Free Download Mathematical Foundations for Data Science and Analytics Specialization | Coursera
Released 8/2025
By Morgan Frank - University of Pittsburgh
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 68 Lessons ( 12h 33m ) | Size: 2.2 GB
Master Mathematical Foundations for Data Science. Gain Advanced Skills in Linear Algebra, Calculus, Probability, and Regression Analysis
What you'll learn
Perform vector and matrix arithmetic using NumPy for data science.
Calculate expected values and apply normal distribution for analysis.
Perform derivatives and integrals for optimization in data science.
Apply probability theory and regression methodologies with Python.
Skills you'll gain
Mathematics and Mathematical Modeling
Machine Learning
Predictive Modeling
Probability Distribution
Applied Mathematics
Statistics
Probability
NumPy
Linear Algebra
Matplotlib
Pandas (Python Package)
Statistical Modeling
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.
Applied Learning Project
Engage in practical assignments and real-world projects to apply mathematical methods. Gain hands-on experience in vector arithmetic, solving linear systems, calculating probabilities, and performing regression analysis using Python. These projects will deepen your understanding of how to apply mathematical principles to real-world data scenarios.
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