Online Course – Google Certified Professional Internship in Data Science Methods for Quality Improvement, University of Colorado Boulder

Invest in your career in data science. Master strategies in data science methods for quality improvement.

Suggested by: Coursera (What is Coursera?)

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Data analysis and interpretation of results
  • Using RStudio
  • Using RMarkdown
  • Management, description and analysis of continuous and discrete data
  • Evaluating processes for sources of change over time
  • Determining process capability in relation to customer requirements
  • Evaluation of measurement systems for continuous and discrete data
  • Performing analyses for different data types and scenarios
  • Interpreting results and making appropriate decisions

What you will learn in the course

Courses for which the course is suitable

  • Data Analyst
  • Data Scientist
  • Business Process Manager
  • Statistical Modeler
  • Information Systems Analyst
  • RStudio expert
  • Business Performance Analyst
  • Data Analysis Consultant
  • Data Project Manager
  • Data Solutions Developer

Internship – 3-part course series

Data analysis skills are highly sought after by employers, both in Israel and abroad. This specialization is suitable for anyone interested in analyzing data to improve quality and processes in business and industry.

By completing this training, you will develop your ability to analyze data and interpret results, and you will also gain new skills, such as using RStudio and RMarkdown. If you are looking for a job in data analysis, operations, or simply want to do more with data, this internship is a great way to get started.

recommendation

  • It is recommended that learners complete the specialization in the order in which the courses are presented.

More information

This internship can be completed for academic credit as part of the CU Boulder Master of Science in Data Science (MS-DS), offered on the Coursera platform. The MS-DS is a multidisciplinary degree that brings together faculty from various CU Boulder departments, such as Applied Mathematics, Computer Science, Information Science, and more.

With performance-based admissions and no admissions process, the MS-DS is suitable for individuals with a broad background in mathematics, statistics, computer science, and information science. Learn more about the MS-DS program on Coursera .

Applied Learning Project

Learners develop an understanding of how to manage, describe, and analyze continuous and discrete data using examples from the world of business and industry. They explore how to evaluate processes for sources of change over time, as well as determine process capability in relation to customer requirements.

Learners become familiar with the analysis procedures for evaluating measurement systems for continuous and discrete data to make decisions about the capability and acceptability of the measurement system. The tasks require learners to perform analyses for different data types and scenarios, interpret results, and make appropriate decisions.

Details of the courses that make up the specialization

Data management, description and analysis

Course 1

17 hours
4.6 (32 reviews)

What you’ll learn

  • Calculating descriptive statistics and creating graphical representations using R software
  • Problem solving and decision making using probability distributions
  • Exploring the basics of sampling and sample selection in the context of statistical inference
  • Classifying data types using measurement metrics

Skills you will acquire

  • Data Analytics
  • Data description
  • Graphical presentation of data
  • Using R

Course 2

9 hours
4.4 (12 reviews)

What you’ll learn

  • Understand how to use, select, and interpret control charts to identify specific causes of deviation
  • Creation and interpretation of control charts for normal and non-normal distributions
  • Creating and interpreting control charts for discrete-time data
  • Analyze the process’ ability to meet customer requirements

Skills you will acquire

  • Make decisions about process improvement
  • Slice analysis for femininity
  • R programming
  • Rstudio
  • Process analysis for capability

Course 3

16 hours

What you’ll learn

  • Understanding the terms and concepts related to measurement systems analysis
  • Measurement error analysis to determine the potential capability of a measurement system
  • Analyze measurement error to determine system capability in the short and long term
  • Analyze a measurement system for discrete data using prospective, short-term, and long-term studies

Skills you will acquire

  • Using RMarkdown to create a report
  • Analysis of a continuous measurement system for different capacity sources
  • Discrete measurement system analysis for validity and agreement
  • Making decisions about the acceptability of measurement systems