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Dr. Srijith Rajamohan
The objective of this course is to introduce Computational Statistics to aspiring or new data scientists. The attendees will start off by learning the basics of probability, Bayesian modeling and inference. This will be the first course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
Read more
The objective of this course is to introduce Computational Statistics to aspiring or new data scientists. The attendees will start off by learning the basics of probability, Bayesian modeling and inference. This will be the first course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
Read more
The objective of this course is to introduce Computational Statistics to aspiring or new data scientists. The attendees will start off by learning the basics of probability, Bayesian modeling and inference. This will be the first course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
The instructors for this course will be Dr. Srijith Rajamohan and Dr. Robert Settlage.
Register for this course and see more details by visiting:
OpenCourser.com/course/kge58f/introduction
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What's inside
Syllabus
Environment Setup
Introduction to the compute environment for the Specialization. The users will be introduced to the Databricks Ecosystem for Data Science. The users can also deploy the notebooks to Binder for setup-free access.
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Syllabus
Environment Setup
Introduction to the compute environment for the Specialization. The users will be introduced to the Databricks Ecosystem for Data Science. The users can also deploy the notebooks to Binder for setup-free access.
Read more
Introduction to the Fundamentals of Probability
In this module, you will learn the foundations of probability and statistics. The focus is on gaining familiarity with terms and concepts.
A Hands-On Introduction to Common Distributions
Tis module will be an introduction to common distributions along with the Python code to generate, plot and interact with these distributions. You will also learn how to perform Maximum Likelihood Estimation (MLE) for various distributions and Kernel Density Estimation (KDE) for non-parametric distributions.
Sampling Algorithms
This module introduces you to various sampling algorithms for generating distributions. You will also be introduced to Python code that performs sampling.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Well-suited for aspiring or new data scientists seeking to strengthen their knowledge in computational statistics
Python and Jupyter notebooks are utilized for practical implementation and illustration of Bayesian modeling, enhancing the hands-on learning experience
Taught by experienced instructors in the field, providing valuable insights and a strong foundation in computational statistics
Covers fundamental concepts of probability, Bayesian modeling, and inference, essential knowledge for data scientists
Incorporates sampling algorithms, a critical aspect of computational statistics, and provides practical Python implementations for sampling
Requires a basic understanding of statistics and probability, which may require additional preparation for some learners
Save this course
Save Introduction to Bayesian Statistics to your list so you can find it easily later:
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Reviews summary
Introduction to bayesian statistics: <span>clear</span>, <span>comprehensive</span>, <span>practical</span>
Learners say this well-structured course offers a comprehensive and practical introduction to Bayesian statistics. While some wish for more interactivity, they feel the course's clear explanations and real-world examples make it suitable for both beginners and those with some prior knowledge. The course covers a wide range of topics, from basic concepts to advanced methods, equipping learners to tackle complex problems in the future. However, some note that the teaching style can be tedious and that there are unacceptable number of typos and grammatical errors in the accompanying notebooks.
Focuses on practical application of Bayesian statistics.
"Introduction to Bayesian Statistics - A Comprehensive Overview"
"Rather an easy course to follow."
"Good introduction and background."
Explains complex concepts clearly.
"Clear explanations with lots of small examples to illustrate the material."
"Amazing Course"
"The notes and codes are very good!!"
Covers a broad range of topics, from beginner to advanced concepts.
"The "Introduction to Bayesian Statistics" provides a thorough and accessible introduction to the fundamental concepts and techniques of Bayesian statistics."
"Clear explanations with lots of small examples to illustrate the material."
"The "Introduction to Bayesian Statistics" is a valuable resource for anyone looking to gain a solid understanding of Bayesian statistics."
Interactive elements and a proofread of accompanying notebooks would enhance the course.
"It's very obvious from the alarming number of mistakes that none of the content was proofread before publishing."
"With a few added interactive elements, it has the potential to be even more effective in facilitating learning."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Introduction to Bayesian Statistics with these
activities:
Practice Python coding
Show steps
Solidify your understanding of Python basics, variables, loops, and functions.
Browse courses on
Python
Show steps
-
Set up a Python development environment
-
Run examples of common Python programming tasks:
-
Create a simple script that reads and writes a file
Learn basic probability concepts
Show steps
Reinforce understanding of probability theory and practice applying it to real-world scenarios.
Browse courses on
Probability
Show steps
-
Review basic probability theory
-
Solve probability problems using Bayes' Theorem
-
Calculate probabilities for different events
Explore Jupyter Notebook tutorials
Show steps
Become comfortable with using Jupyter Notebooks for data analysis and visualization.
Browse courses on
Jupyter Notebook
Show steps
-
Find Jupyter Notebook tutorials online
-
Follow the tutorials to create your own notebooks
Four other activities
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Show all seven activities
Join online forums or discussion groups
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Engage with other learners to clarify concepts, share insights, and provide support.
