Math for Data science,Data analysis and Machine Learning from Udemy

In this course, we will learn Math essentials for Data science,Data analysis and Machine Learning. We will also discuss the importance of Linear Algebra,Statistics and Probability,Calculus and Geometry in these technological areas. Since data science is studied by both the engineers and commerce students ,this course is designed in such a way that it is useful for both beginners as well as for advanced level. The lessons of the course is also beneficial for the students of Computer science /artificial intelligence and those learning Python programming.

Here, this course covers the following areas :

Importance of Linear Algebra
Types of Matrices
Addition of Matrices and its Properties
Matrix multiplication and its Properties
Properties of Transpose of Matrices
Hermitian and Skew Hermitian Matrices
Determinants ; Introduction
Minors and Co factors in a Determinant
Properties of Determinants
Differentiation of a Determinant
Rank of a Matrix
Echelon form and its Properties
Eigenvalues and Eigenvectors
Gaussian Elimination Method for finding out solution of linear equations
Cayley Hamilton Theorem
Importance of Statistics for Data Science
Statistics : An Introduction
Statistical Data and its measurement scales
Classification of Data
Measures of Central Tendency
Measures of Dispersion: Range, Mean Deviation, Std. Deviation & Quartile Deviation
Basic Concepts of Probability
Sample Space and Verbal description & Equivalent Set Notations
Types of Events and Addition Theorem of Probability
Conditional Probability
Total Probability Theorem
Baye's Theorem
Importance of Calculus for Data science
Basic Concepts : Functions, Limits and Continuity
Derivative of a Function and Formulae of Differentiation
Differentiation of functions in Parametric Form
Rolle;s Theorem
Lagrange's Mean Value Theorem
Average and Marginal Concepts
Concepts of Maxima and Minima
Elasticity : Price elasticity of supply and demand
Importance of Euclidean Geometry
Introduction to Geometry
Some useful Terms,Concepts,Results and Formulae
Set Theory : Definition and its representation
Type of Sets
Subset,Power set and Universal set
Intervals as subset of 'R'
Venn Diagrams
Laws of Algebra of Sets
Important formulae of no. of elements in sets
Basic Concepts of Functions
Graphs of real valued functions
Graphs of Exponential , Logarithmic and Reciprocal Functions

Each of the above topics has a simple explanation of concepts and supported by selected examples.

I am sure that this course will be create a strong platform for students and those who are planning for appearing in competitive tests and studying higher Mathematics .

You will also get a good support in Q&A section . It is also planned that based on your feed back, new course materials will be added to the course. Hope the course will develop better understanding and boost the self confidence of the students.

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What's inside

Syllabus

Introduction

Linear Algebra

Importance of Linear Algebra

Types of Matrices

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Covers fundamental mathematical concepts like linear algebra, statistics, calculus, and geometry, which are essential building blocks for understanding data science and machine learning algorithms

Explores the importance of various mathematical concepts in data science, data analysis, and machine learning, providing context for how these concepts are applied in real-world scenarios

Includes a comprehensive review of set theory, functions, and graphs, which are often featured in standardized tests and can help students strengthen their problem-solving abilities

Discusses topics such as eigenvalues, eigenvectors, and Gaussian elimination, which are relevant to machine learning algorithms and other areas of computer science and artificial intelligence

Presents a wide range of topics, from basic matrix operations to Baye's Theorem, which may be overwhelming for absolute beginners without prior exposure to mathematical concepts

Focuses on theoretical concepts and mathematical formulas, which may require learners to seek out additional resources for practical applications and hands-on coding exercises

Reviews summary

Foundations in math for data science

According to learners, this course provides a broad overview of the essential mathematical topics needed for data science, data analysis, and machine learning. Many students find it a good starting point, particularly for those who are new to the field or need a refresher, appreciating the simple explanations of concepts and practical examples provided. However, some reviewers note that while the course offers significant breadth by covering linear algebra, statistics, probability, and calculus, it may lack the necessary depth for learners with intermediate or advanced backgrounds or for directly tackling complex machine learning applications. This suggests that while the course builds a foundational platform, additional, more advanced study may be required depending on individual career goals.

Suitability depends on prior math knowledge.

"Coming from a non-math background, it was challenging but doable."

"If you already have a math degree, this might feel too simple as a review."

"Good as a refresher, but not deep enough if you need advanced proofs."

"My experience differed from others based on my engineering background."

Concepts are supported by practical examples.

"The illustrations really helped make abstract math ideas concrete."

"I appreciated the various examples used to show how concepts apply."

"Seeing the math worked out step-by-step in examples was useful."

"Examples were relevant and made understanding easier."

Concepts explained simply, suitable for new students.

