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Data Science Foundations: Statistical Inference

Data Science Foundations: Statistical Inference

This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform.

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Vital Skills for Data Science

Vital Skills for Data Science

Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data.

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Modern Regression Analysis in R

Modern Regression Analysis in R

This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison.

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  • 45 ساعات
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Data Science Methods for Quality Improvement

Data Science Methods for Quality Improvement

Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown.

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A-level Mathematics for Year 12 - Course 1: Algebraic Methods, Graphs and Applied Mathematics Methods

A-level Mathematics for Year 12 - Course 1: Algebraic Methods, Graphs and Applied Mathematics Methods

Develop your thinking skills, fluency and confidence to aim for an A* in A-levelmaths and prepare for undergraduate STEM degrees.

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  • 12
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Business Writing

Business Writing

Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas.

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  • 13 ساعات
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Algorithms for Searching, Sorting, and Indexing

Algorithms for Searching, Sorting, and Indexing

This course covers basics of algorithm design and analysis, as well as algorithms for sorting arrays, data structures such as priority queues, hash functions, and applications such as Bloom filters. Algorithms for Searching, Sorting, and Indexing can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others.

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  • 35 ساعات
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The Structured Query Language (SQL)

The Structured Query Language (SQL)

In this course you will learn all about the Structured Query Language ("SQL".) We will review the origins of the language and its conceptual foundations. But primarily, we will focus on learning all the standard SQL commands, their syntax, and how to use these commands to conduct analysis of the data within a relational database.

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  • 55 ساعات
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Measurement Systems Analysis

Measurement Systems Analysis

In this course, you will learn to analyze measurement systems for process stability and capability and why having a stable measurement process is imperative prior to performing any statistical analysis. You will analyze continuous measurement systems and statistically characterize both accuracy and precision using R software. You will perform measurement systems analysis for potential, short-term and long-term statistical control and capability.

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  • 17 ساعات
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Statistical Inference and Hypothesis Testing in Data Science Applications

Statistical Inference and Hypothesis Testing in Data Science Applications

This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values.

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  • 37 ساعات
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