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Daniel Egger
and
Paul Bendich
Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.
Read more
Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.
Read more
Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.
Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material.
Topics include:
~Set theory, including Venn diagrams
~Properties of the real number line
~Interval notation and algebra with inequalities
~Uses for summation and Sigma notation
~Math on the Cartesian (x,y) plane, slope and distance formulas
~Graphing and describing functions and their inverses on the x-y plane,
~The concept of instantaneous rate of change and tangent lines to a curve
~Exponents, logarithms, and the natural log function.
~Probability theory, including Bayes’ theorem.
While this course is intended as a general introduction to the math skills needed for data science, it can be considered a prerequisite for learners interested in the course, "Mastering Data Analysis in Excel," which is part of the Excel to MySQL Data Science Specialization. Learners who master Data Science Math Skills will be fully prepared for success with the more advanced math concepts introduced in "Mastering Data Analysis in Excel."
Good luck and we hope you enjoy the course!
Register for this course and see more details by visiting:
OpenCourser.com/course/m385a9/data
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What's inside
Syllabus
Welcome to Data Science Math Skills
This short module includes an overview of the course's structure, working process, and information about course certificates, quizzes, video lectures, and other important course details. Make sure to read it right away and refer back to it whenever needed
Read more
Syllabus
Welcome to Data Science Math Skills
This short module includes an overview of the course's structure, working process, and information about course certificates, quizzes, video lectures, and other important course details. Make sure to read it right away and refer back to it whenever needed
Read more
Building Blocks for Problem Solving
This module contains three lessons that are build to basic math vocabulary. The first lesson, "Sets and What They’re Good For," walks you through the basic notions of set theory, including unions, intersections, and cardinality. It also gives a real-world application to medical testing. The second lesson, "The Infinite World of Real Numbers," explains notation we use to discuss intervals on the real number line. The module concludes with the third lesson, "That Jagged S Symbol," where you will learn how to compactly express a long series of additions and use this skill to define statistical quantities like mean and variance.
Functions and Graphs
This module builds vocabulary for graphing functions in the plane. In the first lesson, "Descartes Was Really Smart," you will get to know the Cartesian Plane, measure distance in it, and find the equations of lines. The second lesson introduces the idea of a function as an input-output machine, shows you how to graph functions in the Cartesian Plane, and goes over important vocabulary.
Measuring Rates of Change
This module begins a very gentle introduction to the calculus concept of the derivative. The first lesson, "This is About the Derivative Stuff," will give basic definitions, work a few examples, and show you how to apply these concepts to the real-world problem of optimization. We then turn to exponents and logarithms, and explain the rules and notation for these math tools. Finally we learn about the rate of change of continuous growth, and the special constant known as “e” that captures this concept in a single number—near 2.718.
Introduction to Probability Theory
This module introduces the vocabulary and notation of probability theory – mathematics for the study of outcomes that are uncertain but have predictable rates of occurrence.
We start with the basic definitions and rules of probability, including the probability of two or more events both occurring, the sum rule and the product rule, and then proceed to Bayes’ Theorem and how it is used in practical problems.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces set theory, real number line properties, functions and their inverse, rates of change, probability, and Bayes' theorem
Provides a strong foundation for beginners in data science math
Prepares learners for advanced math concepts in the "Mastering Data Analysis in Excel" course
Teaches basic math skills necessary for success in almost any data science math course
Provides clear explanations and real-world applications of math concepts
Requires learners to have basic math skills, but not necessarily algebra or pre-calculus
Save this course
Save Data Science Math Skills to your list so you can find it easily later:
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Reviews summary
Data science math skills for beginners
learners say this course provides a largely positive overview of the basic mathematical concepts and skills commonly used in data science, including set theory, functions, probability, and logarithms. Students note that the first three weeks of the course cover foundational math concepts in a clear and engaging manner, while the fourth week on probability is more challenging and requires additional effort and external resources to fully grasp.
According to students, the course effectively introduces important mathematical concepts and provides helpful practice quizzes to reinforce understanding. However, they also highlight areas for improvement, such as the need for more examples and a more in-depth explanation of probability concepts. Additionally, learners raise concerns about errors in the videos and quizzes, which can be distracting and hinder learning.
