Mathematics is crucial in data science as it underpins algorithms and models used for data analysis and prediction. It helps understand data patterns, optimize solutions, and make informed decisions. Learning math is, therefore, essential for mastering statistical methods, machine learning techniques, and effective problem-solving in data science. This article lists the top courses on mathematics for data science that provide comprehensive knowledge and skills in areas like calculus, linear algebra, probability, and statistics, equipping you to excel in the data science field.
Mathematics for Machine Learning and Data Science Specialization
This course, created by DeepLearning.AI, covers essential math for machine learning using Python programming. It includes hands-on labs, and visualizations and covers topics like vector and matrix algebra, linear transformations, PCA, gradient descent, probability distributions, and statistical methods.
Introduction to Statistics
This course teaches essential statistical concepts for analyzing data and communicating insights. It covers topics like descriptive statistics, probability, regression, hypothesis testing, and advanced methods like Monte Carlo and Bootstrap.
Intro to Statistics
This beginner course offers a comprehensive introduction to data analysis, visualization, and statistical concepts. It covers topics from basic charts and probability to hypothesis testing and regression, with optional programming exercises.
Linear algebra
This course by Khan Academy covers vectors, spaces, and matrices, focusing on solving systems, linear transformations, and matrix operations. It explores orthogonal projections, changes of basis, and the Gram-Schmidt process, concluding with eigenvalues and eigenvectors.
Statistics: Unlocking the World of Data
This introductory course covers the key principles of statistics, helping learners analyze and interpret everyday data using interactive applets. No prior knowledge of statistics is needed, but secondary school mathematics is advisable. The course equips learners to perform and interpret simple statistical analyses.
Intro to Inferential Statistics
This course, “Intro to Inferential Statistics,” covers hypothesis testing, t-tests, ANOVA, correlation, and regression. It includes problem sets, a final project, and a Google Spreadsheet tutorial, with no prior experience required. This course is for learning to make predictions based on statistical data.
Data Science Math Skills
This course teaches the basic math skills needed for data science, covering set theory, real numbers, functions, derivatives, exponents, logarithms, and probability theory. It is designed for learners with basic math skills and prepares them for advanced topics in data science. Key concepts include graphing, calculus, and Bayes’ theorem.
Multivariable Calculus
This course by Khan Academy introduces multivariable calculus, covering topics like visualizing and differentiating multivariable functions, applications of derivatives, and integrating multivariable functions. It also delves into advanced theorems such as Green’s, Stokes’, and the divergence theorems.
Mathematical Methods for Data Analysis
This intermediate course covers mathematical methods for data analysis, including vector spaces, Fourier analysis, and machine learning algorithms. It features case studies on clustering, regression, and classification.
Advanced Statistics for Data Science Specialization
This course, “Advanced Statistics for Data Science Specialization,” covers fundamental concepts in probability, statistics, and linear models, starting with biostatistics and progressing to advanced linear models using R. It includes rigorous quizzes and requires basic calculus and linear algebra. Key topics include least squares, linear regression, and hypothesis testing.
Expressway to Data Science: Essential Math Specialization
This course teaches foundational mathematics critical for Data Science, including algebra, calculus, linear algebra, and numerical analysis. It prepares learners for advanced studies, specifically CU Boulder’s Master of Science in Data Science program.
Data Analysis: Statistical Modeling and Computation in Applications
This advanced MITx course teaches data science through statistical and computational tools, focusing on real data analysis in areas like epigenetics, criminal networks, economics, and environmental data. It includes hypothesis testing, regression, network analysis, and time series modeling. Prerequisites include Python programming, calculus, linear algebra, probability, and machine learning.
Statistics with Python Specialization
This course teaches beginning and intermediate statistical analysis using Python, covering data collection, design, management, exploration, and visualization. It includes assignments and quizzes in the Jupyter Notebook environment to apply concepts like confidence intervals, hypothesis testing, and statistical modeling. Key skills include data visualization, statistical inference, and Python programming.
Mathematics for Machine Learning Specialization
This course bridges the gap in mathematical understanding for Machine Learning and Data Science, covering Linear Algebra, Multivariate Calculus, and PCA. It includes interactive Python projects to apply concepts like eigenvectors, gradient descent, and data compression.
Bayesian Statistics Specialization
This course teaches Bayesian statistics, covering concepts from basic probability to advanced topics like MCMC and time series analysis. It includes four courses on Bayesian methods, R programming, and statistical modeling, culminating in a project to apply skills to real-world data. Key skills include Bayesian inference, dynamic linear modeling, and forecasting.
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