Informações sobre o curso
4.5
1,779 ratings
243 reviews
This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to…. 1. Describe the basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations Commitment: 1 week of study, 4-6 hours Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD...
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Sugerido: 1 week of study, 4-6 hours

Aprox. 6 horas restantes
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English

Legendas: English, Japanese

O que você vai aprender

  • Check
    Describe the basic data analysis iteration
  • Check
    Differentiate between various types of data pulls
  • Check
    Explore datasets to determine if data is appropriate for a project
  • Check
    Use statistical findings to create convincing data analysis presentations

Habilidades que você terá

Data AnalysisCommunicationInterpretationExploratory Data Analysis
Globe

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Clock

Sugerido: 1 week of study, 4-6 hours

Aprox. 6 horas restantes
Comment Dots

English

Legendas: English, Japanese

Programa - O que você aprenderá com este curso

1

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6 horas para concluir

Managing Data Analysis

Welcome to Managing Data Analysis! This course is one module, intended to be taken in one week. The course works best if you follow along with the material in the order it is presented. Each lecture consists of videos and reading materials that expand on the lecture. I'm excited to have you in the class and look forward to your contributions to the learning community. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!...
Reading
19 vídeos (Total de 144 min), 17 leituras, 7 testes
Video19 videos
Data Analysis Iteration8min
Stages of Data Analysis1min
Six Types of Questions6min
Characteristics of a Good Question6min
Exploratory Data Analysis Goals & Expectations11min
Using Statistical Models to Explore Your Data (Part 1)13min
Using Statistical Models to Explore Your Data (Part 2)5min
Exploratory Data Analysis: When to Stop6min
Making Inferences from Data: Introduction5min
Populations Come in Many Forms4min
Inference: What Can Go Wrong7min
General Framework8min
Associational Analyses10min
Prediction Analyses10min
Inference vs. Prediction12min
Interpreting Your Results10min
Routine Communication in Data Analysis6min
Making a Data Analysis Presentation5min
Reading17 leituras
Pre-Course Survey10min
Course Textbook: The Art of Data Science10min
Conversations on Data Science10min
Data Science as Art10min
Epicycles of Analysis10min
Six Types of Questions10min
Characteristics of a Good Question10min
EDA Check List10min
Assessing a Distribution10min
Assessing Linear Relationships10min
Exploratory Data Analysis: When Do We Stop?10min
Factors Affecting the Quality of Inference10min
A Note on Populations10min
Inference vs. Prediction10min
Interpreting Your Results10min
Routine Communication10min
Post-Course Survey10min
Quiz7 exercícios práticos
Data Analysis Iteration10min
Stating and Refining the Question16min
Exploratory Data Analysis10min
Inference10min
Formal Modeling, Inference vs. Prediction10min
Interpretation10min
Communication10min
4.5
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consegui um benefício significativo de carreira com este curso

Melhores avaliações

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(24)
por ELMar 1st 2017

A long course compared to others in the specialization, but a lot of great material. Very well presented, the instructors know how to present this material and make it easy to grasp and understand.

por STNov 23rd 2016

The course is full of the cases and the real life examples coupled with the theory background. Its very simple to understand and the course will definitely be of an value for people looking for

Instrutores

Jeff Leek, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Sobre Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world....

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Assemble the right team, ask the right questions, and avoid the mistakes that derail data science projects. In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects....
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Perguntas Frequentes – FAQ

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

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