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Análisis de datos con programación en R

Análisis de datos con programación en R

Este es el séptimo curso del Certificado de análisis computacional de datos de Google. En estos cursos obtendrás las habilidades necesarias para solicitar empleos de analista de datos de nivel introductorio. En este curso, aprenderás el lenguaje de programación conocido como R. Además, se profundizará en cómo usar RStudio, el entorno que te permite trabajar con R, y se cubrirán temas como las aplicaciones y las herramientas de software que son exclusivas para R, como los paquetes de R.

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Machine Learning Algorithms with R in Business Analytics

Machine Learning Algorithms with R in Business Analytics

One of the most exciting aspects of business analytics is finding patterns in the data using machine learning algorithms. In this course you will gain a conceptual foundation for why machine learning algorithms are so important and how the resulting models from those algorithms are used to find actionable insight related to business problems. Some algorithms are used for predicting numeric outcomes, while others are used for predicting the classification of an outcome. Other algorithms are used for creating meaningful groups from a rich set of data.

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  • 14 hours
  • English
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Financial Analytics in RStudio: Identify Profit Drivers

Financial Analytics in RStudio: Identify Profit Drivers

This Guided Project 'Financial Analytics in RStudio: Identify Profit Drivers' is for Supply Chain and/or Operations Analysts. In this 2-hour long project-based course, you will learn how to determine operational & financial performance, calculate Operating Margin (OM), and plot OM waterfall chart showing OM variance drivers.

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  • 3 hours
  • English
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7. データ分析とR 言語

7. データ分析とR 言語

Google データアナリティクスプロフェッショナル認定プログラムの 7 つめのコースです。このプログラムでは、エントリーレベルのデータアナリストの仕事に就くために必要なスキルを身につけることができます。このコースでは、R というプログラミング言語と、R で作業する環境としての RStudio の使い方を学びます。また、R パッケージなど、R 特有のソフトウェア アプリケーションやツールについても学びます。R を使ってより優れた新しい手法でデータのクリーニング、整理、分析、可視化、レポート作成が可能になることを体感していただきます。 現職の Google データ アナリストが、最適なツールやリソースを使って、一般的なアナリスト業務を遂行する実践的な方法を指導します。 この認定プログラムを修了すると、エントリーレベルのデータ アナリスト職に応募できるようになります。過去の業務経験は不要です。 このコース修了後の目標は以下の通りです。 - R プログラミング言語を使用することのメリットを理解する。 - RStudio を使用して、R を分析に適用する方法を理解する。 - R でのプログラミングに関連する基本的な概念を理解する。 - Tidyverse パッケージを含む R パッケージの内容およびコンポーネントを探求する。

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Tout Pour Démarrer avec RStudio

Tout Pour Démarrer avec RStudio

Dans ce cours d'une heure, basé sur un projet, vous apprendrez comment démarrer avec RStudio IDE, sur votre ordinateur ou sur la solution en ligne de RStudio, comment créer des projets, installer des librairies R, ainsi qu'apprendre comment afficher des cartes interactives, des graphiques et des tableaux avec 1 ligne de code.

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  • 3 hours
  • French
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R을 사용한 확률 및 데이터 소개

R을 사용한 확률 및 데이터 소개

이 과정에서는 데이터 표본 추출 및 탐색, 기본 확률 이론 및 베이즈 정리를 소개합니다. 다양한 유형의 표본 추출 방법을 검토하고 이러한 방법이 추론 범위에 어떤 영향을 미칠 수 있는지 논의합니다. 수치 요약 통계 및 기본 데이터 시각화를 포함하여 다양한 탐색적 데이터 분석 기술을 다룹니다. R 및 RStudio(무료 통계 소프트웨어)를 설치하고 사용하는 방법을 안내하고 이 소프트웨어를 실습 및 최종 프로젝트에 사용합니다. 이 과정의 개념과 기술은 전문화 과정의 추론 및 모델링 과정을 위한 빌딩 블록 역할을 합니다.

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  • Korean
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Expressway to Data Science: R Programming and Tidyverse

Expressway to Data Science: R Programming and Tidyverse

R is one of the most popular languages used in the data science community and essential for any aspiring data scientist. This specialization will teach you how to do data science in R whether you are a beginning or a seasoned programmer. You will begin this three-course specialization by reviewing the fundamentals of programming in R and the importance of creating reproducible research.

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  • English
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Introduction to Data Science

Introduction to Data Science

Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field. This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions.

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Data Analytics in the Public Sector with R

Data Analytics in the Public Sector with R

Every government entity collects and stores millions of data points to perform administrative and legislative duties, allocate resources, and make decisions. Professionals in the public sector need the necessary skills to accurately interpret and inform administrators and policymakers about the meaning behind these data. This Specialization will equip you with fundamental technical skills using the R programming language to gather, manipulate, analyze, visualize, and interpret data to inform public policy and public administrative functions.

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  • English
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IBM Data Science

IBM Data Science

Prepare for a career in the high-growth field of data science. In this program, you’ll develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist in as little as 4 months. No prior knowledge of computer science or programming languages is required. Data science involves gathering, cleaning, organizing, and analyzing data with the goal of extracting helpful insights and predicting expected outcomes.

