Mathematics for Data Science

person icon Ermin Dedic

4.4 ★★★★ ★

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Mathematics for Data Science

Name: Mathematics for Data Science - Online Course
Rating: 4.4 (344 reviews)
Author: Ermin Dedic

obtain all the necessary math knowledge required for data science

updated on icon Updated on Jun, 2025

language icon Language - English

person icon Ermin Dedic

English [CC]

category icon Development ,Data Science,Maths & Statistics

Lectures -55

Duration -5 hours

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4.4 ★★★★ ★

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Course Description

Ermin presents the material through an interactive whiteboard presentation.

The course starts with Linear Algebra.

We start with a definition of what a linear equation is, look at forms of a linear equation, define systems of linear equations, consider notation, and how to solve systems of equations via Row Echelon Form (REF) and Reduced Row Echelon Form (R-REF), and perform matrix-vector multiplication. Then, we explore the concept of mathematical structures to better understand the idea of a vector space, before dealing with concepts like subspaces, bases for vector spaces, dimensions of a vector space/subspace, linear maps, orthogonal projection, and how that is related to least-squares approximation.

The next section is an intro to probability. You will first explore the idea of probability models and axioms, and simple counting, before considering discrete cases of marginal probability, conditional probability, and Bayesian probability. You will also discover the concept of independence and permutations and combinations. Next, the idea of a random variable is illustrated, along with the probability mass and density function, cumulative distribution function, covariance/correlation, the law of large numbers, and the central limit theorem. In the final part, you will discover statistical inference. You will see how the Bayesian Estimator works.

Goals

Define and Solve a System of Linear Equations.
Describe the concept of a Vector Space and Subspace.
Discuss the concepts of linear combinations, span, and basis confidently.
Identify the idea of a Probability Model and its Axioms.
Indicate the purpose of a random variable.
Compare and contrast a Probability Mass Function and Probability Density Function.
Compute a Joint PDF.
Recall what the Law of Large Numbers and Central Limit Theorem tell us.
Estimate error via Bayesian Estimator.

Prerequisites

No prerequisites. This course is geared towards beginners.

Curriculum

Check out the detailed breakdown of what’s inside the course

Intro to Linear Algebra

14 Lectures

Linear Equation Definition 04:51 04:51
Forms of a Linear Equation 03:40 03:40
Systems of Linear Equations 02:56 02:56
Line and Plane 02:54 02:54
Aij Notation 05:27 05:27
System of Equations as a Matrix 04:50 04:50
System in Corresponding Forms 07:40 07:40
Row Echelon Form (Gaussian Elimination) 06:43 06:43
Reduced Row Echelon Form 04:21 04:21
Row Operations Example (REF) 09:07 09:07
Row Operations Rules 05:41 05:41
Visualizing Ax=b 03:23 03:23
General Formula - Matrix Vector Multiplication 09:15 09:15
Tips for Row Operations 06:47 06:47

Mathematical Structures

17 Lectures

Intro to Probability

8 Lectures

Random Variables and Multiple Discrete and Continuous Variables

12 Lectures

Statistical Inference

4 Lectures

Instructor Details

Ermin Dedic

I have a passion for anything data, whether it is applying statistical methods to data more generally, or utilizing a data-driven approach in the Healthcare or Finance/Banking industries.

I studied Psychology for 6-years, including 2 years of Graduate school, where I was training to be a Child/School Psychologist. I was fortunate enough to have the opportunity to experience a blend of course work and clinical work but also recognize some of the problems facing the mental health system and graduate school system. While I am very interested in finding a solution for the latter, this is a long-term goal.

I did ultimately decide to voluntarily leave the Grad program, it was via academics that I fell in love with statistics and statistical software like SPSS/SAS.

Furthermore, it was my Graduate school experience that not only solidified my interest in teaching, it's where I received a lot of positive feedback on my ability to break down complex topics.

I enjoy receiving messages from students who have passed exams, obtained interviews, or gained employment, from taking one of my courses.

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Para meus alunos de língua portuguesa ...

Sou apaixonado por estatística, ciência de dados, programação orientada a objetos e psicologia / saúde mental. Eu desenvolvi um conhecimento em Programação e Estatística SAS através da minha escolaridade e auto-estudo. Também sou autodidata em programação orientada a objetos.

Eu sou um ex-aluno de graduação em psicologia educacional. Dois anos depois, decidi me retirar voluntariamente. Aprendi que o ambiente acadêmico tradicional e o ambiente clínico não eram o caminho adequado para promover mudanças em larga escala.

Ensinar é uma paixão há muito tempo. Criei meu primeiro curso de vídeo online em 2016 (um curso de Estatística). Foi um projeto de pura paixão. Como resultado de obter ótimos comentários, continuei! Atualmente, ensino os cursos de SAS, estatísticas e psicologia, mas também estou sempre aprendendo. Gosto de receber mensagens de alunos que passaram nos exames, obtiveram entrevistas ou obtiveram emprego ao fazer um de meus cursos.

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Mathematics for Data Science

Mathematics for Data Science