Upskill yourself with Coursera this International Day of Mathematics
Emerging technologies are reshaping industries, which has impacted workforce requirements as more and more companies are recruiting professionals with niche and specialized skills. A 2020 McKinsey & Company survey discovered that the financial services, telecom, and high tech industries would face the most disruption from skills gaps. The essential skills that will remain in high demand depend on mathematical and advanced data-analysis abilities. Even in-demand skills like Coding and Data Science require a solid foundation of basic math skills as machine learning algorithms, and performing analyses and discovering insights from data need a thorough foundation in mathematics.
Upskilling and reskilling in Mathematics and numbers boost a learner’s creative and deductive thinking skills. On this International Day of Mathematics, Coursera brings to you some popular intermediate-level courses in Mathematics for upskilling and reskilling to aid your career advancement journey :
Introduction to Mathematical Thinking by Stanford University
This course helps learners develop that crucial way of thinking, like how mathematicians do with the help of a robust cognitive process developed over thousands of years. The key to success in mathematical thinking is to think outside-the-box, which is a valuable ability in today’s world.
Differential Calculus through Data and Modeling Specialization by John Hopkins University
This specialization introduces single and multivariable calculus topics and focuses on using calculus to address questions in the natural and social sciences. Students will learn to use the tools of calculus to process, analyze, and interpret data and to communicate meaningful results using scientific computing and mathematical modeling.
Analytic Combinatorics by Princeton University
Through this course, students will learn calculus that enables precise quantitative predictions of large combinatorial structures. It introduces the symbolic method to derive functional relations among ordinary, exponential, and multivariate generating functions, and methods in complex analysis for deriving accurate asymptotics from the GF equations.
Mathematics for Machine Learning: PCA by Imperial College London
This course introduces learners to the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. It covers some basic statistics of data sets, such as mean values and variances.
Statistical Modeling for Data Science Applications Specialization by University of Colorado Boulder
Through this specialization, learners will be able to add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.