Mathematical Foundations for Data Science and Analytics
About this course
Elevate your data science skills with our "Mathematical Foundations for Data Science and Analytics" specialization. This comprehensive program includes three courses: Linear Algebra and Regression for Data Science, Statistics and Calculus Methods for Data Analysis, and Probability Theory and Regression for Predictive Analytics. Start with Linear Algebra and Regression for Data Science. Master vector arithmetic, matrix operations, and eigen calculations using Python’s NumPy library. Learn to solve linear equations and implement ordinary least squares (OLS) regression to fit models and predict trends. Progress to Statistics and Calculus Methods for Data Analysis. Calculate expected values and apply the normal distribution to statistical analysis. Perform derivative and integral calculations for optimization and data analysis. Finally, explore Probability Theory and Regression for Predictive Analytics. Learn conditional probability and Bayes' Theorem for inference. Understand probability distributions and apply regression techniques, including logistic and Lasso regression, to analyze data trends. Engage in practical assignments and projects to apply mathematical methods to data problems. Gain hands-on experience with Python, preparing you for advanced data science and analytics.
90/100
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- What the provider tells you
- 39/45
- Who stands behind it
- 35/35
- How complete the listing is
- 16/20
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What you'll learn
- master vector arithmetic and matrix operations
- implement ordinary least squares (OLS) regression
- calculate expectations and apply the normal distribution
- perform derivative and integral calculations
- understand conditional probability and Bayes' Theorem
- apply logistic and Lasso regression techniques
Course objectives
- to develop a strong understanding of mathematical methods used in data science
- to apply theoretical concepts through practical assignments and projects
- to prepare students for advanced data science roles
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