Show steps
-
Identify relevant online forums or discussion groups
-
Participate in discussions by sharing your knowledge and asking questions
Practice statistical inference
Show steps
Develop proficiency in statistical inference methods and apply them to analyze data.
Browse courses on
Statistical Inference
Show steps
-
Learn about hypothesis testing and confidence intervals
-
Practice performing hypothesis tests using real-world data
-
Calculate confidence intervals for different parameters
Build a simple Bayesian model
Show steps
Gain hands-on experience in building and evaluating Bayesian models using Python.
Browse courses on
Bayesian Modeling
Show steps
-
Choose a dataset and define a model
-
Implement the model in Python using PyMC3 or another library
-
Run the model and analyze the results
Contribute to open-source projects
Show steps
Enhance understanding of Bayesian modeling and contribute to the open-source community.
Browse courses on
Open-Source
Show steps
-
Identify open-source libraries or projects related to Bayesian modeling
-
Review the project's code and documentation
-
Propose and implement improvements or bug fixes
Practice Python coding
Show steps
Solidify your understanding of Python basics, variables, loops, and functions.
Browse courses on
Python
Show steps
Show steps
Show steps
- Set up a Python development environment
- Run examples of common Python programming tasks:
- Create a simple script that reads and writes a file
Learn basic probability concepts
Show steps
Reinforce understanding of probability theory and practice applying it to real-world scenarios.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review basic probability theory
- Solve probability problems using Bayes' Theorem
- Calculate probabilities for different events
Explore Jupyter Notebook tutorials
Show steps
Become comfortable with using Jupyter Notebooks for data analysis and visualization.
Browse courses on
Jupyter Notebook
Show steps
Show steps
Show steps
- Find Jupyter Notebook tutorials online
- Follow the tutorials to create your own notebooks
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join online forums or discussion groups
Show steps
Engage with other learners to clarify concepts, share insights, and provide support.
Show steps
Show steps
Show steps
- Identify relevant online forums or discussion groups
- Participate in discussions by sharing your knowledge and asking questions
Practice statistical inference
Show steps
Develop proficiency in statistical inference methods and apply them to analyze data.
Browse courses on
Statistical Inference
Show steps
Show steps
Show steps
- Learn about hypothesis testing and confidence intervals
- Practice performing hypothesis tests using real-world data
- Calculate confidence intervals for different parameters
Build a simple Bayesian model
Show steps
Gain hands-on experience in building and evaluating Bayesian models using Python.
Browse courses on
Bayesian Modeling
Show steps
Show steps
Show steps
- Choose a dataset and define a model
- Implement the model in Python using PyMC3 or another library
- Run the model and analyze the results
Contribute to open-source projects
Show steps
Enhance understanding of Bayesian modeling and contribute to the open-source community.
Browse courses on
Open-Source
Show steps
Show steps
Show steps
- Identify open-source libraries or projects related to Bayesian modeling
- Review the project's code and documentation
- Propose and implement improvements or bug fixes
Career center
Learners who complete Introduction to Bayesian Statistics will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
A Statistician designs, conducts, and interprets statistical studies, analyzing and interpreting data to provide insights. This course provides a foundation in probability and Bayesian modeling, which are essential skills for Statisticians. By understanding how to model and infer from data, learners can make informed decisions and draw meaningful conclusions from complex datasets.
Data Analyst
Data Analyst
A Data Analyst collects, processes, and analyzes data to identify trends and patterns. This course provides a solid foundation in probability and Bayesian statistics, which are essential for understanding data and making accurate predictions. By mastering these concepts, learners can develop the analytical skills needed to succeed as a Data Analyst.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. This course provides a foundation in Bayesian modeling, which is a powerful approach for building and evaluating machine learning models. By understanding how to model and infer from data, learners can develop the skills needed to create accurate and reliable machine learning solutions.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to biological and medical data. This course provides a foundation in probability and Bayesian statistics, which are essential for understanding and analyzing biological data. By mastering these concepts, learners can develop the skills needed to make informed decisions and draw meaningful conclusions from medical studies.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst (Quant) uses mathematical and statistical models to analyze financial data. This course provides a foundation in Bayesian modeling, which is a powerful approach for modeling and predicting financial data. By understanding how to model and infer from data, learners can develop the skills needed to succeed as a Quant.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical methods to improve the efficiency and effectiveness of organizations. This course provides a foundation in probability and Bayesian modeling, which are essential for understanding and analyzing operational data. By mastering these concepts, learners can develop the skills needed to make informed decisions and improve organizational performance.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. This course provides a foundation in Bayesian modeling, which is a powerful approach for modeling and inferring from data. By understanding how to model and infer from data, learners can develop the skills needed to build data-driven solutions and make informed decisions.
