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60 Minutes to Better Survey Analysis Using Python

Topic: Data
Walter Paczkowski, Ph.D.

Surveys are widely used as a primary way to gather data, and ultimately information, on attitudes, interests, and opinions (AIOs) of customers and constituents that are vital for private or public sector policy decisions. This course is important for anyone who analyzes survey data for decisions in either sector.

Why focus on surveys? The reason is simple. Surveys are a main source of data key decision makers use for the critical decisions they make every day. There are other data sources such as observational data (e.g. transactions data) and experimental data. Neither will tell you about AIOs. Knowing how to analyze survey data is important for getting the information on these aspects of customers or constituents.

There are two overarching objectives for this course:

  1. Teach you how to extract actionable, insightful, and useful information from survey data.
  2. Teach you how to use Python to analyze your survey data.

At the end of one hour, you will understand the basics of analyzing survey data from a tabular, visual, and statistical perspective. The statistics will be at an introductory level.

What you'll learn-and how you can apply it

By the end of this live, hands-on, online course, you’ll understand:

  • The importance of survey data.
  • The need to dig deeper using more sophisticated analysis methods.
  • How to use Python to do survey data analysis.

And you’ll be able to:

  • Create data visualizations using Python.
  • Create and analyze tables using Python.
  • Create and analyze correspondence maps of the tables using Python.

This training course is for you because...

  • You are a market research professional in the private or public sector. In the private sector, you work in either a market research department of a business or a market research consultancy. In the public sector, you could work in a government agency where you are responsible for measuring and assessing constituents' attitudes and opinions regarding a policy proposal or government action (or inaction).
  • You work with surveys and survey data.
  • You want to become more proficient at analyzing survey data to extract more actionable insight from your data.

Prerequisites

  • You should have a basic working knowledge of Python and the use of Jupyter notebooks. Knowledge at the level of my Business Data Analytics Using Python course is sufficient.
  • Prior knowledge of statistics will be helpful, but is not required.

About your instructor

  • Walter R. Paczkowski has a Ph.D. in Economics from Texas A&M University (1977). With over 40 years of extensive quantitative experience as an analyst in AT&T's Analytical Support Center, a Member of the Technical Staff at AT&T Bell Labs, head of Pricing Research at AT&T's Computer Systems division, and founder of Data Analytics Corp., he brings a wealth of knowledge to share about data analysis. His work as a market research consultant is focused on helping companies in a wide range of industries, such as telecommunications, pharmaceuticals, jewelry, food & beverages, and automotive to mention a few, to turn their market data into actionable market information. Walter is also currently on the faculty of the Department of Economics, Rutgers University (Adjunct) and was formerly with the Department of Mathematics & Statistics, The College of New Jersey (Adjunct). Walter is also the author of two analytical books: Market Data Analysis Using JMP (SAS Press, 2016) and Pricing Analytics (Routledge 2018) with a third forthcoming on quantitative methods for new product development (Routledge, 2019). You can learn more about Walter and his consulting company, Data Analytics Corp., at www.dataanalyticscorp.com.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Introduction (5 minutes)

  • The importance of surveys in modern life
  • The current state of survey analysis

Understanding the structure of survey data (20 minutes)

  • Knowing the structure of the data using simple statistics
  • Descriptive statistics
  • Frequency counts
  • Basic data visualization
  • Histograms
  • Boxplots
  • Bar and pie charts
  • Mosaic charts

Digging into survey data (30 minutes)

  • Tabulations
  • Correspondence analysis
  • Issues with tables
  • Mapping table data

Wrap-up (5 minutes)