Online Course – Google Certified Professional Internship in Data Collection and Survey Analysis, University of Michigan

Learn how to collect quality data and conduct in-depth analysis in six courses. Collect and analyze data while communicating results professionally.

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קורס אונליין - התמחות מקצועית מוסמכת באיסוף נתונים וסקר וניתוחם של Google

Professional Certificate

Beginners

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Questionnaire design basics
  • Data collection methods
  • Sampling design
  • Dealing with missing values
  • Making assessments
  • Integrating data from different sources
  • Survey data analysis

What you will learn in the course

Courses for which the course is suitable

  • Survey researcher
  • Survey statistician
  • Survey Methodologist
  • Market Research Manager
  • Data Analyst
  • Sampling planner
  • Data collection specialist
  • Survey data analyst
  • Social science researcher
  • Political researcher

Internship – 6-part course series

This specialization covers the basics of surveys as they are used in market research, evaluation studies, social science and political research, official government statistics, and many other fields.

Courses

  • Questionnaire design basics
  • Data collection methods
  • Sampling design
  • Dealing with missing values
  • Making assessments
  • Integrating data from different sources
  • Survey data analysis

The faculty for this aspect comes from the Michigan Program in Survey Methodology and the Joint Program in Survey Methodology, a collaboration between the University of Maryland, the University of Michigan, and the data collection company Westat.

The goal is to educate the next generation of survey researchers, survey statisticians, and survey methodologists.

In addition to this specialization, we offer:

  • Short courses
  • Summer school
  • Certificates
  • Master’s degrees
  • Doctoral programs

Details of the courses that make up the specialization

Framework for data collection and analysis

Course 1: Review of existing data products

Duration: 9 hours
Rating: 4.2 (742 ratings)

What you will learn:

  • Review of existing data products.
  • Identify data sources that match the research question.
  • Transforming the research question into units of measurement.
  • Data analysis and collection plan.
  • Understanding errors in data sources.
  • Assessing the quality of the data source.

Course 2: Data Collection – Online, Telephone and Face-to-Face

Duration: 21 hours
Rating: 4.6 (332 ratings)

What you will learn:

  • Evaluation and comparison of self-management methods and conversational cues.
  • Investigating new methods and data sources.
  • Understanding key concepts in survey data collection methods.

Skills you will gain:

  • Sample analysis (statistics).
  • Statistical analysis.

Course 3: Designing Questionnaires for Social Surveys

Duration: 16 hours
Rating: 4.5 (446 ratings)

What you will learn:

  • Understanding the basic elements of questionnaire design and evaluation.

Course 4: Sampling People, Networks, and Records

Duration: 25 hours
Rating: 4.4 (102 ratings)

What you will learn:

  • Understanding the value and risks of sampling and randomization methods.
  • Distinguishing between the different types of samples.
  • Explanation of principles and techniques of random sampling methods.

Skills you will gain:

  • Survey design.
  • Data collection.

Course 5: Dealing with Missing Data

Duration: 17 hours
Rating: 3.8 (132 ratings)

What you will learn:

  • Measures of survey weight.
  • Methods for adapting to situations of unresponsiveness.
  • Techniques for completing values ​​for missing items.

Skills you will gain:

  • Survey design.

Course 6: Integrating and Analyzing Complex Data

Duration: 9 hours
Rating: 4.1 (58 ratings)

What you will learn:

  • Using survey weights to estimate descriptive statistics.
  • Record linking and statistical matching.
  • Ethical issues in combining datasets.

Skills you will gain:

  • Data collection.
  • Data quality.
  • Data analysis.
  • Data creation process.

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