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Every data science course that I recommended in that article required that you have a decent understanding of Programming, Math, or Statistics. For example, the most famous course on ML by Andrew Ng also relies heavily on students' understanding of vector algebra and calculus. So, I decided to give in and do it all myself. I have spent the last 3 months developing a curriculum that will provide a solid foundation for your career as a I talk about this extensively in my book, and you’ll probably not be surprised by my answer based on some previous answers I gave to other questions ; ) I think the the experienced programmer is going to do better in a majority of data science job listings out there, because most tasks in data science are unglamorous data wrangling and moving it from one place to another. Then there is a growing awkward need to put models in production, and a programmer is already going to know how to do this well. This is 95-99% of useful data science work. I am going to focus on technical data jobs that require expertise in at least one programming language. You can find answers to a lot of these questions in the book Deep Learning by Ian Goodfellow and Yoshua Bengio. But that book is a bit too technical and math heavy for many.
Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Linear algebra is the branch of mathematics that studies vector spaces. You’ll see how vectors constitute vector spaces and how linear algebra applies linear transformations to these spaces. You’ll also learn the powerful relationship between sets of linear equations and vector equations, related to important data science concepts like least squares approximation. You’ll finally learn important matrix decomposition methods: eigendecomposition and Singular Value Decomposition (SVD), important to understand unsupervised learning methods like Principal Component Analysis (PCA). What are Vectors? Hence, here I present you the Foundations for Data Science or ML — First Steps to learn Data Science and ML That's me when I decided to launch.Keep in mind that, to apply a matrix to a vector, you left multiply the vector by the matrix: the matrix is on the left to the vector.
Most data roles are programming-based, except for a few like business intelligence, market analysis, product analyst, and others. The very first skill that you need to master in Mathematics is Linear Algebra, following which Statistics, Calculus, etc. come into play. We will be providing you with a structure of Mathematics that you need to learn to become a successful Data Scientist. 4 Mathematics Pillars that are required for Data Science 1. Linear Algebra & Matrix At the start of this year, I published a mind map on the Data Science learning roadmap (shown below). Many people found the roadmap useful, my article got translated into different languages, and a large number of folks thanked me for publishing it. The knowledge of this essential math is particularly important for newcomers arriving at data science from other professions: hardware engineering, retail, the chemical process industry, medicine and health care, business management, etc. Although such fields may require experience with spreadsheets, numerical calculations, and projections, the math skills required in data science can be significantly different.
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What if you hate math and tutorials out there are either too basic tutorials or too deep? Could I recommend a compact yet comprehensive course on Math and Statistics? Choosing content wasn’t easy. There are certainly topics I wish I could have included such as how to build simulations as well as optimization algorithms in more depth. But I made machine learning the end goal of the book, and to get there I guided readers through foundational topics like linear algebra, calculus, and statistics which then feed into linear regression, logistic regression, and neural networks. The “build upon” approach worked quite nicely, and areas I couldn’t get to like optimization could at least get called out as other areas to explore, and I provide tons of resources throughout the book to learn more. I made a diligent effort as well to tie in real world examples and insights, as well as pitfalls to watch out for. Inferential Statistics — A more practical and advanced branch of statistics that helps in designing hypothesis testing experiments, pushes us to understand the meaning of metrics deeply and at the same time helps us in quantifying the significance of the results. Handling multi-dimensional arrays, indexing, slicing, transposing, broadcasting and pseudorandom number generation using NumPy.
A good way to understand the relationship between matrices and linear transformations is to actually visualize these transformations. To do that, you’ll use a grid of points in a two-dimensional space, each point corresponding to a vector (it is easier to visualize points instead of arrows pointing from the origin).Essential Math for Data Science" by Thomas Nield is a fantastic resource for individuals looking to strengthen their mathematical foundation in data science and machine learning. It is well-structured, and the author's practical approach makes complex concepts more accessible. The book is an excellent supplementary resource, but some areas have room for additional depth. I found it a valuable refresher and a source of inspiration for explaining mathematical concepts. On a scale of 1 to 10, I rate it a solid 9. Chapter 5: Linear Regression The chapter on linear regression is well-structured and covers key aspects, including finding the best-fit line, correlation coefficients, and prediction intervals. Including stochastic gradient descent is a valuable addition, providing readers with a practical understanding of the topic. Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Data Visualization using Matplotlib — the API hierarchy, how to add styles, color, and markers to a plot, knowledge of various plots and when to use them, line plots, bar plots, scatter plots, histograms, boxplots, and Seaborn for more advanced plotting. 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.
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