Mathematics-Basics to Advanced for Data Science And GenAI from Udemy

Are you eager to dive into the world of data science but feel overwhelmed by the mathematical concepts involved? Welcome to the "Complete Maths to Learn Data Science" course, your comprehensive guide to mastering the essential mathematical foundations needed to excel in data science and machine learning.

This course is designed to bridge the gap between your current math skills and the level required to understand and implement data science algorithms effectively. Whether you are a beginner or an experienced professional looking to strengthen your mathematical understanding, this course will equip you with the tools you need to succeed.

What You Will Learn:

Calculus for Data Science:
- Understand the fundamentals of calculus, including derivatives, integrals, and limits.
- Learn how these concepts are applied in optimizing machine learning algorithms, such as gradient descent, and in understanding complex data transformations.
Linear Algebra Essentials:
- Gain a deep understanding of vectors, matrices, eigenvalues, and eigenvectors.
- Discover how these linear algebra concepts are crucial for data manipulation, dimensionality reduction (like PCA), and building advanced machine learning models.
Probability Theory and Its Applications:
- Dive into the world of probability, including concepts like random variables, distributions, and Bayes’ Theorem.
- Explore how probability forms the backbone of predictive modeling, classification algorithms, and risk assessment in data science.
Statistics for Data Analysis:
- Master key statistical techniques such as hypothesis testing, regression analysis, and statistical inference.
- Learn to make data-driven decisions by understanding and applying statistical methods to real-world datasets.

Why This Course?

This course stands out by focusing on the clarity and practical application of mathematical concepts in data science. Each topic is broken down into simple, easy-to-understand modules that build on one another. You will not only learn the theory but also see exactly how these mathematical tools are used in real data science scenarios.

Throughout the course, you’ll engage with interactive quizzes, assignments, and hands-on projects designed to reinforce your understanding. By applying what you learn in real-world projects, you’ll gain practical experience and build a portfolio that showcases your newly acquired skills.

Who Is This Course For?

Aspiring Data Scientists: Individuals looking to build a strong mathematical foundation essential for mastering data science and machine learning.
Data Science Beginners: Those new to the field who want to understand the core mathematical concepts that drive data science algorithms.
Professionals Transitioning into Data Science: Engineers, analysts, or professionals from other fields seeking to acquire the mathematical skills necessary for a career shift into data science.
Students and Academics: Students pursuing studies in data science, computer science, or related fields who need a comprehensive understanding of mathematics for data science applications.
Lifelong Learners: Anyone with a passion for learning and a desire to understand how mathematics powers the world of data science, even without prior experience in the field.

Enroll Today.

Join thousands of learners who have transformed their careers by mastering the mathematics behind data science. Whether you’re aiming to start a new career, enhance your skills, or simply satisfy your curiosity, this course will provide the solid mathematical foundation you need to succeed. Enroll now and take the first step towards becoming a confident and skilled data scientist.

What's inside

Learning objectives

Master calculus: understand derivatives and integrals, and apply them in optimizing machine learning algorithms and data analysis tasks.
Learn linear algebra: grasp vectors, matrices, and eigenvalues, essential for building and understanding advanced data science models.

Understand probability: dive into probability theory, crucial for making informed predictions and working with uncertainty in data.
Apply statistics: gain practical skills in statistical analysis, helping you make data-driven decisions and interpret results effectively.

Master calculus: understand derivatives and integrals, and apply them in optimizing machine learning algorithms and data analysis tasks.
Learn linear algebra: grasp vectors, matrices, and eigenvalues, essential for building and understanding advanced data science models.
Understand probability: dive into probability theory, crucial for making informed predictions and working with uncertainty in data.
Apply statistics: gain practical skills in statistical analysis, helping you make data-driven decisions and interpret results effectively.

Syllabus

Welcome To This Course

What We are Going To Learn

Introduction To Linear Algebra

Introduction

Scalars And Vectors

Addition Of Vectors

Multiplication Of Vectors

Vector Databases- Examples Of Cosines similarity

Vectors Multiplication-Element Wise Multiplication

Vectors Multiplication-Scaler Multiplication

Introduction To Matrices And Application

Matrices Operation

Introduction To Functions And Transformation

Introduction To Function And Linear Transformation

Vector Transformations

Linear Transformation

Why Linear Transformation?

