Using Python, learn statistical and probabilistic approaches to understand and gain insights from data.
This advanced-level course, "Probability and Statistics for Data Science," is part of the Data Science MicroMasters program offered by UCSanDiegoX. It is designed to equip students with the fundamental knowledge and practical skills in probability and statistics essential for data science. The course focuses on teaching students how to reason about uncertainty in noisy datasets, a crucial skill for data scientists. Through a combination of theoretical learning and hands-on experience using Jupyter notebooks, students will gain a deep understanding of the mathematical foundations that underpin machine learning and data analysis techniques.
The skills acquired in this course are directly applicable to various real-world scenarios:
By mastering the concepts taught in this course, learners will be well-equipped to tackle real-world data science challenges across various industries, making them valuable assets in the increasingly data-driven job market.
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This lecture series discusses how the concept of probability can be used to handle, control, and exploit uncertainty in the real-world. It is an undergraduate-level lecture series on probability, but is entirely different from the usual courses on probability theory. The lectures cover the basics of probability theory including the relevant mathematics, but instead of focusing on mathematics, the lectures explain how probability theory can help understand real-world uncertainty using various examples. The examples are used to describe how uncertainty can be exploited to implement modern randomized algorithms such as Markov chain Monte Carlo and deep learning.
An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.
'Statistical and Probabilistic Foundations of AI' provides an accessible overview of the mathematics and statistics behind fundamental concepts of data science, machine learning, and artificial intelligence. It covers descriptive and exploratory data analysis and a brief introduction to inferential statistics. It provides the principles of probability necessary to understand the methods used in inferential statistics and machine learning at an introductory level.
The first in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
"Basics of Data Science" gives a comprehensible overview of many fundamental concepts and tools of data science, including data quality and data preprocessing, supervised and unsupervised learning techniques including their evaluation, frequent itemsets and association rules, sequence mining, process mining, text mining, and responsible data science.
