Math for Data Science Masterclass from Udemy

Welcome to the best online course for learning about the Math behind the field of Data Science.

Working together for the first time ever, Krista King and Jose Portilla have combined forces to deliver you a best in class course experience in how to use mathematics to solve real world data science problems. This course has been specifically designed to help you understand the mathematical concepts behind the field of data science, so you can have a first principles level understanding of how to use data effectively in an organization.

Often students entering the field of data science are confused on where to start to learn about the fundamental math behind the concepts. This course was specifically designed to help bridge that gap and provide students a clear, guided path through the complex and interesting world of math used in the field of data science. Designed to balance theory and application, this is the ultimate learning experience for anyone wanting to really understand data science.

Why choose this course?

Combined together, Krista and Jose have taught over 3.2 million students about data science and mathematics and their joint expertise means you'll be able to get the best and clearest mathematical explanations from Krista with framing about real world data science applications from Jose. At the end of each section is a set of practice problems developed from real-world company situations, where you can directly apply what you know to test your understanding.

What's covered in this course?

In this course, we'll cover:

Understanding Data Concepts
Measurements of Dispersion and Central Tendency
Different ways to visualize data
Permutations
Combinatorics
Bayes' Theorem
Random Variables
Joint Distributions
Covariance and Correlation
Probability Mass and Density Functions
Binomial, Bernoulli, and Poisson Distributions
Normal Distribution and Z-Scores
Sampling and Bias
Central Limit Theorem
Hypothesis Testing
Linear Regression
and much more.

Enroll today and we'll see you inside the course.

Krista and Jose

What's inside

Syllabus

Introduction

Welcome to the Course! Important Info in this Lecture!

Course Overview and Curriculum

Let's get familiar with some key ideas behind data and information!

Hi there!

Let's quickly check your knowledge with a quick question. If you watched the previous lecture, this question should be fairly easy to answer.

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Read about what's good

what should give you pause

and possible dealbreakers

Balances theory and application, which is ideal for learners who want to understand the math behind data science and apply it to real-world problems

Covers measurements of dispersion and central tendency, which are fundamental concepts for understanding and interpreting data in any data science project

Explores various data distributions, including binomial, Bernoulli, Poisson, and normal distributions, which are essential for statistical modeling and analysis in data science

Includes practice problems developed from real-world company situations, allowing learners to apply their knowledge and test their understanding in practical scenarios

Requires learners to understand core data concepts, which may necessitate additional introductory coursework for individuals completely new to data analysis

Teaches hypothesis testing, which is a core skill for drawing conclusions and making data-driven decisions in various data science applications

Reviews summary

Math fundamentals for data science

According to learners, the Math for Data Science Masterclass offers comprehensive coverage of fundamental mathematical concepts critical for understanding data science. Taught by experienced instructors Krista King and Jose Portilla, the course provides a clear, guided path through topics including probability, distributions, sampling, hypothesis testing, and linear regression. It emphasizes a balance between theory and real-world data science applications, reinforced by practice problems developed from real-world scenarios. Students seem to appreciate the clear explanations and the course's success in helping them build a solid mathematical foundation necessary for the field.

Taught by notable experts.

"Learning from Krista King and Jose Portilla was a great experience; their combined expertise is evident."

"The instructors clearly know their stuff and teach the math concepts effectively."

"They have a wealth of experience that shines through in their explanations."

Concepts are explained clearly.

"The math concepts were explained in a really clear and understandable way, which is crucial for this subject."

"I never felt lost thanks to the clear teaching style and pacing."

"Krista's explanations of the mathematical underpinnings were particularly clear."

Covers essential math topics for DS.

"The course covers all the fundamental math concepts I expected for data science, like probability and distributions."

"I found the range of topics, from probability basics to linear regression, very thorough and relevant."

"It provides a solid overview of the mathematical landscape needed to start in data science."

Balances concepts with practice.

"I liked how the course balanced theoretical math with practical applications in data science."

"The real-world problems helped me see how the math is actually used on the job."

"Applying the concepts in the practice problems solidified my understanding significantly."

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 Math for Data Science Masterclass with these activities:

Review Basic Statistics Concepts

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Reinforce foundational statistical concepts like mean, median, mode, variance, and standard deviation to better understand data concepts covered in the course.