Overall, learners say this course is a suitable starting point for those looking to brush up on their math skills or gain a basic understanding of the mathematical foundations of data science. However, they emphasize the need for supplementary resources and recommend that learners with limited math backgrounds seek additional support to fully benefit from the course.
need for supplementary resources and recommend that learners with limited math backgrounds seek additional support to fully benefit from the course
"Overall, learners say this course is a suitable starting point for those looking to brush up on their math skills or gain a basic understanding of the mathematical foundations of data science."
"However, they emphasize the need for supplementary resources and recommend that learners with limited math backgrounds seek additional support to fully benefit from the course."
need for more examples
"learners say this course provides a largely positive overview of the basic mathematical concepts and skills commonly used in data science, including set theory, functions, probability, and logarithms."
"Students note that the first three weeks of the course cover foundational math concepts in a clear and engaging manner, while the fourth week on probability is more challenging and requires additional effort and external resources to fully grasp."
"However, they also highlight areas for improvement, such as the need for more examples"
more in-depth explanation of probability concepts
"learners say this course provides a largely positive overview of the basic mathematical concepts and skills commonly used in data science, including set theory, functions, probability, and logarithms."
"Students note that the first three weeks of the course cover foundational math concepts in a clear and engaging manner, while the fourth week on probability is more challenging and requires additional effort and external resources to fully grasp."
"However, they also highlight areas for improvement, such as the need for more examples and a more in-depth explanation of probability concepts."
helpful practice quizzes to reinforce understanding
"the course effectively introduces important mathematical concepts and provides helpful practice quizzes to reinforce understanding"
errors in the videos and quizzes, which can be distracting and hinder learning
"learners say this course provides a largely positive overview of the basic mathematical concepts and skills commonly used in data science, including set theory, functions, probability, and logarithms."
"Students note that the first three weeks of the course cover foundational math concepts in a clear and engaging manner, while the fourth week on probability is more challenging and requires additional effort and external resources to fully grasp."
"However, they also highlight areas for improvement, such as the need for more examples and a more in-depth explanation of probability concepts."
"Additionally, learners raise concerns about errors in the videos and quizzes, which can be distracting and hinder learning."
suitable starting point for those looking to brush up on their math skills or gain a basic understanding of the mathematical foundations of data science
"Overall, learners say this course is a suitable starting point for those looking to brush up on their math skills or gain a basic understanding of the mathematical foundations of data science."
first three weeks of the course cover foundational math concepts in a clear and engaging manner
"learners say this course provides a largely positive overview of the basic mathematical concepts and skills commonly used in data science, including set theory, functions, probability, and logarithms."
"Students note that the first three weeks of the course cover foundational math concepts in a clear and engaging manner"
challenging and requires additional effort and external resources to fully grasp
"Students note that the first three weeks of the course cover foundational math concepts in a clear and engaging manner, while the fourth week on probability is more challenging and requires additional effort and external resources to fully grasp."
largely positive overview of the basic mathematical concepts and skills commonly used in data science
"this course provides a largely positive overview of the basic mathematical concepts and skills commonly used in data science"
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 Data Science Math Skills with these
activities:
Join a Study Group
Show steps
Connect with fellow learners to form a study group, which will provide a supportive and collaborative environment for discussing course concepts and enhancing understanding.
Show steps
-
Reach out to classmates or online forums to find interested individuals.
-
Establish regular meeting times and set clear goals for each session.
-
Actively participate in discussions, share insights, and support others.
Develop a Math Skills Cheat Sheet
Show steps
Create a concise cheat sheet summarizing key math concepts covered in the course, which will serve as a valuable reference for future studies.
Show steps
-
Review course notes and identify essential math concepts.
-
Organize and summarize these concepts in a clear and concise manner.
-
Design a visually appealing and user-friendly cheat sheet.
Practice Set Theory Problems
Show steps
Complete numerous practice problems to ensure you understand the basics of set theory, unions, intersections, and cardinality.
Show steps
-
Find online problem sets or textbooks with practice exercises.
-
Solve a minimum of 20 problems covering various aspects of set theory.
-
Review your solutions and identify areas where you need further practice.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Work through Real Number Line Problems
Show steps
Strengthen your understanding of the real number line, intervals, and inequality algebra by solving a variety of practice problems.
Show steps
-
Locate resources that provide practice problems on the real number line.
-
Solve at least 30 problems involving intervals, inequality notation, and real number properties.
-
Check your answers and focus on improving your understanding of challenging concepts.
Master Summation and Sigma Notation
Show steps
Gain proficiency in using summation and sigma notation through dedicated practice, which is essential for understanding statistical concepts like mean and variance.
