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Introduction to Bayesian Statistics

Dr. Srijith Rajamohan

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.

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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.

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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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Traffic lights

Read about what's good

what should give you pause

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

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Reviews summary

Introduction to bayesian statistics for data scientists

According to students, this course provides a very good introduction to Bayesian statistics, particularly for aspiring and new data scientists. Many appreciate the clear lectures and the practical, hands-on approach, especially the effective use of Python and Jupyter notebooks for applying concepts like MLE and KDE. While it offers a strong foundational understanding, some learners found the pace a bit fast or felt that the course understated the necessary mathematical and statistical prerequisites, making it challenging without prior knowledge. The Databricks environment setup is generally smooth.

Provides a solid groundwork, but not advanced depth.

"Overall, a very good introduction to Bayesian statistics."

"Good course, but I wish there was more depth on specific applications. It's a great introduction..."

"This course is perfect for data scientists looking to add Bayesian methods to their toolkit."

Smooth setup and integration with the Databricks ecosystem.

"The Databricks environment setup was smooth."

"The Databricks setup was straightforward, which was a relief."

Strong emphasis on hands-on coding with Python for practical use.

"...the Python notebooks were extremely helpful for practical application."

"The hands-on exercises using Python were key to understanding."

"The practical coding examples were invaluable. Great for professionals who need to quickly grasp the fundamentals and apply them."

"I appreciated the practical approach to Bayesian modeling. The Jupyter notebooks were well-annotated..."

Instructors deliver complex topics with excellent clarity.

"The lectures were clear and the Python notebooks were extremely helpful..."

"Dr. Rajamohan explained complex concepts like MLE and KDE with great clarity."

"The explanations of sampling algorithms were particularly clear. The course successfully demystified many concepts."

Could benefit from more interactive practice opportunities.

"My main feedback is that the course could benefit from more interactive quizzes or problem sets to reinforce learning."

Course is challenging; strong math/stats background recommended.

"The prerequisites were understated. You really need a strong math background, especially in calculus and linear algebra..."

"Found this course quite challenging. As someone without a strong math background, I struggled with the theoretical parts."

"The initial probability review was good, but once it got into MLE and KDE, things got quite dense. It assumes a higher level of statistical maturity..."

"I felt the pace was a bit too fast in some sections, especially for those new to probability theory."

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

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Solidify your understanding of Python basics, variables, loops, and functions.

Browse courses on Python

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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

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Reinforce understanding of probability theory and practice applying it to real-world scenarios.

Browse courses on Probability

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Review basic probability theory
Solve probability problems using Bayes' Theorem
Calculate probabilities for different events

Explore Jupyter Notebook tutorials

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Become comfortable with using Jupyter Notebooks for data analysis and visualization.

Browse courses on Jupyter Notebook

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Find Jupyter Notebook tutorials online
Follow the tutorials to create your own notebooks

Four other activities

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Join online forums or discussion groups

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Engage with other learners to clarify concepts, share insights, and provide support.

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Identify relevant online forums or discussion groups
Participate in discussions by sharing your knowledge and asking questions

Practice statistical inference

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Develop proficiency in statistical inference methods and apply them to analyze data.

Browse courses on Statistical Inference

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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

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Gain hands-on experience in building and evaluating Bayesian models using Python.

Browse courses on Bayesian Modeling

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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

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Enhance understanding of Bayesian modeling and contribute to the open-source community.

Browse courses on Open-Source

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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

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Biostatistician

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.

See salaries and explore the career path for Quantitative 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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Insurance Analyst

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.

See salaries and explore the career path for Epidemiologist

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.

See salaries and explore the career path for Market Researcher

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Economist

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.

See salaries and explore the career path for Financial Analyst

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.

See salaries and explore the career path for Risk Manager

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.

See salaries and explore the career path for Software Engineer

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.

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Bayesian Data Analysis, Third Edition

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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.

Bayesian Data Analysis, Third Edition

Bayesian Data Analysis, Third Edition

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Statistical Rethinking

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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.

Statistical Rethinking

Statistical Rethinking

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Doing Bayesian Data Analysis

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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.

Doing Bayesian Data Analysis: A Tutorial with R,...

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Bayesian Statistics

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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.

Bayesian Statistics

Bayesian Statistics

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Cover image

Introduction to Bayesian Statistics

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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.

Introduction to Bayesian Statistics

Introduction to Bayesian Statistics

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Cover image

Bayesian Methods for Hackers

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Provides a practical introduction to Bayesian programming using Python. It is written for programmers who want to learn more about Bayesian methods.

Bayesian Methods for Hackers: Probabilistic...

Bayesian Methods for Hackers: Probabilistic...

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Probabilistic Graphical Models

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Provides a comprehensive introduction to probabilistic graphical models. It is written for students and researchers with a background in probability and statistics.

Probabilistic Graphical Models: Principles and...

Probabilistic Graphical Models: Principles and...

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Cover image

Probabilistic Machine Learning

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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.

Probabilistic Machine Learning: Advanced Topics...

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Party Competition: An Agent-Based Model

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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.

Party Competition: An Agent-Based Model

Party Competition: An Agent-Based Model

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Cover image

Causal Inference in Statistics

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Provides a comprehensive introduction to causal inference in statistics. It is written for students and researchers with a background in probability and statistics.

Causal Inference in Statistics - A Primer

Causal Inference in Statistics: A Primer

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Cover image

Contemporary Artificial Intelligence

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Provides a comprehensive introduction to Bayesian networks. It is written for students and researchers with a background in probability and statistics.

Contemporary Artificial Intelligence

Contemporary Artificial Intelligence

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Similar courses

Similar courses are unavailable at this time. Please try again later.

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

Traffic lights

Read about what's good

what should give you pause

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

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