Linear Transformation Visualization

Vector Length And Vector Unit

Introduction To Projection

Inverse Functions Or Transformation

Inversion Functions

Applications of Function And Inverse Function

How to find Inverse Of A Matrix

Eigen Vectors And Eigen Values

All You need to know about Eigen Values And Eigen Vectors

Equation Of a Line,Plane,Hyperplane

Equation OF a Line,Plane And Hyperplane

Introduction To Statistics

Types Of Statistics

Population And Sample Data

Types Of Sampling

Types Of Data

Scales OF Measurement Of Data

Descriptive Statistics

Measure Of Central Tendency

Measure Of Dispersion

Why Sample Variance is Divided By N-1

Random Variables

Percentile And Quartiles

5 Number Summary

Histogram And Skewness

Correlation And Covariance

Introduction To Probability

Addition Rule In Probability

Multiplication Rule In Probability

Probability Distribution function And Types Of Distribution

PDF,PMF,CDF

Types Of Probability Distribution

Bernoulli Distribution

Binomial Distribution

Poisson Distribution

Normal Gaussian Distribution

Standard Normal Distribution and Z score

Uniform Distribution

Log Normal Distribution

Power Law Distribution

Pareto Distribution

Central Limit Theorem

Estimates

Inferential Stats and Hypothesis Testing

Hypothesis Testing And Its Mechanism

P value and Hypothesis Testing

Z test And Hypothesis Testing

Student T Distribution

T stats With t Test Hypothesis Testing

Z test vs T test

Type 1 and Type 2 Error

Baye's Theorem

Confidence Interval And Margin OF Error

Chi Square Test With Solved Exmaples

What is Chi Square Test

Chi Square Goodness OF Fit

Annova Test With Solved Examples

What is Annova

Assumptions Of Annova

Types OF Annova

Partioning OF Variance In Anova

Differential Calculus

What are Slopes and How To Calculate

Introduction To Derivatives

Mathematical Notation Of Derivatives With Limits

Finding a Derivative At a Point with Examples

Power Rules And Derivative Rules

Power Rules In Derivative

Derivative Rules- Constant,Sum,Difference And Scaler Multiplication

Equation Of Tangent Of Polynomials

Derivatives Of Trignometric,Logarithmic and Exponential Functions

Note: Small Announcement About Previous Video

Product Rules In Derivative

Product Rules In Derivative with Exmaples

Chain Rule Of Derivatives

Composition Of 3 or many functions

Application Of Chain Rule Of Derivative

Application Of Linear algebra,Stats And Differential Calculus In Data Science

Main Aim Of This Section

Learning First ML Algorithm- Simple Linear Regression

Understanding Linear Regression Equations

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers calculus, linear algebra, probability, and statistics, which are fundamental mathematical pillars for data science and machine learning, providing a strong base for algorithm understanding

Breaks down complex mathematical concepts into simple, easy-to-understand modules, making it accessible for individuals with limited prior mathematical knowledge or experience

Offers hands-on projects and real-world applications, enabling learners to build a portfolio showcasing their newly acquired mathematical skills in a practical context

Includes a comprehensive study of statistical techniques such as hypothesis testing and regression analysis, which are essential for data-driven decision-making and interpreting results effectively

Requires learners to grasp concepts such as derivatives, integrals, vectors, matrices, eigenvalues, and eigenvectors, which may demand significant time and effort to fully comprehend

Assumes learners have access to software and tools to implement the mathematical concepts, which may require additional costs or resources beyond the course fees

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Mathematics-Basics to Advanced for Data Science And GenAI with these activities:

Review Basic Algebra Concepts

Show steps

Strengthen your understanding of fundamental algebraic concepts. This will provide a solid foundation for the linear algebra and calculus topics covered in the course.

Browse courses on Algebra

Show steps

Review key concepts like solving equations and manipulating expressions.
Practice solving algebra problems.