Browse courses on Mean

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Review definitions and formulas for key statistical measures.
Work through practice problems involving these measures.
Check your solutions against answer keys.

Review 'Naked Statistics: Stripping the Dread from the Data'

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Gain a more intuitive understanding of statistical concepts through real-world examples and engaging explanations.

View Naked Statistics: Stripping the Dread from the... on Amazon

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Read the book, focusing on chapters related to course topics.
Take notes on key concepts and examples.
Reflect on how these concepts relate to data science applications.

Probability Problem Sets

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Sharpen your understanding of probability by working through a variety of practice problems.

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Find online resources with probability problem sets.
Solve problems related to conditional probability and Bayes' Theorem.
Compare your solutions with provided answers and explanations.

Four other activities

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Create a Data Visualization Portfolio

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Solidify your understanding of data visualization techniques by creating a portfolio of visualizations using real-world datasets.

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Select several datasets relevant to your interests.
Create different types of plots (scatter plots, histograms, bar plots, etc.) for each dataset.
Write a brief description of each visualization, explaining the insights it reveals.
Compile your visualizations and descriptions into a portfolio.

Statistical Analysis of a Public Dataset

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Apply the statistical concepts learned in the course to analyze a public dataset and draw meaningful conclusions.

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Choose a public dataset from a source like Kaggle or the UCI Machine Learning Repository.
Perform exploratory data analysis, calculating descriptive statistics and creating visualizations.
Formulate hypotheses and conduct hypothesis tests to answer specific questions about the data.
Write a report summarizing your findings and conclusions.

Review 'All of Statistics: A Concise Course in Statistical Inference'

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Deepen your understanding of statistical inference and its applications in data science.

View All of Statistics: A Concise Course in... on Amazon

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Read chapters related to hypothesis testing, regression, and other advanced topics.
Work through the exercises at the end of each chapter.
Compare your solutions with the provided solutions manual.

Tutor Other Students

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Reinforce your understanding of the material by helping other students who are struggling with the concepts.

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Offer to tutor classmates or students in introductory statistics courses.
Prepare explanations and examples to help them understand difficult concepts.
Answer their questions and provide feedback on their work.

Career center

Learners who complete Math for Data Science Masterclass will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist extracts knowledge and insights from data using scientific methods, algorithms, and systems. This involves a deep understanding of statistical analysis and machine learning, along with the ability to communicate findings effectively. The "Math for Data Science Masterclass" certainly builds a strong foundation for this role. A data scientist works with concepts directly addressed in the course, such as understanding data concepts, measurements of dispersion, probability, distributions, hypothesis testing, and linear regression. Anyone looking to become a data scientist should take this course.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst is responsible for collecting, processing, and performing statistical analyses of data. They examine data, identify trends, and develop reports to help drive business decisions. This course on math for data science is highly applicable, as it provides a solid understanding of core data concepts, measurements of central tendency, dispersion, and visualizations. Data analysts must understand probability and distributions. The course's focus on hypothesis testing and linear regression could be particularly useful. This masterclass is a great starting point for aspiring data analysts aiming to understand the mathematical foundations of their work.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

A Business Intelligence Analyst analyzes data to identify trends and provide insights that help businesses make better decisions. Their work involves a strong understanding of data analysis techniques and the ability to translate data into actionable recommendations. Skills taught in the "Math for Data Science Masterclass" such as understanding data concepts, measurements of central tendency, and visualizing data, are essential for success in this role. Understanding probability, sampling, and hypothesis testing provides additional value. A business intelligence analyst interprets data, often using the skills that this particular course hones.

See salaries and explore the career path for Business Intelligence Analyst

Machine Learning Engineer

A Machine Learning Engineer develops and implements machine learning algorithms and models. This requires a strong background in mathematics, statistics, and computer science. The "Math for Data Science Masterclass" helps build a fundamental understanding of the mathematical concepts behind machine learning. The course's coverage of probability, distributions, linear regression, and hypothesis testing are all highly relevant. This course would be very helpful if you want to begin a path towards becoming a machine learning engineer, or want to increase your fundamental knowledge.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

A Quantitative Analyst, often working in the finance industry, uses mathematical and statistical methods to develop and implement trading strategies, manage risk, and value financial instruments. Quantitative analysts typically require an advanced degree. This course on math for data science may provide a relevant foundation, as it covers essential concepts like probability, distributions, covariance, correlation, and linear regression. The course's discussion of Bayes' Theorem and hypothesis testing could be particularly helpful. A quantitative analyst thrives by using the mathematics learned in courses like this.