"I was new to some of these math concepts and found the explanations clear."

"If you're just starting out in data science math, this is a great first step."

"The basic examples helped me grasp the fundamentals easily."

"Very accessible explanations for someone coming from a non-math background."

Explores multiple core math areas for DS/ML.

"This course touches on all the math areas you need for data science."

"I liked getting an introduction to linear algebra, stats, and calculus in one place."

"It gives a good overview of the math concepts used in ML."

"A very comprehensive course that covers many necessary math areas."

Coverage can be too basic for advanced learners.

"While it covers many topics, I felt the depth wasn't enough for complex ML tasks."

"As someone with some background, I found parts too introductory and wanted more."

"It's a good starting point, but don't expect to master topics here; you'll need more study."

"The explanations were simple but didn't go deep enough into proofs or complex applications."

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,Data analysis and Machine Learning with these activities:

Review Linear Algebra Fundamentals

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Strengthen your understanding of linear algebra concepts before diving into the course. This will make grasping advanced topics easier.

Browse courses on Linear Algebra

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Review basic matrix operations (addition, multiplication).
Practice solving systems of linear equations.
Understand the concepts of eigenvalues and eigenvectors.

Review 'Linear Algebra and Its Applications' by Gilbert Strang

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Supplement your learning with a comprehensive textbook on linear algebra. This will provide a deeper understanding of the concepts covered in the course.

View Linear Algebra for Everyone on Amazon

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Read the chapters relevant to the course topics.
Work through the examples and exercises in the book.
Compare the book's explanations to the course materials.

Review 'Probability and Statistics for Data Science' by James D. Miller

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Expand your knowledge of probability and statistics with a dedicated textbook. This will help you understand the statistical concepts used in data science.

View Statistics for Data Science on Amazon

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Read the chapters on probability distributions and statistical inference.
Work through the examples and exercises in the book.
Relate the book's content to the course's statistics section.

Four other activities

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Practice Matrix Operations

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Reinforce your understanding of matrix operations through repetitive exercises. This will improve your speed and accuracy.

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Find practice problems online or in textbooks.
Work through problems involving matrix addition, subtraction, and multiplication.
Check your answers and review any mistakes.

Create a cheat sheet for calculus formulas

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Creating a cheat sheet will help you memorize and quickly recall important calculus formulas. This will be useful for problem-solving.

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Gather all the important calculus formulas from the course materials.
Organize the formulas into logical categories (derivatives, integrals, etc.).
Create a visually appealing and easy-to-read cheat sheet.

Create a glossary of key terms

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Compiling a glossary will help you internalize the definitions of important terms. This will improve your understanding of the course material.

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Identify key terms from each module of the course.
Write clear and concise definitions for each term.
Organize the terms alphabetically or by topic.

Implement Gaussian Elimination

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Solidify your understanding of Gaussian elimination by implementing it in code. This will give you practical experience with solving linear equations.

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Choose a programming language (e.g., Python).
Write a function that takes a matrix as input and performs Gaussian elimination.
Test your function with various matrices and compare the results to known solutions.

Career center

Learners who complete Math for Data science,Data analysis and Machine Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

A data scientist uses mathematical and statistical techniques to analyze data and extract meaningful insights. Professionals in this role build and implement models to solve business problems. This course will be particularly helpful, as it covers Linear Algebra, Statistics, Probability, Calculus, and Geometry. This course's treatment of matrices, determinants, eigenvalues, measures of central tendency, probability theorems, and calculus concepts such as derivatives and optimization directly contribute to the mathematical foundation needed to excel as a data scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A machine learning engineer develops and deploys machine learning models. These engineers require a strong understanding of mathematics and algorithms. This course covers essential mathematical concepts like Linear Algebra, Statistics, Probability, and Calculus. The course's treatment of matrices, Gaussian elimination, measures of dispersion, conditional probability, and functions are very relevant. A machine learning engineer who has taken this course will have a stronger foundation in the mathematical principles underlying many machine learning algorithms.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A data analyst collects, processes, and performs statistical analyses of data. They then report findings to stakeholders. The course covers a range of mathematical and statistical concepts that are crucial for data analysis, including Linear Algebra, measures of central tendency, measures of dispersion, and basic probability. A data analyst will find the sections on statistical data and its measurement scales, classification of data, and measures of central tendency to be especially relevant to their day-to-day tasks.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

A quantitative analyst, often working within the finance industry, uses mathematical and statistical models to analyze financial risk and investment opportunities. This course covers Linear Algebra, Statistics, Probability, and Calculus, which are fundamental to quantitative analysis. The modules on matrices, determinants, eigenvalues, measures of dispersion, conditional probability, and calculus concepts such as derivatives and optimization directly align with the mathematical tools used by a quantitative analyst.

