Master Of Mathematics In Data Science: 3-In-1 Bootcamp

From Foundations to Advanced Applications: Master Mathematical Statistics, Probability, and Core Mathematics

What you'll learn

Master the statistical foundations to design, validate, and interpret data-driven experiments and models with confidence.

Apply core probability theory to solve complex problems in fields like quantitative finance and risk analysis.

Comprehend and use essential mathematical concepts like linear algebra and optimization theory that form the building blocks of machine learning and AI algorith

Build a strong theoretical framework for a data science career that goes beyond simply using pre-built libraries and empowers you to innovate.

Implement key mathematical and statistical principles in Python using libraries like NumPy and SciPy.

Requirements

A passion for a rigorous, mathematical approach to data science.

A high school-level understanding of algebra.

No prior knowledge of data science or coding is required. We will guide you from the ground up on everything you need to know.

Description

"Embark on a transformative learning journey with the Master of Mathematics in Data Science course package. This is not just another data science tutorial; it's a rigorous, three-part program designed to give you the deep theoretical foundation that separates expert data scientists from the rest. You'll master the mathematical and statistical principles that power modern data science, machine learning, and artificial intelligence."What You'll Learn (Learning Objectives)Master Mathematical Statistics: Learn the core principles of statistical inference, hypothesis testing, and regression analysis. Understand how to design experiments and validate models with confidence.Conquer Probability for Data Science: Dive into the crucial concepts of probability theory, Bayesian inference, and stochastic processes, essential for fields like quantitative finance and risk modeling.Build the Mathematical Foundation: Solidify your understanding of key mathematical concepts, including advanced linear algebra, calculus, and optimization, which are the building blocks of all data science algorithms.Apply Theory with Code: Put your knowledge into practice with hands-on projects and coding exercises using Python and popular libraries like NumPy, Pandas, and SciPy.Who This Course Is ForCareer Changers: Aspiring data scientists from non-technical backgrounds who want to build a solid, credible foundation.Students & Graduates: University students in math, physics, engineering, or economics who want to apply their theoretical knowledge to data science.Junior Data Analysts: Professionals who want to move beyond basic tools and truly understand the algorithms they use.Anyone curious about the "why" and "how" behind data science models, not just the "what."Course Structure This comprehensive package is broken down into three distinct modules, each acting as a standalone course. You can take them in any order, but we recommend following the structured path for the most effective learning experience.Module 1: Mathematical Statistics for Data ScienceModule 2: Probability for Data ScienceModule 3: Core Mathematics for Data Science

