About this Course

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Learner Career Outcomes

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started a new career after completing these courses

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got a tangible career benefit from this course
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Approx. 9 hours to complete
English

What you will learn

  • 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

Skills you will gain

Data AnalysisCommunicationInterpretationExploratory Data Analysis

Learner Career Outcomes

26%

started a new career after completing these courses

28%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 9 hours to complete
English

Offered by

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Johns Hopkins University

Syllabus - What you will learn from this course

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Week
1

Week 1

9 hours to complete

Managing Data Analysis

9 hours to complete
19 videos (Total 144 min), 17 readings, 7 quizzes
19 videos
Data Analysis Iteration8m
Stages of Data Analysis1m
Six Types of Questions6m
Characteristics of a Good Question6m
Exploratory Data Analysis Goals & Expectations11m
Using Statistical Models to Explore Your Data (Part 1)13m
Using Statistical Models to Explore Your Data (Part 2)5m
Exploratory Data Analysis: When to Stop6m
Making Inferences from Data: Introduction5m
Populations Come in Many Forms4m
Inference: What Can Go Wrong7m
General Framework8m
Associational Analyses10m
Prediction Analyses10m
Inference vs. Prediction12m
Interpreting Your Results10m
Routine Communication in Data Analysis6m
Making a Data Analysis Presentation5m
17 readings
Pre-Course Survey10m
Course Textbook: The Art of Data Science10m
Conversations on Data Science10m
Data Science as Art10m
Epicycles of Analysis10m
Six Types of Questions10m
Characteristics of a Good Question10m
EDA Check List10m
Assessing a Distribution10m
Assessing Linear Relationships10m
Exploratory Data Analysis: When Do We Stop?10m
Factors Affecting the Quality of Inference10m
A Note on Populations10m
Inference vs. Prediction10m
Interpreting Your Results10m
Routine Communication10m
Post-Course Survey10m
7 practice exercises
Data Analysis Iteration30m
Stating and Refining the Question30m
Exploratory Data Analysis30m
Inference30m
Formal Modeling, Inference vs. Prediction30m
Interpretation30m
Communication30m

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About the Executive Data Science Specialization

Executive Data Science

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