See salaries and explore the career path for Quantitative Analyst

Business Intelligence Analyst

A business intelligence analyst examines data trends to provide insights that improve business performance. This course explores the mathematical principles behind data analysis, including Linear Algebra, Statistics, and Probability. The sections on statistical data, measures of central tendency, and basic probability will be valuable for a business intelligence analyst seeking to improve their analytical skills. The course will help build a strong mathematical base for approaching business intelligence challenges.

See salaries and explore the career path for Business Intelligence Analyst

Statistician

A statistician designs and conducts experiments, analyzes data, and interprets results to solve problems in various fields. A strong foundation in mathematics is essential. This course's coverage of Linear Algebra, Statistics, Probability, and Calculus is directly applicable to the work of a statistician. The course's treatment statistical data and its measurement scales, classification of data, measures of central tendency, measures of dispersion, and probability theorems will be very useful for a statistician.

See salaries and explore the career path for Statistician

Actuary

An actuary assesses and manages financial risks, particularly in insurance and finance. Actuaries rely heavily on mathematical and statistical modeling. This course covers key mathematical concepts, including Statistics, Probability, and Calculus, which are crucial for actuarial work. The modules on measures of central tendency, measures of dispersion, conditional probability, Baye's theorem, and calculus concepts such as derivatives and elasticity are especially relevant for aspiring actuaries.

See salaries and explore the career path for Actuary

Financial Analyst

A financial analyst analyzes financial data, prepares reports, and provides investment recommendations. Mathematical and statistical concepts are extremely relevant in this role. This course, with its coverage of Statistics, Probability, and Calculus, provides a foundation for financial analysis. The course's explanation of measures of dispersion, basic probability, and calculus concepts like average and marginal concepts are applicable to financial data analysis.

See salaries and explore the career path for Financial Analyst

Econometrician

An econometrician uses statistical methods to analyze economic data and test economic theories. Econometrics relies heavily on mathematical and statistical tools. This course's modules on Statistics, Probability, and Calculus contribute to the knowledge required of an econometrician. The modules on measures of central tendency, measures of dispersion, conditional probability, and calculus concepts such as derivatives and elasticity are relevant for econometric work.

See salaries and explore the career path for Econometrician

Research Scientist

A research scientist conducts experiments and analyzes data to advance knowledge in a specific field. This course may be useful by providing a strong mathematical foundation needed for data analysis. The sections on Linear Algebra, Statistics, and Probability, found in this course, will strengthen a research scientist's capacity to understand and interpret quantitative data. The course's discussion of statistical data, measures of central tendency, and probability will increase comfort with data-driven research.

See salaries and explore the career path for Research Scientist

Bioinformatician

A bioinformatician analyzes biological data using computational tools and statistical methods. This course may be useful for a deeper understanding of the mathematical principles underlying statistical analyses. The course's modules on Statistics and Probability, enhance a bioinformatician's analytical skills, particularly when dealing with large datasets. The sections on measures of central tendency, measures of dispersion, and probability theorems are relevant.

See salaries and explore the career path for Bioinformatician

Market Research Analyst

A market research analyst studies market conditions to examine potential sales of a product or service. This course may be helpful because it provides a foundational understanding of statistical analysis. The course's sections on Statistics and Probability, can help a market research analyst interpret survey data and identify trends. The modules on statistical data and measures of central tendency can improve the precision of market analysis.

See salaries and explore the career path for Market Research Analyst

Logistics Analyst

A logistics analyst optimizes supply chain operations using data analysis. This course may be useful by providing some familiarity with analytical tools. The course's inclusion of Statistics and Geometry could support the quantitative aspects of logistics analysis. The modules on measures of central tendency and basic geometric concepts might provide insights into improving logistical efficiency.

See salaries and explore the career path for Logistics Analyst

Teacher

A teacher educates students in a variety of subjects. This course may be helpful by deepening the teacher's understanding of essential mathematical principles. The course's exploration of Linear Algebra, Statistics, Geometry, and Calculus could allow a teacher to convey mathematical concepts with increased confidence. The modules on set theory and functions might be helpful for teachers in clarifying fundamental principles for students.

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Technical Writer

A technical writer creates documentation for technical products or services. This course may be useful by providing a conceptual understanding of the subject matter. The course's explanation of Linear Algebra, Statistics, Geometry, and Calculus could help a technical writer to accurately document data analysis and machine learning concepts. Learning about matrices and statistical measures could improve the quality of technical documentation.

See salaries and explore the career path for Technical Writer

Math for Data science,Data analysis and Machine Learning

What's inside

Syllabus

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Reviews summary

Foundations in math for data science

Activities

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