Insurance Analyst
Insurance Analyst
An Insurance Analyst uses statistical methods to assess risk and determine insurance premiums. This course provides a foundation in probability and Bayesian modeling, which are essential for understanding and analyzing insurance data. By mastering these concepts, learners can develop the skills needed to make informed decisions and manage risk effectively.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and patterns of disease in populations. This course provides a foundation in probability and Bayesian modeling, which are essential for understanding and analyzing epidemiological data. By mastering these concepts, learners can develop the skills needed to identify and prevent the spread of disease.
Market Researcher
Market Researcher
A Market Researcher conducts surveys and analyzes data to understand consumer behavior. This course provides a foundation in probability and Bayesian modeling, which are essential for designing and analyzing surveys and for understanding and interpreting consumer behavior. By mastering these concepts, learners can develop the skills needed to conduct effective market research and make informed decisions.
Actuary
Actuary
An Actuary uses mathematical and statistical methods to assess risk and determine insurance premiums. This course provides a foundation in probability and Bayesian modeling, which are essential for understanding and analyzing actuarial data. By mastering these concepts, learners can develop the skills needed to make informed decisions and manage risk effectively.
Economist
Economist
An Economist studies the production, distribution, and consumption of goods and services. This course provides a foundation in probability and Bayesian modeling, which are essential for understanding and analyzing economic data. By mastering these concepts, learners can develop the skills needed to make informed decisions and analyze economic trends.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations. This course provides a foundation in probability and Bayesian modeling, which are essential for understanding and analyzing financial data. By mastering these concepts, learners can develop the skills needed to make informed investment decisions.
Risk Manager
Risk Manager
A Risk Manager identifies, assesses, and manages risk. This course provides a foundation in probability and Bayesian modeling, which are essential for understanding and analyzing risk data. By mastering these concepts, learners can develop the skills needed to make informed decisions and manage risk effectively.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course provides a foundation in probability and Bayesian modeling, which are essential for understanding and analyzing software systems. By mastering these concepts, learners can develop the skills needed to build and maintain robust and reliable software systems.
For more career information including salaries, visit:
OpenCourser.com/course/kge58f/introduction
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Bayesian Statistics.
Comprehensive introduction to Bayesian data analysis, providing both theoretical foundations and practical applications. It is commonly used as a textbook for graduate-level courses in Bayesian statistics and is highly recommended for students who want to gain a deeper understanding of the subject.
Provides a hands-on introduction to Bayesian statistics using R and Stan. It is written for students with a background in introductory statistics and great resource for those who want to learn more about Bayesian modeling.
Practical guide to Bayesian data analysis using R, JAGS, and Stan. It is written for researchers and students who want to apply Bayesian methods to their own research.
Save
Gentle introduction to Bayesian statistics. It is written for students with a background in introductory statistics and good starting point for those who want to learn more about Bayesian methods.
Provides a gentle introduction to Bayesian statistics. It is written for students with a background in introductory statistics and good starting point for those who want to learn more about Bayesian methods.
Provides a practical introduction to Bayesian programming using Python. It is written for programmers who want to learn more about Bayesian methods.
Provides a comprehensive introduction to probabilistic graphical models. It is written for students and researchers with a background in probability and statistics.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It is written for students and researchers with a background in probability and statistics.
Provides a comprehensive introduction to Bayesian analysis for the social sciences. It is written for students and researchers with a background in social science and statistics.
Provides a comprehensive introduction to causal inference in statistics. It is written for students and researchers with a background in probability and statistics.
Provides a comprehensive introduction to Bayesian networks. It is written for students and researchers with a background in probability and statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kge58f/introduction
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Level
Introductory
Via
Coursera
Institution
Databricks
Instructor
Dr. Srijith Rajamohan
Language
English
Transcripts
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This course belongs to a
collection
called
Introduction to Computational Statistics for Data Scientists. It's comprised of three courses:
- Introduction to Bayesian Statistics (viewing)
- Bayesian Inference with MCMC
- Introduction to PyMC3 for Bayesian Modeling and Inference
Good to know
Well-suited for aspiring or new data scientists seeking to strengthen their knowledge in computational statistics
Python and Jupyter notebooks are utilized for practical implementation and illustration of Bayesian modeling, enhancing the hands-on learning experience
Taught by experienced instructors in the field, providing valuable insights and a strong foundation in computational statistics
Covers fundamental concepts of probability, Bayesian modeling, and inference, essential knowledge for data scientists
Incorporates sampling algorithms, a critical aspect of computational statistics, and provides practical Python implementations for sampling
Requires a basic understanding of statistics and probability, which may require additional preparation for some learners
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