Review 'Linear Algebra Done Right'

Show steps

Deepen your understanding of linear algebra. This book offers a more theoretical approach, complementing the practical applications covered in the course.

View Linear Algebra Done Right (Undergraduate Texts... on Amazon

Show steps

Read the chapters related to vectors, matrices, and linear transformations.
Work through the exercises to solidify your understanding.

Practice Calculus Problems

Show steps

Reinforce your calculus skills through practice problems. This will help you apply the concepts learned in the course to real-world data science scenarios.

Show steps

Find a set of calculus practice problems online or in a textbook.
Work through the problems, focusing on derivatives and integrals.
Check your answers and review any concepts you struggled with.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Create a Cheat Sheet for Statistical Distributions

Show steps

Consolidate your knowledge of different statistical distributions. Creating a cheat sheet will help you quickly recall the properties and applications of each distribution.

Show steps

List the key statistical distributions covered in the course.
For each distribution, note its properties, parameters, and common applications.
Organize the information in a clear and concise format.

Implement Linear Regression from Scratch

Show steps

Solidify your understanding of linear regression by implementing it from scratch. This will give you a deeper appreciation for the underlying mathematics and algorithms.

Show steps

Choose a dataset to use for your linear regression model.
Implement the linear regression algorithm using Python or another programming language.
Evaluate the performance of your model.

Review 'Probability and Statistics for Data Science'

Show steps

Expand your knowledge of probability and statistics. This book provides a data science-focused perspective, complementing the theoretical foundations covered in the course.

View Putting Knowledge to Work on Amazon

Show steps

Read the chapters related to probability distributions and hypothesis testing.
Work through the examples and exercises to solidify your understanding.

Help Others in the Course Discussion Forum

Show steps

Reinforce your understanding by helping other students. Explaining concepts to others is a great way to solidify your own knowledge.

Show steps

Regularly check the course discussion forum for questions.
Answer questions to the best of your ability, providing clear and concise explanations.

Career center

Learners who complete Mathematics-Basics to Advanced for Data Science And GenAI will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data scientists analyze complex data to derive insights and build predictive models. This course directly helps a data scientist because it covers many of the mathematical building blocks needed for data analysis and machine learning. The course includes calculus, linear algebra, probability theory, and statistics, all of which allow a data scientist to manipulate data and to optimize their models. The course gives one a deeper appreciation for the methods employed by a data scientist, as well as the conceptual understanding to be successful.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A machine learning engineer builds and deploys machine learning models. Since this course provides a deep dive into the mathematical foundations - such as calculus, linear algebra, probability, and statistics - this course is directly applicable to this role. The course covers the mathematical underpinnings of many machine learning algorithms, optimizing them, and understanding how they work. A student taking this course will gain a solid background in the math needed for a role as a machine learning engineer.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

Quantitative analysts use mathematical and statistical models to analyze financial data and to make trading decisions. A quantitative analyst will find this course highly applicable since it thoroughly covers the mathematical foundations of finance, including probability, statistics, linear algebra, and even calculus. This course helps a quantitative analyst to understand the mathematical underpinnings of financial modeling and to create new algorithms for trading. Having a solid mathematical foundation is crucial for a quantitative analyst.

See salaries and explore the career path for Quantitative Analyst

Artificial Intelligence Researcher

Artificial intelligence researchers design, develop, and test new AI algorithms and techniques. This course may help an AI researcher by providing a strong base in mathematics, including calculus, linear algebra, probability theory, and statistics. An AI research scientist can use these skills to understand existing models or to create new ones. This course will help one gain the ability to understand the math behind AI at a fundamental level. Note that this role typically requires an advanced degree.

See salaries and explore the career path for Artificial Intelligence Researcher

Data Analyst

Data analysts examine data to identify trends, create visualizations, and provide reports. A data analyst will find that this course provides many of the mathematical skills needed to understand data. The course covers statistical methods, probability theory, and basic linear algebra, which are essential to analyzing and interpreting data. Understanding the mathematics at this level gives a data analyst a significant edge in their career.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians develop and apply statistical theories and methods. This course may be helpful to a statistician as it covers key statistical concepts such as hypothesis testing, regression analysis, and probability distributions. Given that statisticians must deeply understand these concepts, this course can help one build a strong foundation in those topics, leading to a more successful career. Note that this role typically requires an advanced degree.

