このコースについて

78,425 最近の表示

受講生の就業成果

29%

コース終了後に新しいキャリアをスタートした

30%

コースが具体的なキャリアアップにつながった

10%

昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
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次における5の3コース
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スケジュールに従って期限をリセットします。
約6時間で修了
英語
字幕:英語, 日本語

学習内容

  • Differentiate between various types of data pulls

  • Describe the basic data analysis iteration

  • Explore datasets to determine if data is appropriate for a project

  • Use statistical findings to create convincing data analysis presentations

習得するスキル

Data AnalysisCommunicationInterpretationExploratory Data Analysis

受講生の就業成果

29%

コース終了後に新しいキャリアをスタートした

30%

コースが具体的なキャリアアップにつながった

10%

昇給や昇進につながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の3コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
約6時間で修了
英語
字幕:英語, 日本語

提供:

ジョンズ・ホプキンズ大学(Johns Hopkins University) ロゴ

ジョンズ・ホプキンズ大学(Johns Hopkins University)

シラバス - 本コースの学習内容

コンテンツの評価Thumbs Up95%(6,431 件の評価)Info
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6時間で修了

Managing Data Analysis

6時間で修了
19件のビデオ (合計144分), 17 readings, 7 quizzes
19件のビデオ
Data Analysis Iteration8 分
Stages of Data Analysis1 分
Six Types of Questions6 分
Characteristics of a Good Question6 分
Exploratory Data Analysis Goals & Expectations11 分
Using Statistical Models to Explore Your Data (Part 1)13 分
Using Statistical Models to Explore Your Data (Part 2)5 分
Exploratory Data Analysis: When to Stop6 分
Making Inferences from Data: Introduction5 分
Populations Come in Many Forms4 分
Inference: What Can Go Wrong7 分
General Framework8 分
Associational Analyses10 分
Prediction Analyses10 分
Inference vs. Prediction12 分
Interpreting Your Results10 分
Routine Communication in Data Analysis6 分
Making a Data Analysis Presentation5 分
17件の学習用教材
Pre-Course Survey10 分
Course Textbook: The Art of Data Science10 分
Conversations on Data Science10 分
Data Science as Art10 分
Epicycles of Analysis10 分
Six Types of Questions10 分
Characteristics of a Good Question10 分
EDA Check List10 分
Assessing a Distribution10 分
Assessing Linear Relationships10 分
Exploratory Data Analysis: When Do We Stop?10 分
Factors Affecting the Quality of Inference10 分
A Note on Populations10 分
Inference vs. Prediction10 分
Interpreting Your Results10 分
Routine Communication10 分
Post-Course Survey10 分
7の練習問題
Data Analysis Iteration10 分
Stating and Refining the Question16 分
Exploratory Data Analysis10 分
Inference10 分
Formal Modeling, Inference vs. Prediction10 分
Interpretation10 分
Communication10 分

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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....
Executive Data Science

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