard Error
Hypothesis Testing, Level of Significance, Confidence Limits and Errors
Understanding Test of Significance for large Samples
Set 1 - Numericals: (Problem 2)
Set 1 - Numericals: (Problem 3)
Set 1 - Numericals: (Problem 4)
Set 1 - Numericals: (Problem 5)
Understanding Comparision of Large Samples
Set 2 - Numericals: (Problem 1)
Set 2 - Numericals: (Problem 2)
Purpose Definition - Why this module?
Understanding Central Limit Theorem
Central limit Theorem - Example
Understanding Students t-Distribution & Formulae
Set 1 - Numericals: (Problem 1)
Set 1 - Numericals: (Problem 2)
Set 1 - Numericals: (Problem 3)
Set 1 - Numericals: (Problem 5)
Set 1 - Numericals: (Problem 6)
Introduction to Chi-square & 'f' distribution
Set 2 - Numericals: (Problem 1 - Chi-square)
Set 2 - Numericals: (Problem 2 - Chi-square)
Set 2 - Numericals: (Problem 3 - Chi-square)
Set 2 - Numericals: (Problem 4 - Chi-square)
Set 2 - Numericals: (Problem 5 - Chi-square)
Set 2 - Numericals: (Problem 7 - 'f'-distribution)
Introducing the Curriculum and Expectations
Understanding Principles of Experimentation in Design
Understanding Completely Randomized Design (One-Way ANOVA)
Understanding Randomized Block Design (Two-way ANOVA)
Understanding Latin-Square Design
General Formulae
Set 1 - Numericals: (Problem 1 - One-Way ANOVA)
Set 1 - Numericals: (Problem 2 - One-Way ANOVA)
Set 1 - Numericals: (Problem 3 - Two-Way ANOVA)
Set 1 - Numericals: (Problem 5 - Latin-Square Design)
Set 1 - Numericals: (Problem 6 - Latin-Square Design)
Understanding Analysis of Co-Variance
Set 2 - Numericals: (Problem 1)
No Discussion Found
0.0
0 Reviews
Meet Your Instructor
₹ 1999.00
-
Course Duration0
-
Course LevelIntermediate
-
Student Enrolled0
-
LanguageEnglish
This Course Includes
- 0 Video Lectures
- 0 Quizzes
- 0 Assignments
- 0 Downloadable Resources
- Full Lifetime Access
- Certificate of Completion