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  • Berkeley Global

Practical Statistics for Data Scientists Using R

COMPSCI X402.1

48901130
Delivery Options Online

Get a hands-on introduction to statistics for data science using the R programming language. You explore the foundations of statistics with a strong emphasis on constructing models from data. Topics include descriptive statistics, probability (including conditional probabilities and Bayes rule), multiple regression, multiway analysis of variance, and logistic regression. You also study statistical concepts, methods, tools for modeling multivariate systems, and the statistical foundations for machine and statistical learning applications for drawing reliable conclusions from real-world data. You also get a basic introduction to the R programming language and R tools for data exploration, modeling and presentation of results.

Prerequisites:

Knowledge of statistics as covered in a first-semester undergraduate course. Knowledge and experience using a programming language such as Java, C, BASIC, FORTRAN, or Ruby.

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Sections

Section 011

Jun 05 to Aug 16

Type: Live Online

Instructor:

  • Allan Miller

Cost: $995.00

See section 011 Details

Type Live Online

Days

M, W

Time

6:30PM to 8:00PM Pacific Time

Dates

Jun 05, 2023 to Aug 16, 2023

Location

Live Online

Schedule and Location

View Details

Instructional Hours

30.0

Location

Live Online

Delivery Options

Online

Available for Credit

2 semester units

Section Notes

No class meeting on June 19, 2023 or July 3, 2023.

Students will receive access to the course in Canvas on the course start date. Zoom meeting information will be emailed to enrolled students before the first class meeting and posted to the announcements page in Canvas.

Summer enrollment opens on March 20!

This course applies to the following programs:

Certificate Program in Data Science

Expand or collapse section

Programming

  • Introduction to R: Data Exploration and Visualization
  • Python for Data Analysis and Scientific Computing
  • Introduction to Data Science
  • Introduction to Data Science Using R

Machine Learning

  • Introduction to Machine Learning Using Python
  • Machine Learning and Deep Learning With Spark
  • Practical Machine Learning (With R)
  • Machine Learning With TensorFlow

Core Courses

  • Practical Statistics for Data Scientists Using R
  • Introduction to Big Data
  • Data Science Principles and Practice Using Python
  • Data Visualization

Electives

  • Introduction to Databases
  • Introduction to SQL

Learn More About this Program

Professional Program in Data Analysis

Expand or collapse section

Data Analysis Courses

  • Introduction to Data Analytics
  • Introduction to R: Data Exploration and Visualization
  • Python for Data Analysis and Scientific Computing
  • Data Analytics and Visualization
  • Practical Statistics for Data Scientists Using R

Data Management Courses

  • Introduction to Databases
  • Introduction to SQL
  • Business Intelligence With SQL Server
  • Data Mining Using SQL

Learn More About this Program


Instructional Types

Live Online
Live Online courses provide an interactive learning experience with scheduled synchronous online sessions held via Zoom video conferencing (Pacific Time). Learn more about the Live Online format.
Online, Start Anytime
Continuous enrollment course begins when you enroll. You have a minimum of 90 days and a maximum of 180 days to complete the course.
Online, Fixed Date
Enroll in this course by its start date and complete it by its specified end date. There are no live sessions, but plenty of opportunities to collaborate with your classmates and instructor.
Classroom
Take classes in-person at one of our buildings or partner locations.

English Language Proficiency Requirements

All of our courses are taught in English. If English is not your first language, please use the following test scores as guides in order to be successful:

  • TOEFL: 90
  • IELTS Academic Format: 7
  • DAAD: C1
  • TEM-4 or TEM-8: Level 70
  • Duolingo: 115

Note: You do not need to submit test scores.

Extension

1995 University Ave., Suite 130

Berkeley, CA 94704-7000

extension@berkeley.edu

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Western Association of Schools and Colleges

The University of California, Berkeley, is accredited by the Western Association of Schools and Colleges (WASC). UC Berkeley Extension—like all other UC Berkeley schools, colleges and departments—is accredited by WASC through the University.

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Course Fee

Allan Miller

Allan Miller is a veteran data scientist and educator. He is an active member of the San Francisco Bay Area R-language data science community, founding and current faculty in the UC Berkeley Extension Data Science program, and a member of the Extension Program's data science advisory board.

COMPSCI X402.1 - 011 - Practical Statistics for Data Scientists Using R

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