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Udemy's e-learning platform might be the only personal development training you need.

ZDNet

Tech is among the fastest-moving, fastest-changing fields in the modern world. Even for the savviest of tech brainiacs, there's too much happening too fast to stay on top of it all. That's why it's important to identify the places you can turn to in order to learn all that late-breaking information and get up to speed when a new opportunity presents itself. Udemy can be that place for tech-focused achievers. After 15 years as one of the premier locations on the web for personal development, Udemy is now home to over 183,000 video courses covering virtually any business growth area possible.


Online Education Is Bridging the Skill Gap Brought Upon Us by Industry 4.0

#artificialintelligence

Back in the 1990s or even 2000s if you wanted to learn something new you had to physically attend classes in the brick-and-mortar institutions. The courses were few and generic and the model was simple; Enroll, pay the fees, attend classes, appear in exams, get your degree or diploma and walk out. But, those were the old days. The dynamite of the fourth industrial revolution and technology disruption blew this framed system of generic courses into a jillion skills and specializations fragments, each unique in its form and composition. Generalized courses were tossed out of the employment window and employers became nit-picky on the kind of skill they needed.


Ranking Countries and Industries by Tech, Data, and Business Skills

#artificialintelligence

The pace of technological change is rendering many job activities -- and the skills they require -- obsolete. Research by McKinsey suggests that globally more than 50% of the workforce is at risk of losing their jobs to automation, and a survey by the World Economic Forum suggests that 42% of the core job skills required today will change substantially by 2022. In this landscape of constant disruption, individuals, companies, and governments are fighting to ensure they have the skills to remain competitive. To shed light on the global skills landscape, Coursera recently released the first edition of our Global Skills Index (GSI) report. As the world's largest platform for higher education, Coursera brings together 40 million learners around the world with over 3,000 courses from leading universities and companies.


Data Science Math Skills Coursera

@machinelearnbot

Data science courses contain math--no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. 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. Topics include: Set theory, including Venn diagrams Properties of the real number line Interval notation and algebra with inequalities Uses for summation and Sigma notation Math on the Cartesian (x,y) plane, slope and distance formulas Graphing and describing functions and their inverses on the x-y plane, The concept of instantaneous rate of change and tangent lines to a curve Exponents, logarithms, and the natural log function.


Data Science Math Skills Coursera

@machinelearnbot

About this course: Data science courses contain math--no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. 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. Topics include: Set theory, including Venn diagrams Properties of the real number line Interval notation and algebra with inequalities Uses for summation and Sigma notation Math on the Cartesian (x,y) plane, slope and distance formulas Graphing and describing functions and their inverses on the x-y plane, The concept of instantaneous rate of change and tangent lines to a curve Exponents, logarithms, and the natural log function.