If you are interested in pursuing a career in Data Science, Data Analysis, Machine Learning or Artificial intelligence, learning Math essentials is a must. The course titled Math for Data science, Data analysis and Machine Learning offers you the perfect opportunity to learn about Linear Algebra, Statistics and Probability, Calculus and Geometry, and their importance in these technological areas. With more than 20 years of experience and a great reputation, this course is designed to cater to both beginners and advanced level students.
What to Expect?
The Math for Data science, Data analysis and Machine Learning course is designed to help you develop a comprehensive understanding of the subject. The course is structured in such a way that it is useful not only for engineers and commerce students but also for students of Computer science and artificial intelligence and those learning Python programming.
The course covers all essential areas of Math that are relevant to Data Science, Data Analysis, Machine Learning and Artificial intelligence. The topics range from the basics of Linear Algebra, such as types of matrices, addition, multiplication, determinants, and more, to more advanced Calculus topics like limits and continuity, derivatives of functions, Rolle’s theorem, and Lagrange’s mean value theorem. The course also covers essential Statistics and Probability topics such as classification of data, measures of central tendency, dispersion, and conditional probability. Lastly, the course covers Euclidean Geometry and Set Theory, which are also essential for getting a comprehensive understanding of mathematics.
Course Content - An In-Depth Look
The course is carefully crafted and as mentioned before covers several essential topics. Here is an in-depth look at the content:
Linear Algebra Basics
This section covers the basics of Linear Algebra. It starts with an introduction to Matrices and their types. It then covers properties such as addition, multiplication, transpose, determinants, and more. Lastly, it covers concepts like Rank of a Matrix, Eigenvalues and Eigenvectors and Gaussian Elimination Method, which is used for finding out the solution of linear equations.
Statistics and Probability Basics
This section covers the basics of Statistics and Probability. It starts with an introduction to Statistical Data and its measurement scales. It covers the classification of data and then moves on to cover measures of central tendency, measures of dispersion like range, mean deviation, Standard deviation, and Quartile Deviation. It then covers Basic Concepts of Probability, Sample Space, and Verbal description & Equivalent Set Notations, Types of Events, Addition Theorem of Probability, Conditional Probability, Total Probability Theorem, and Bayes Theorem, among others.
Calculus Basics
This section covers the basics of Calculus. It starts with a basic introduction to Functions, Limits, and Continuity. It covers the Derivative of a Function and Formulae of Differentiation. This section also includes Differentiation of Functions in Parametric Form, Rolle's Theorem, Lagrange's Mean Value Theorem, Average and Marginal Concepts, Concepts of Maximum and Minimum, and finally covers Elasticity, including Price Elasticity of supply and demand.
Euclidean Geometry Basics
In this section, you will learn about Euclidean Geometry. It Includes an introduction to Geometry, Some useful Terms, Concepts, Results, and Formulae, Set Theory, its definition, and representation. This section covers Types of Sets, Subsets, Power Set, and Universal Set, Intervals as subsets of 'R', Venn Diagrams, Laws of Algebra of Sets, Important formulae of the number of elements in sets, Basic Concepts of Functions, Graphs of real-valued functions, and Graphs of Exponential, Logarithmic and Reciprocal Functions.
The Benefits of the Course
The Math for Data science, Data analysis and Machine Learning course is an excellent platform for students wanting to pursue a career in these areas. Students from a variety of backgrounds will benefit from this course as the lessons are presented in a simple yet comprehensive way. The course covers all the essential topics required for these fields, making it a one-stop-shop for all your Math essentials.
Throughout the course, you will be provided with multiple examples to supplement your understanding of each topic. The course is also updated regularly based on student feedback, ensuring the content stays up-to-date and meets the requirements of today's fast-paced technological environment.
Student Reviews and Ratings
With a rating aggregate of 4.66317 and 194 reviews, it’s no surprise that the Math for Data science, Data analysis and Machine Learning course is amongst the top-rated courses in this field. Many students praise the course for its comprehensive content, while others commend the course's design that makes it suitable for both beginners and advanced level students. The course is also appreciated for its simplicity and ease of understanding, making difficult concepts simple to understand and apply.
Join the Course Now!
The Math for Data science, Data analysis and Machine Learning course is undoubtedly an excellent opportunity for students to develop a comprehensive understanding of Math topics related to Data Science, Data Analysis, Machine Learning, and Artificial Intelligence. The course's comprehensive content and examples are designed to provide an immersive learning experience. Furthermore, the course's regular updates ensure the content is current, making it relevant to the demands of today's ever-evolving technological environment.
If you're ready to take your Math skills to the next level, join the Math for Data science, Data analysis and Machine Learning course right away!
