This course is part of the Introduction to Computational Statistics for Data Scientists Specialization

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31 ratings

Dr. Srijith Rajamohan

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Offered By

Introduction to Computational Statistics for Data Scientists SpecializationDatabricks

About this Course

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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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Course 1 of 3 in the

Introduction to Computational Statistics for Data Scientists Specialization

Beginner Level

Some experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.

Knowledge of Jupyter Notebooks will be beneficial.

Approx. 12 hours to complete

English

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What you will learn

The basics of Probability, Bayesian statistics, modeling and inference.
You will also get a hands-on introduction to using Python for computational statistics using Scikit-learn, SciPy and Numpy.

Skills you will gain

Bayesian Inference
visualization
Python Programming
Scipy
Statistics

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 3 in the

Introduction to Computational Statistics for Data Scientists Specialization

Beginner Level

Some experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.

Knowledge of Jupyter Notebooks will be beneficial.

Approx. 12 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

3.23/5 (14 Ratings)

Dr. Srijith Rajamohan

Sr. Developer Advocate (Data Science)

4,419 Learners

3 Courses

Offered by

Databricks

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Syllabus - What you will learn from this course

Week1

Week 1

21 minutes to complete

Environment Setup

21 minutes to complete

4 videos (Total 11 min), 1 reading

Week2

Week 2

6 hours to complete

Introduction to the Fundamentals of Probability

6 hours to complete

17 videos (Total 119 min), 7 readings, 12 quizzes

17 videos

Introductions1mChance regularities and random processes6mOutcomes, events and spaces8mAddition rules of probability6mMultiplication rules of probability5mConditional probability, Random Variables and Experiments8mRandom Variables and Distributions12mMoments, mean and variance10mJoint distributions of Random Variables5mEstimation using MoM and MLE9mBasics of Bayes' Rule7mDecisions and Loss Functions11mPriors introduction5mPriors as conjugates2mInformative vs non-informative priors3mJeffrey's Prior2mPrior distributions and posterior ramifications12m

7 readings

Introduction and references2mRules for manipulating probability10mRandom variables10mMoM and MLE10mBayes' and decisions10mLoss functions10mMore on priors10m

12 practice exercises

Week 2 Belief and Probability Practice10mWeek 2 Belief and Probability Graded Quiz10mWeek 2 Manipulating Probability Practice10mWeek 2 Manipulating Probability Graded Quiz20mWeek 2 Distributions Practice5mWeek 2 Distributions Graded Quiz10mWeek 2 Estimation Practice10mWeek 2 Estimation Graded Quiz30mWeek 2 Decisions Practice10mWeek 2 Decisions Graded Quiz30mWeek 2 Priors Practice10mWeek 2 Priors Graded Quiz10m

Week3

Week 3

4 hours to complete

A Hands-On Introduction to Common Distributions

4 hours to complete

12 videos (Total 49 min), 2 readings, 2 quizzes

12 videos

The Binomial Distribution5mNegative Binomial Distribution4mPoisson Distribution3mExponential Distribution2mGamma Distribution3mNormal Distribution3mLognormal Distribution2mStudent's t-distribution5mBeta Distribution4mMLE Estimation using a Beta Distribution 4mGaussian Mixture Model5mNon-parametric Methods: Kernel Density Estimation3m

2 readings

Reference1hReference1h

2 practice exercises

Common distributions30mNon-parametric methods20m

Week4

Week 4

3 hours to complete

Sampling Algorithms

3 hours to complete

6 videos (Total 32 min), 2 readings, 3 quizzes

Reviews

3.5

13 reviews

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3 stars
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2 stars
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1 star
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TOP REVIEWS FROM INTRODUCTION TO BAYESIAN STATISTICS

by MGAug 13, 2021

This course would be a bit hard for "complete" beginners, but would be enough for people who wish to refresh knowledge about Bayesian inference and stuff. The notes and codes are very good!!

by TSFeb 25, 2022

Content/notes wise this course is great, But teaching style needs to be improved. Rather than reading the notes instructor should teach by giving examples and driving some of the results.

View all reviews

About the Introduction to Computational Statistics for Data Scientists Specialization

The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST). What it does cover is:

The basics of Bayesian statistics and probability Understanding Bayesian inference and how it works The bare-minimum set of tools and a body of knowledge required to perform Bayesian inference in Python, i.e. the PyData stack of NumPy, Pandas, Scipy, Matplotlib, Seaborn and Plot.ly A scalable Python-based framework for performing Bayesian inference, i.e. PyMC3 With this goal in mind, the content is divided into the following three main sections (courses). Introduction to Bayesian Statistics - The attendees will start off by learning the the basics of probability, Bayesian modeling and inference in Course 1. Introduction to Monte Carlo Methods - This will be followed by a series of lectures on how to perform inference approximately when exact calculations are not viable in Course 2. PyMC3 for Bayesian Modeling and Inference - PyMC3 will be introduced along with its application to some real world scenarios. The lectures will be delivered through Jupyter notebooks and the attendees are expected to interact with the notebooks.

Introduction to Computational Statistics for Data Scientists

Frequently Asked Questions

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Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Introduction to Bayesian Statistics

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