See salaries and explore the career path for Statistician

Algorithm Developer

Algorithm developers design and implement algorithms for various applications, including data analysis, machine learning, and other fields. This course may be useful to an algorithm developer as it thoroughly covers the concepts of linear algebra, probability, and calculus, which are the underpinnings of many algorithms. The course may help an aspiring algorithm developer better understand these algorithms. By understanding these mathematics, one can build and optimize useful algorithms for a variety of uses.

See salaries and explore the career path for Algorithm Developer

Financial Modeler

Financial modelers build mathematical models to forecast financial outcomes and evaluate investments. This course may be useful to a financial modeler because it introduces the mathematical and statistical techniques used in financial modeling. The course covers linear algebra, probability, and statistical inference, allowing one to approach modeling with more confidence. The course also introduces students to the kinds of mathematics used by financial modelers daily.

See salaries and explore the career path for Financial Modeler

Business Intelligence Analyst

Business intelligence analysts use data to analyze business performance and to identify opportunities for improvement. This course may be helpful for a business intelligence analyst since it covers data analysis techniques in its study of statistics and probability. The course also introduces linear algebra, which provides a basis for many sophisticated data handling techniques. Completing this course will help a business intelligence analyst to better understand the data they are working with daily.

See salaries and explore the career path for Business Intelligence Analyst

Research Scientist

Research scientists conduct experiments and analyze data to explore scientific questions. A research scientist may find this course useful because it introduces statistical methods, probability, and linear algebra, all of which are essential for data analysis and experimentation. The course helps build a basic understanding of how to interpret data. This course can be a good fit for those planning to enter research science. Note that this role typically requires an advanced degree.

See salaries and explore the career path for Research Scientist

Operations Research Analyst

Operations research analysts apply mathematical and analytical methods to solve complex problems and improve operational efficiency. A course on mathematics may be helpful to an operations research analyst, as it covers the mathematical techniques used in modeling and optimization. The course gives one tools for creating new models with applications in the field. Linear algebra, calculus, and statistics are particularly useful in this role, all of which are covered by the course.

See salaries and explore the career path for Operations Research Analyst

Bioinformatician

Bioinformaticians analyze biological data using computational tools and methods. A bioinformatician may find this course useful because it gives a broad introduction to data analysis. Statistics and probability, covered in this course, are both very important components of bioinformatics. The course may help a bioinformatician understand the underlying models that they work with, giving insight and intuition into the data. Note that this role typically requires an advanced degree.

See salaries and explore the career path for Bioinformatician

Analytics Consultant

Analytics consultants advise businesses on how to improve their performance using data analytics. This course may be helpful to an analytics consultant as it introduces the mathematics behind data analysis. The course covers topics like statistics, probability, and linear algebra. These topics provide a strong mathematical foundation, allowing an analytics consultant to make well informed recommendations to their clients. This course may also facilitate discussion with technical experts.

See salaries and explore the career path for Analytics Consultant

Software Developer

Software developers create and maintain software applications using coding. Software developers may find this course useful because it gives insight into the mathematics underlying many aspects of software engineering, including machine learning and data analysis. The course provides an understanding of linear algebra, calculus, and statistics, helping one better work with and debug algorithms and software. However, much of a developer's work is not directly related to mathematics.

See salaries and explore the career path for Software Developer

Database Administrator

Database administrators manage and maintain databases, ensuring data integrity and availability. While this career is not as directly related to the course content as some others, this course may be useful for a database administrator since it touches on linear algebra, which provides the conceptual background to understand data structures. It introduces ways to think about organizing and manipulating data. However, a database administrator may find other more relevant courses to be more helpful.

See salaries and explore the career path for Database Administrator

Mathematics-Basics to Advanced for Data Science And GenAI

What's inside

Learning objectives

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

Share

Similar courses