Overview

Section 1: 1. Introduction to Linear Algebra

Lecture 1 1. What is a Matrix

Lecture 2 2. Scalars and Vectors

Lecture 3 3. Linear Algebra and Geometry

Lecture 4 4. Scalars, Vectors, and Matrices as Python Arrays

Lecture 5 5. What is a Tensor

Lecture 6 6. Addition and Subtraction

Lecture 7 7. Errors when Adding Matrices

Lecture 8 8. Transpose of a Matrix

Lecture 9 9. Dot Product

Lecture 10 10. Dot Product of Matrices

Lecture 11 11. Why is Linear Algebra Useful

Section 2: 1. The Basics of Probability

Lecture 12 1.What is the probability formula.mp4

Lecture 13 2. Computing Expected Values.mp4

Lecture 14 3. The Probability Frequency Distribution.mp4

Lecture 15 4. Complements.mp4

Section 3: 2. Combinatorics

Lecture 16 1. Fundamentals of Combinatorics.mp4

Lecture 17 2. Computing Permutations.mp4

Lecture 18 3. Solving Factorials.mp4

Lecture 19 4. Computing Variations with Repetition.mp4

Lecture 20 5. Computing Variations without Repetition.mp4

Lecture 21 6. Computing Combinations.mp4

Lecture 22 7. Symmetry of Combinations.mp4

Lecture 23 8. Combinations with Separate Sample Spaces.mp4

Lecture 24 9. Winning the Lottery.mp4

Lecture 25 10. A Summary of Combinatorics.mp4

Lecture 26 11. Combinatorics – Practical Example.mp4

Section 4: 3. Bayesian Inference

Lecture 27 1. Sets and Events.mp4

Lecture 28 2. The Different Ways Events Can Interact.mp4

Lecture 29 3. The Intersection of Two Sets.mp4

Lecture 30 4. The Union of Two Sets.mp4

Lecture 31 5. Mutually Exclusive Sets.mp4

Lecture 32 6. Dependent and Independent Events.mp4

Lecture 33 7. Conditional Probability.mp4

Lecture 34 8. Law of Total Probability.mp4

Lecture 35 9. Additive Law.mp4

Lecture 36 10. Multiplication Rule.mp4

Lecture 37 11. Bayes Rule.mp4

Lecture 38 12. Bayesian - Practical Example.mp4

Section 5: 4. Probability Distributions

Lecture 39 1. An overview of distributions.mp4

Lecture 40 2. Types of Distributions.mp4

Lecture 41 3. Discrete Uniform Distributions.mp4

Lecture 42 4. Discrete Uniform Distributions.mp4

Lecture 43 5. Bernoulli Distributions.mp4

Lecture 44 6. Binomial Distributions.mp4

Lecture 45 7. Poisson Distributions.mp4

Lecture 46 8. Continuous Distributions.mp4

Lecture 47 9. Normal Distributions.mp4

Lecture 48 10. Standardizing Normal Distributions.mp4

Lecture 49 11. StudentsT Distributions.mp4

Lecture 50 12. Chi Squared Distributions.mp4

Lecture 51 13. Exponential Distributions.mp4

Lecture 52 14. Logistic Distribution.mp4

Lecture 53 15. Probability Distributions - A Practical Example.mp4

Section 6: 5. Probability in Other Fields

Lecture 54 1. Probability in Finance.mp4

Lecture 55 2. Probability in Statistics.mp4

Lecture 56 3. Probability in Data Science.mp4

Section 7: Statistics

Lecture 57 1. Introduction Statistics

Lecture 58 2. Population vs sample.mp4

Section 8: 2. Descriptive Statistics Fundamentals

Lecture 59 1. Types of data.mp4

Lecture 60 2. Levels of measurement.mp4

Lecture 61 3. Categorical variables. Visualization techniques.mp4

Lecture 62 4. Numerical variables. Frequency distribution.mp4

Lecture 63 5. The histogram.mp4

Lecture 64 6. Cross table and scatter plot.mp4

Lecture 65 7. Mean, median, mode.mp4

Lecture 66 8. Skewness.mp4

Lecture 67 9. Variance.mp4

Lecture 68 10. Standard deviation and coefficient of variation.mp4

Lecture 69 11. Covariance.mp4

Lecture 70 12. Correlation.mp4

Section 9: 3. Practical Example - Descriptive Statistics

Lecture 71 1. Practical Example - Descriptive Statistics

Section 10: 4. Inferential Statistics Fundamentals

Lecture 72 1. Introduction.mp4

Lecture 73 2. What is a distribution.mp4

Lecture 74 3. The Normal Distributions.mp4

Lecture 75 4. The Standard Normal Distribution.mp4

Lecture 76 5. Central limit theorem.mp4

Lecture 77 6. Standard error.mp4

Lecture 78 7. Estimators and estimates.mp4

Section 11: 5. Confidence Intervals

Lecture 79 1. Definition of confidence intervals.mp4

Lecture 80 2. Population variance known, z-score.mp4

Lecture 81 3. Confidence Interval Clarifications.mp4

Lecture 82 4. Student's T Distribution.mp4

Lecture 83 5. Population variance unknown, t-score.mp4

Lecture 84 6. Margin of error.mp4

Lecture 85 7. Confidence intervals. Two means, Dependent samples.mp4

Lecture 86 8. Confidence intervals. Two means, Independent samples (Part 1).mp4

Lecture 87 9. Confidence intervals. Two means, Independent samples (Part 2).mp4

Lecture 88 10. Confidence intervals. Two means, Independent samples (Part 3).mp4

Section 12: 6. Practical Example - Confidence Intervals

Lecture 89 1. Practical Example - Confidence Intervals

Section 13: 7. Hypothesis testing

Lecture 90 1. Null vs Alternative.mp4

Lecture 91 2. Rejection region and significance level.mp4

Lecture 92 3. Type I error vs type II error.mp4

Lecture 93 4. Test for the mean. Population variance known.mp4

Lecture 94 5. p-value.mp4

Lecture 95 6. Test for the mean. Population variance unknown.mp4

Lecture 96 7. Test for the mean. Dependent samples.mp4

Lecture 97 8. Test for the mean. Independent samples (part 1).mp4

Lecture 98 9. Test for the mean. Independent samples (part 2).mp4

Section 14: 8. Practical Example - Hypothesis testing

Lecture 99 1. Practical Example - Hypothesis testing

Career changers who want a solid, credible, and theoretical foundation in data science before transitioning into the field.,University students (undergraduate or graduate) studying math, physics, engineering, or economics who want to apply their theoretical knowledge to real-world data science problems.,Junior data analysts and data scientists who want to deepen their understanding of the underlying principles behind the models they use every day.,Anyone who is curious about the "why" and "how" behind data science and machine learning algorithms, not just the "what."