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  • English
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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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Line Balancing With MILP Optimization In RStudio

Line Balancing With MILP Optimization In RStudio

By the end of this project, you will learn to use R lpSolveAPI.

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  • 3 hours
  • English
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Exploratory Data Analysis for the Public Sector with ggplot

Exploratory Data Analysis for the Public Sector with ggplot

Learn about the core pillars of the public sector and the core functions of public administration through statistical Exploratory Data Analysis (EDA). Learn analytical and technical skills using the R programming language to explore, visualize, and present data, with a focus on equity and the administrative functions of planning and reporting. Technical skills in this course will focus on the ggplot2 library of the tidyverse, and include developing bar, line, and scatter charts, generating trend lines, and understanding histograms, kernel density estimations, violin plots, and ridgeplots.

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  • 18 hours
  • English
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Introduction to R: Basic R syntax

Introduction to R: Basic R syntax

This guided project is for beginners interested in taking their first steps with coding in the statistical language R. It assumes no previous knowledge of R, introduces the RStudio environment, and covers basic concepts, tools, and general syntax. By the end of the exercise, learners will build familiarity with RStudio and the fundamentals of the statistical coding language R.

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  • 4 hours
  • English
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Introducción a Data Science: Programación Estadística con R

Introducción a Data Science: Programación Estadística con R

Este curso te proporcionará las bases del lenguaje de programación estadística R, la lengua franca de la estadística, el cual te permitirá escribir programas que lean, manipulen y analicen datos cuantitativos. Te explicaremos la instalación del lenguaje; también verás una introducción a los sistemas base de gráficos y al paquete para graficar ggplot2, para visualizar estos datos.

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Demand Planning in RStudio: Create Demand Forecast

Demand Planning in RStudio: Create Demand Forecast

This Guided Project Demand Planning in RStudio: Create Demand Forecast is for Supply Chain and/or Operations Analysts.

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  • 4 hours
  • English
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RStudio for Six Sigma - Control Charts

RStudio for Six Sigma - Control Charts

Welcome to RStudio for Six Sigma - Control Charts. This is a project-based course which should take under 2 hours to finish. Before diving into the project, please take a look at the course objectives and structure. By the end of this project, you will learn to identify data types (Variables, Attribute Type I & II), understand the overview of Statistical Process Control, pick the right Control Chart based on the data type and other criteria, plot and interpret control charts.

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  • 2 hours
  • English
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Introduction to Distributions in R

Introduction to Distributions in R

This project is aimed at beginners who have a basic familiarity with the statistical programming language R and the RStudio environment, or people with a small amount of experience who would like to review the fundamentals of generating random numerical data from distributions in R.

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  • 2 hours
  • English
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Fundamentals of Data Analytics in the Public Sector with R

Fundamentals of Data Analytics in the Public Sector with R

Gain a foundational understanding of key terms and concepts in public administration and public policy while learning foundational programming techniques using the R programming language. You will learn how to execute functions to load, select, filter, mutate, and summarize data frames using the tidyverse libraries with an emphasis on the dplyr package.

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  • 15 hours
  • English
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Reproducible Templates for Analysis and Dissemination

Reproducible Templates for Analysis and Dissemination

This course will assist you with recreating work that a previous coworker completed, revisiting a project you abandoned some time ago, or simply reproducing a document with a consistent format and workflow. Incomplete information about how the work was done, where the files are, and which is the most recent version can give rise to many complications. This course focuses on the proper documentation creation process, allowing you and your colleagues to easily reproduce the components of your workflow.

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  • 20 hours
  • English
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RStudio for Six Sigma - Hypothesis Testing

RStudio for Six Sigma - Hypothesis Testing

Welcome to RStudio for Six Sigma - Hypothesis Testing. This is a project-based course which should take approximately 2 hours to finish.

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  • 3 hours
  • English
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Financial Risk Management with R

Financial Risk Management with R

This course teaches you how to calculate the return of a portfolio of securities as well as quantify the market risk of that portfolio, an important skill for financial market analysts in banks, hedge funds, insurance companies, and other financial services and investment firms. Using the R programming language with Microsoft Open R and RStudio, you will use the two main tools for calculating the market risk of stock portfolios: Value-at-Risk (VaR) and Expected Shortfall (ES). You will need a beginner-level understanding of R programming to complete the assignments of this course.

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  • 15 hours
  • English
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RStudio for Six Sigma - Basic Descriptive Statistics

RStudio for Six Sigma - Basic Descriptive Statistics

Welcome to RStudio for Six Sigma - Basic Description Statistics. This is a project-based course which should take approximately 2 hours to finish. Before diving into the project, please take a look at the course objectives and structure. By the end of this project, you will learn to perform Basic Descriptive Analysis (Six Sigma) tasks hands-on using RStudio. Both R language and RStudio tools are Open Source and can be used for most Six Sigma analysis tasks without needing commercial software.

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  • 2 hours
  • English
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Mediation Analysis with R

Mediation Analysis with R

In this project, you will learn to perform mediation analysis in RStudio.

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  • 2 hours
  • English
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Tools for Data Science

Tools for Data Science

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them. You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio.

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  • 18 hours
  • English
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