Show steps
-
Identify resources offering practice exercises on summation and sigma notation.
-
Solve a minimum of 40 problems covering different scenarios involving summation and sigma notation.
-
Analyze your solutions and address any areas that require further clarification.
Enhance Your Function Graphing Skills
Show steps
Sharpen your ability to graph functions on the Cartesian plane by engaging in targeted practice, which will strengthen your understanding of input-output relationships.
Show steps
-
Find practice problems or online resources that focus on graphing functions.
-
Graph at least 50 functions, paying attention to their shapes, intercepts, and asymptotes.
-
Compare your graphs with solutions and identify areas for improvement.
Master Calculus Concepts through Practice
Show steps
Deepen your understanding of calculus concepts, such as derivatives and rates of change, by engaging in regular practice, which will enhance your problem-solving abilities.
Show steps
-
Identify practice problems or online resources that cover calculus concepts.
-
Solve a minimum of 60 problems involving derivatives, rates of change, and related topics.
-
Review your solutions and seek clarification on any challenging concepts.
Strengthen Your Probability Skills
Show steps
Enhance your grasp of probability theory through dedicated practice, which will improve your ability to analyze and interpret data involving uncertainty.
Show steps
-
Find practice problems or online resources that cover probability concepts.
-
Solve at least 50 problems involving probability rules, Bayes' Theorem, and related topics.
-
Analyze your solutions and address any areas that require further clarification.
Read 'Mathematics for Machine Learning'
Show steps
Expand your understanding of the mathematical foundations of machine learning by reading this comprehensive book, which will provide a solid theoretical basis for your studies.
View
Mathematics for Machine Learning
on Amazon
Show steps
-
Acquire a copy of the book or access it online.
-
Read the book thoroughly, taking notes and highlighting important concepts.
-
Complete the exercises and practice problems provided in the book to reinforce your understanding.
Join a Study Group
Show steps
Connect with fellow learners to form a study group, which will provide a supportive and collaborative environment for discussing course concepts and enhancing understanding.
Show steps
Show steps
Show steps
- Reach out to classmates or online forums to find interested individuals.
- Establish regular meeting times and set clear goals for each session.
- Actively participate in discussions, share insights, and support others.
Develop a Math Skills Cheat Sheet
Show steps
Create a concise cheat sheet summarizing key math concepts covered in the course, which will serve as a valuable reference for future studies.
Show steps
Show steps
Show steps
- Review course notes and identify essential math concepts.
- Organize and summarize these concepts in a clear and concise manner.
- Design a visually appealing and user-friendly cheat sheet.
Practice Set Theory Problems
Show steps
Complete numerous practice problems to ensure you understand the basics of set theory, unions, intersections, and cardinality.
Show steps
Show steps
Show steps
- Find online problem sets or textbooks with practice exercises.
- Solve a minimum of 20 problems covering various aspects of set theory.
- Review your solutions and identify areas where you need further practice.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Work through Real Number Line Problems
Show steps
Strengthen your understanding of the real number line, intervals, and inequality algebra by solving a variety of practice problems.
Show steps
Show steps
Show steps
- Locate resources that provide practice problems on the real number line.
- Solve at least 30 problems involving intervals, inequality notation, and real number properties.
- Check your answers and focus on improving your understanding of challenging concepts.
Master Summation and Sigma Notation
Show steps
Gain proficiency in using summation and sigma notation through dedicated practice, which is essential for understanding statistical concepts like mean and variance.
Show steps
Show steps
Show steps
- Identify resources offering practice exercises on summation and sigma notation.
- Solve a minimum of 40 problems covering different scenarios involving summation and sigma notation.
- Analyze your solutions and address any areas that require further clarification.
Enhance Your Function Graphing Skills
Show steps
Sharpen your ability to graph functions on the Cartesian plane by engaging in targeted practice, which will strengthen your understanding of input-output relationships.
Show steps
Show steps
Show steps
- Find practice problems or online resources that focus on graphing functions.
- Graph at least 50 functions, paying attention to their shapes, intercepts, and asymptotes.
- Compare your graphs with solutions and identify areas for improvement.
Master Calculus Concepts through Practice
Show steps
Deepen your understanding of calculus concepts, such as derivatives and rates of change, by engaging in regular practice, which will enhance your problem-solving abilities.
Show steps
Show steps
Show steps
- Identify practice problems or online resources that cover calculus concepts.