See salaries and explore the career path for Quantitative Analyst

Statistician

A Statistician collects, analyzes, and interprets data to identify trends and relationships. They often work on designing experiments, conducting surveys, and developing statistical models to solve problems in a variety of fields. Statisticians often require advanced degrees. The "Math for Data Science Masterclass" provides a helpful introduction to many of the core mathematical concepts used in statistics. This course covers distributions, sampling, hypothesis testing, and linear regression. Statisticians use these skills and this course may be useful as an introduction to them.

See salaries and explore the career path for Statistician

Market Research Analyst

A Market Research Analyst studies market conditions to examine potential sales of a product or service. They analyze data on demographics, consumer behavior, and competitive landscape to advise companies on marketing strategies. The analysis of this data may require the kind of math and statistics that the "Math for Data Science Masterclass" provides. Understanding core concepts, measurements of central tendency and dispersion, visualization, probability and distributions could be quite useful. The course can provide a helpful foundation for a market research analyst.

See salaries and explore the career path for Market Research Analyst

Economist

An Economist studies the production, distribution, and consumption of goods and services. They use economic theories and models. An economist often requires a master's degree or doctorate. While this role is broad, economists often rely on the analytical skills that a course such as "Math for Data Science Masterclass" may help sharpen. The course's discussion of regression could be particularly helpful. Economists benefit from an understanding of sampling, and hypothesis testing. This course may provide additional context.

See salaries and explore the career path for Economist

Bioinformatician

A Bioinformatician analyzes biological data using computational tools and statistical methods. One might study genomic data to identify disease markers or develop new treatments. This typically requires at least a master's degree. A course like "Math for Data Science Masterclass" may prove valuable. Key skills include understanding probability, distributions, and hypothesis testing. The course could provide a grounding in data analysis. Bioinformaticians are those that analyze data generated from biological research.

See salaries and explore the career path for Bioinformatician

Financial Analyst

A Financial Analyst provides guidance to businesses and individuals making investment decisions. This involves analyzing financial data, preparing reports, and making recommendations. This career may benefit from the skills that the "Math for Data Science Masterclass" provides. Concepts discussed in the course like covariance and correlation may be very useful. The course's lessons on regression can help financial analysts model outcomes, and forecast results. This course can help provide a basis for understanding analytical models.

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Operations Research Analyst

An Operations Research Analyst uses mathematical and analytical methods to help organizations solve problems and make better decisions. This might involve optimizing supply chains, improving logistics, or streamlining operations. The "Math for Data Science Masterclass" provides a possible foundation for the skills needed in this role. Knowledge of probability, distributions, and linear regression may be particularly useful. The course can provide a foundation in basic mathematical and analytical thinking.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

A Risk Analyst identifies and assesses potential risks that could impact an organization. This involves analyzing data, developing risk models, and recommending strategies to mitigate risk. The "Math for Data Science Masterclass" may be a helpful starting point for learning some of the skills involved. The course covers probability, distributions, covariance, and correlation. This course may prove helpful as an introduction to fundamental mathematics and statistics.

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Actuary

An Actuary analyzes statistical data to estimate risk and uncertainty. These professionals are essential to insurance companies, as the company is exposed to risk. Actuaries typically require a series of professional exams. The "Math for Data Science Masterclass" is thematically useful and may cover some of the concepts tested on preliminary actuarial exams, such as probability. The course may provide a gentle introduction to the kind of thinking this career utilizes.

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Logistics Analyst

A Logistics Analyst manages the supply chain. This involves analyzing data, coordinating shipments, and optimizing routes. A course such as the "Math for Data Science Masterclass" may be thematically useful. The course's coverage of data visualization could be useful. The course may also provide general analytical skills that are helpful in this career.

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Management Consultant

A Management Consultant helps organizations improve their performance by analyzing problems and developing solutions. Although this role is broad, the course "Math for Data Science Masterclass" may teach you some of the concepts used in the role. The course may teach data concepts, measurements of central tendency, dispersion, and visualizations. This course may give someone the first introduction to what management consulting is like.

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Math for Data Science Masterclass

What's inside

Syllabus

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Reviews summary

Math fundamentals for data science

Activities

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