Probability and Statistics in Data Science using Python
In this Data Science Micro-Masters program, you will be able to learn the foundations of probability and statistics along with the mathematical theory.
About Probability and statistics
Probability and statistics are the branches of mathematics concerned with the laws governing random events, including the collection, analysis, interpretation, and display of numerical data and it is now an indispensable tool of both social and natural sciences.
What is Data Science?
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science professionals apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value.
Why Data Science is Important?
In this era more and more companies are coming to realize the importance of data science, artificial intelligence, and machine learning. Regardless of the type of industries or size, organizations that wish to remain competitive in the age of big data need to efficiently and comprehensively develop and implement data science capabilities or risk being left behind.
“Big data is at the foundation of all the megatrends that are happening.”
– By Chris Lynch, American Writer of Books
What is the Python programming language?
In technical terms, Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options.
“The joy of coding Python should be in seeing short, concise, readable classes that express a lot of action in a small amount of clear code not in reams of trivial code that bores the reader to death”.
— Guido van Rossum
About this Probability and Statistics Micromasters Program
In this course, part of the Data Science Micro Masters program, you will be able to learn the foundations of probability and statistics. Also, you will learn both the mathematical theory and get a hands-on experience of applying this theory to actual data using Jupyter notebooks.
Upon completion of this course, you will have cleared the following concepts such as random variables, dependence, correlation, regression, PCA, entropy, and MDL.
Who can take this course?
Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba, and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to you in these countries and regions, the licenses you have received are not broad enough to allow you to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
What you will learn from this course?
- The mathematical foundations for machine learning.
- Statistics literacy: understand the meaning of statements such as “at a 99% confidence level.”
About the instructors:
Alon Orlitsky
–Professor, Electrical and Computer Engineering at UC San Diego
Alon Orlitsky is a Professor at UCSD’s ECE Department. He received his B.Sc. in Math and Electrical Engineering from Ben Gurion University, and his M.Sc. and Ph.D. in Electrical Engineering from Stanford University.
Yoav Freund
–Professor of Computer Science and Engineering at UC San Diego
Dr. Freund is a Professor of Computer Science and Engineering in the University of California San Diego. He and Dr. Robert Schapire have invented the Adaboost learning algorithm for which they received the Kannelakis Prize and the Godel Prize.
Syllabus of the Probability and Statistics Micromasters
In this course you will cover the following topics:
- Introduction to Probability and Statistics.
- Sets.
- Counting.
- Combinatorics.
- Probability Introduction.
- Conditional Probability.
- Random Variables, Expectations, and Variance.
- Discrete Distribution Families.
- Inequalities and limit Theorems.
- Statistics, Parameter Estimation, and Confidence interval.
- Regression and PCA.
- Hypothesis Testing.
Prerequisites for the Probability and Statistics Micromasters:
- The previous course in the Micro-Masters program: DSE200x
- One should have undergraduate level education in:
- Multivariate calculus.
- Linear algebra.
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Description
About Probability and statistics
Probability and statistics are the branches of mathematics concerned with the laws governing random events, including the collection, analysis, interpretation, and display of numerical data and it is now an indispensable tool of both social and natural sciences.
What is Data Science?
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science professionals apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value.
Why Data Science is Important?
In this era more and more companies are coming to realize the importance of data science, artificial intelligence, and machine learning. Regardless of the type of industries or size, organizations that wish to remain competitive in the age of big data need to efficiently and comprehensively develop and implement data science capabilities or risk being left behind.
“Big data is at the foundation of all the megatrends that are happening.”
– By Chris Lynch, American Writer of Books
What is the Python programming language?
In technical terms, Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options.
“The joy of coding Python should be in seeing short, concise, readable classes that express a lot of action in a small amount of clear code not in reams of trivial code that bores the reader to death”.
— Guido van Rossum
About this Probability and Statistics Micromasters Program
In this course, part of the Data Science Micro Masters program, you will be able to learn the foundations of probability and statistics. Also, you will learn both the mathematical theory and get a hands-on experience of applying this theory to actual data using Jupyter notebooks.
Upon completion of this course, you will have cleared the following concepts such as random variables, dependence, correlation, regression, PCA, entropy, and MDL.
Who can take this course?
Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba, and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to you in these countries and regions, the licenses you have received are not broad enough to allow you to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
What you will learn from this course?
- The mathematical foundations for machine learning.
- Statistics literacy: understand the meaning of statements such as “at a 99% confidence level.”
About the instructors:
Alon Orlitsky
–Professor, Electrical and Computer Engineering at UC San Diego
Alon Orlitsky is a Professor at UCSD’s ECE Department. He received his B.Sc. in Math and Electrical Engineering from Ben Gurion University, and his M.Sc. and Ph.D. in Electrical Engineering from Stanford University.
Yoav Freund
–Professor of Computer Science and Engineering at UC San Diego
Dr. Freund is a Professor of Computer Science and Engineering in the University of California San Diego. He and Dr. Robert Schapire have invented the Adaboost learning algorithm for which they received the Kannelakis Prize and the Godel Prize.
Syllabus of the Probability and Statistics Micromasters
In this course you will cover the following topics:
- Introduction to Probability and Statistics.
- Sets.
- Counting.
- Combinatorics.
- Probability Introduction.
- Conditional Probability.
- Random Variables, Expectations, and Variance.
- Discrete Distribution Families.
- Inequalities and limit Theorems.
- Statistics, Parameter Estimation, and Confidence interval.
- Regression and PCA.
- Hypothesis Testing.
Prerequisites for the Probability and Statistics Micromasters:
- The previous course in the Micro-Masters program: DSE200x
- One should have undergraduate level education in:
- Multivariate calculus.
- Linear algebra.
Note: Your review matters
If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.
FAQ
Specification:
- EDX
- University of California, San Diego
- Online Course
- Self-paced
- Advanced
- 1-3 Months
- Free Course (Affordable Certificate)
- English
- Data Analysis Data Science Data Science with 'Python'
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