- Solve a minimum of 60 problems involving derivatives, rates of change, and related topics.
- Review your solutions and seek clarification on any challenging concepts.
Strengthen Your Probability Skills
Show steps
Enhance your grasp of probability theory through dedicated practice, which will improve your ability to analyze and interpret data involving uncertainty.
Show steps
Show steps
Show steps
- Find practice problems or online resources that cover probability concepts.
- Solve at least 50 problems involving probability rules, Bayes' Theorem, and related topics.
- Analyze your solutions and address any areas that require further clarification.
Read 'Mathematics for Machine Learning'
Show steps
Expand your understanding of the mathematical foundations of machine learning by reading this comprehensive book, which will provide a solid theoretical basis for your studies.
View
Mathematics for Machine Learning
on Amazon
Show steps
Show steps
Show steps
- Acquire a copy of the book or access it online.
- Read the book thoroughly, taking notes and highlighting important concepts.
- Complete the exercises and practice problems provided in the book to reinforce your understanding.
Career center
Learners who complete Data Science Math Skills will develop knowledge and skills
that may be useful to these careers:
Operations Research Analyst
Operations Research Analyst
Data Science Math Skills may help build a strong foundation for someone wishing to pursue a career as an Operations Research Analyst. This course teaches the basic math used in data science courses and provides an introduction to math concepts like statistics, probability, and calculus. Data Science Math Skills can help prepare one who wishes to pursue a career as an Operations Research Analyst.
Actuary
Actuary
Data Science Math Skills may help build a solid foundation for someone wishing to pursue a career as an Actuary. This course teaches the basic math used in data science courses and provides an introduction to math concepts like probability theory, statistics, and calculus. Data Science Math Skills can help prepare an aspiring Actuary for success.
Risk Manager
Risk Manager
Data Science Math Skills may help build a strong foundation for someone wishing to pursue a career as a Risk Manager. This course teaches the basic math used in data science courses and provides an introduction to math concepts like probability theory and statistics. Data Science Math Skills can help prepare one for a career in Risk Management.
Statistician
Statistician
Data Science Math Skills may help build a solid foundation for someone wishing to pursue a career as a Statistician. This course teaches the basic math used in data science courses and provides an introduction to math concepts like probability theory, calculus, and graphing functions. Data Science Math Skills can help prepare one who wishes to become a Statistician.
Financial Analyst
Financial Analyst
Data Science Math Skills may help build a solid foundation for someone wishing to pursue a career as a Financial Analyst. This course teaches the basic math used in data science courses and provides an introduction to math concepts like probability, statistics, and graphing functions. Data Science Math Skills can be useful in preparing for a career as a Financial Analyst.
Market Researcher
Market Researcher
Data Science Math Skills may help build a strong foundation for someone wishing to pursue a career as a Market Researcher. This course teaches the basic math used in data science courses and provides an introduction to math concepts like probability, statistics, and graphing functions. Data Science Math Skills can help prepare one for a career in Market Research.
Data Scientist
Data Scientist
Data Science Math Skills may help build a foundation for someone wishing to pursue a career as a Data Scientist. This course teaches the basic math used in data science courses and provides an introduction to math concepts like graphing functions, calculus, and probability theory. Data Science Math Skills helps build a foundation needed to enter this field.
Quantitative Analyst
Quantitative Analyst
Data Science Math Skills may help build a strong foundation for someone wishing to pursue a career as a Quantitative Analyst. This course teaches the basic math used in data science courses and provides an introduction to math concepts like probability and calculus. Data Science Math Skills can be a useful stepping stone into this field.
Business Analyst
Business Analyst
Data Science Math Skills may help build a foundation for someone wishing to pursue a career as a Business Analyst. This course teaches the basic math used in data science courses. Data Science Math Skills can be a useful tool for someone hoping to enter this field.
Teacher
Teacher
Data Science Math Skills may help build a foundation for someone wishing to pursue a career as a Teacher focusing on mathematics at the secondary or post-secondary level. This course teaches the basic math used in data science courses and provides an introduction to math concepts like set theory, probability, and graphing functions. Data Science Math Skills can help prepare one to teach a variety of math topics.
Technical Writer
Technical Writer
Data Science Math Skills may help build a foundation for someone wishing to pursue a career as a Technical Writer specializing in mathematical or scientific topics. This course teaches the basic math used in data science courses and provides an introduction to math concepts like probability, statistics, and calculus. Data Science Math Skills can help prepare one to write clearly about complex topics.
