Learn the math required for data science and analytics and gain an advantage in business in this free online course. If your goal is to be a data scientist, analyst or engineer, then some level of mathematical ability is an absolute must. This course covers fundamental linear algebra, probability, and statistics relevant to applying math in data science and the real world.
| College | Alison |
| Course ID | 195967 |
| Course Type | Online Learning |
| Course Qualification | CPD Certificate |
| Course Duration | 5-6 hours |
| Course Fee | Free |
| Entry Requirements | Like all courses on the Alison Free Learning platform this is a free, CPD-accredited course. A Graduate can choose to buy a certificate or diploma upon successful completion of a course, but this is not required or necessary. At Alison we believe that free education, more than anything, has the power to break through boundaries and transform lives. Alison is the world’s largest free online empowerment platform for education and skills training, offering over 5000 CPD accredited courses and a range of impactful career development tools. It is a for-profit social enterprise dedicated to making it possible for anyone, to study anything, anywhere, at any time, for free online, at any subject level. Through our mission, we are a catalyst for positive social change, creating opportunity, prosperity, and equality for everyone. |
| Career Path | What You Will Learn In This Free Course • Discuss the different forms of a linear equation • Arrange a system of linear equations into augmented matrix form • Solve a system of linear equations using REF or R-REF • Define the idea of a vector space and name the two vector spaces introduced during the course • Explain how linear independence and span are related • Compute a least-squares solution via pseudoinverse • Recall the three elements of a probability model • Distinguish between a PMF and PDF • Compare covariance and variance • Contrast the law of large numbers and the central limit theorem • Describe the steps involved in computing a joint PDF • Indicate how to find the mean squared error using the Bayesian estimator |
| Course Code | 4246 |
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