Mathematician
Mathematician
Data Science Math Skills may help build a strong foundation for someone wishing to pursue a career as a Mathematician. This course teaches the basic math used in data science courses and provides an introduction to math concepts like set theory, probability, and calculus. Data Science Math Skills can be a helpful tool in a potential Mathematician's toolbox.
Software Engineer
Software Engineer
Data Science Math Skills may be of little relevance to someone wishing to pursue a career as a Software Engineer. This course teaches the basic math used in data science courses and provides an introduction to math concepts like probability, statistics, calculus, and graphing functions. While these topics may not be directly relevant to this role, they may be helpful in developing a strong foundation in logical thinking.
Doctor
Doctor
Data Science Math Skills may be of little relevance to someone wishing to pursue a career as a Doctor. This course teaches the basic math used in data science courses and provides an introduction to math concepts like probability, statistics, and calculus. While these topics may not be directly relevant to this role, they can help you develop strong critical thinking and analytical skills.
Nurse
Nurse
Data Science Math Skills may be of little relevance to someone wishing to pursue a career as a Nurse. This course teaches the basic math used in data science courses and provides an introduction to math concepts like probability, statistics, and calculus. While these topics may not be directly relevant to this role, they can help you develop strong critical thinking and analytical skills.
For more career information including salaries, visit:
OpenCourser.com/course/m385a9/data
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Science Math Skills.
Provides a comprehensive introduction to the mathematical foundations of machine learning, including linear algebra, probability theory, and optimization. It valuable resource for anyone interested in understanding the mathematical underpinnings of data science.
Provides a comprehensive introduction to statistical learning, including topics such as supervised learning, unsupervised learning, and model selection. It valuable resource for anyone interested in understanding the mathematical foundations of data science.
Save
Provides a comprehensive introduction to deep learning, including topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone interested in understanding the mathematical foundations of data science.
Provides a comprehensive introduction to information theory, inference, and learning algorithms. It valuable resource for anyone interested in understanding the mathematical foundations of data science.
Provides a comprehensive introduction to Bayesian data analysis, including topics such as Bayesian inference, model selection, and Markov chain Monte Carlo. It valuable resource for anyone interested in understanding the mathematical foundations of data science.
Provides a practical introduction to machine learning using Python, including topics such as data preprocessing, model training, and model evaluation. It useful resource for anyone interested in understanding the mathematical foundations of data science.
Provides a comprehensive introduction to causal inference, which is the study of how to draw conclusions about cause and effect from data. It valuable resource for anyone interested in understanding the mathematical foundations of data science.
Provides a clear and concise introduction to probability theory, including topics such as random variables, distributions, and Bayes' theorem. It useful reference for anyone interested in understanding the mathematical foundations of data science.
Provides a comprehensive introduction to linear algebra, including topics such as matrices, determinants, and eigenvalues. It valuable resource for anyone interested in understanding the mathematical foundations of data science.
Provides a practical introduction to Bayesian data analysis using R and Stan. It useful resource for anyone interested in understanding the mathematical foundations of data science.
Provides a practical introduction to Bayesian statistics using R and Stan. It useful resource for anyone interested in understanding the mathematical foundations of data science.
Classic text on information theory, which is the mathematical foundation of data science. It provides a clear and concise introduction to topics such as entropy, mutual information, and channel capacity.
Provides a practical introduction to machine learning for people with a programming background. It useful resource for anyone interested in understanding the mathematical foundations of data science.
Save
Provides a gentle introduction to data science, including topics such as data wrangling, data visualization, and machine learning. It useful resource for anyone interested in understanding the mathematical foundations of data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/m385a9/data
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Effort
Four weeks, 3-5 hours per week.
Level
Introductory
Via
Coursera
Institution
Duke University
Instructors
Daniel Egger
Paul Bendich
Language
English
Transcripts
Español
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Português
Deutsch
Italiano
中文
العربية
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آذربایجان دیلی
Good to know
Introduces set theory, real number line properties, functions and their inverse, rates of change, probability, and Bayes' theorem
Provides a strong foundation for beginners in data science math
Prepares learners for advanced math concepts in the "Mastering Data Analysis in Excel" course
Teaches basic math skills necessary for success in almost any data science math course
Provides clear explanations and real-world applications of math concepts
Requires learners to have basic math skills, but not necessarily algebra or pre-calculus
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