assignment 1 analyze a dataset using tableau

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Data Analysis with Tableau

This course is part of Tableau Business Intelligence Analyst Professional Certificate

Taught in English

Some content may not be translated

Instructor: Tableau Learning Partner Instructor

Financial aid available

2,196 already enrolled

(20 reviews)

Recommended experience

Beginner level

No prior experience is necessary

What you'll learn

Apply Tableau techniques to manipulate and prepare data for analysis.

Perform exploratory data analysis using Tableau and report insights using descriptive statistics and visualizations.

Identify the benefits of the analytics feature in Tableau by utilizing this tool versus manually calculating the analytics.

Skills you'll gain

• Data Manipulation
• Tableau Data Analytics
• Data Analysis Reporting

Details to know

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January 2024

12 quizzes, 5 assignments

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There are 4 modules in this course

The Data Analysis with Tableau Course will teach you how to manipulate and prepare data for analysis and reporting. You will also learn how to use the analytics features in Tableau to more effectively calculate analytics versus manual calculations. In this course, you will perform exploratory data analysis as well as create reports using descriptive statistics and visualizations.

This course is for anyone who is curious about entry-level roles that demand fundamental Tableau skills, such as business intelligence analyst or data reporting analyst roles. It is recommended (but not required) that you have some experience with Tableau Public, but even if you're new to Tableau Public, you can still be successful in this program. By the end of the course, you will be able to: -Apply Tableau Public techniques to manipulate and prepare data for analysis. -Perform exploratory data analysis using Tableau and report insights using descriptive statistics and visualizations. -Identify the benefits of the analytics feature in Tableau by utilizing this tool versus manually calculating the analytics.

Data Analysis and Exploration

Welcome to Data Analysis with Tableau, the seventh course in the Tableau Business Intelligence Analyst Professional Certificate series. In this first week, we introduce you to the Tableau Data Analysis Process framework. This comprehensive guide is essential for effectively preparing, analyzing, interpreting, and communicating data. You'll also begin learning about data exploration, which helps you dive into datasets and refine them for more insightful analysis.

What's included

4 videos 18 readings 2 quizzes 1 assignment 1 discussion prompt

4 videos • Total 10 minutes

• Welcome to the Course • 2 minutes • Preview module
• The Business Intelligence Analyst Role • 2 minutes
• Welcome to Week 1 • 1 minute
• Data Analysis Process Framework • 3 minutes

18 readings • Total 155 minutes

• What Is Pathstream? • 5 minutes
• Data Analysis with Tableau Course Syllabus • 5 minutes
• How to Be Successful in the Course • 5 minutes
• How to Use Discussion Forums • 5 minutes
• Obtain the Superstore Dataset • 10 minutes
• Data Analysis Process Overview • 5 minutes
• Data Preparation • 10 minutes
• Data Analysis • 10 minutes
• Insight Development • 10 minutes
• Data Storytelling • 10 minutes
• Importance of Data Exploration • 10 minutes
• Exploring Structure and Metadata • 10 minutes
• Relationships, Joins, and Unions • 10 minutes
• Organizing and Customizing Fields • 10 minutes
• Combining Values • 10 minutes
• Removing Fields and Values • 10 minutes
• Creating Fields • 10 minutes
• Week 1 Recap • 10 minutes

2 quizzes • Total 60 minutes

• Practice - Data Analysis Process • 30 minutes
• Practice - Data Exploration • 30 minutes

1 assignment • Total 60 minutes

• Data Analysis and Exploration • 60 minutes

1 discussion prompt • Total 15 minutes

• Get to Know Your Classmates - Meet & Greet • 15 minutes

Data Preprocessing and Aggregation

Welcome to Week 2 of Data Analysis with Tableau, where you will dive deeper into the crucial stages of data preparation. This week focuses on refining your skills in preparing data for in-depth analysis. Building upon the preprocessing concepts introduced in previous courses, you will explore advanced preprocessing techniques to enhance the quality and relevance of your data. Additionally, this week is dedicated to understanding and utilizing the powerful aggregation tools integrated within Tableau.

10 videos 27 readings 3 quizzes 1 assignment

10 videos • Total 54 minutes

• Welcome to Week 2 • 1 minute • Preview module
• Data Source and Worksheet Filters • 4 minutes
• Context Filters • 9 minutes
• Creating Folders and Hierarchies • 5 minutes
• Drilling Down • 4 minutes
• Groups • 6 minutes
• Sets • 4 minutes
• Aggregate Functions in Calculated Fields • 9 minutes
• Quick Table Calculations • 5 minutes
• Table Calculations • 4 minutes

27 readings • Total 180 minutes

• Data Preparation Overview • 10 minutes
• Filtering Data in Tableau Overview • 5 minutes
• How to Create a Data Source Filter • 10 minutes
• How to Create a Worksheet Filter • 5 minutes
• Filter Interface with Dimensions • 5 minutes
• How to Filter with Dimensions • 5 minutes
• Filter Interface with Measures • 5 minutes
• How to Filter with Measures • 5 minutes
• Understanding Context Filters • 5 minutes
• How to Create a Context Filter • 5 minutes
• Filtering Order of Operations • 10 minutes
• Introduction to Combining Data • 5 minutes
• Using Folders in Tableau • 5 minutes
• Introduction to Hierarchies • 5 minutes
• Create a Custom Hierarchy • 5 minutes
• Understanding Drill-Down Reports • 5 minutes
• How to Drill Down in Time • 5 minutes
• How to Create a Custom Group • 10 minutes
• Introduction to Sets • 5 minutes
• Introduction to Aggregation • 5 minutes
• Aggregate Functions in Calculated Fields • 10 minutes
• What Are Table Calculations? • 5 minutes
• What Are Quick Table Calculations? • 5 minutes
• How to Use a Quick Table Calculation • 10 minutes
• What Are Grand Totals and Subtotals? • 10 minutes
• Adding Grand Totals and Subtotals to a Table • 10 minutes
• Week 2 Recap • 10 minutes

3 quizzes • Total 90 minutes

• Practice - Data Filtering • 30 minutes
• Practice - Combining Data • 30 minutes
• Practice - Data Aggregation • 30 minutes
• Data Preprocessing and Aggregation • 60 minutes

Introduction to Statistical Analysis

Welcome to the third week of Data Analysis with Tableau! This week, you will engage with external modules to deepen your understanding of data distributions and variation for data comparisons. You'll explore fundamental statistical concepts including mean, variance, standard deviation, as well as frequency and population distributions, continuous distributions, hypothesis testing, and confidence intervals. After completing the external modules, you will learn how to create and interpret histograms and box plots in Tableau, turning your newly acquired statistical knowledge into valuable visualizations. By the end of this module, you will not only comprehend the underlying principles of statistical analysis but also be adept at visually representing statistical data.

3 videos 11 readings 3 quizzes 1 assignment

3 videos • Total 25 minutes

• Welcome to Week 3 • 1 minute • Preview module
• Histograms • 11 minutes
• Box Plots • 12 minutes

11 readings • Total 180 minutes

• Intro to Statistical Analysis • 10 minutes
• External Module: Data Distributions • 45 minutes
• External Module: Variation for Data Comparisons • 45 minutes
• Statistics Cheat Sheet • 10 minutes
• Histograms • 10 minutes
• How to Build a Histogram • 10 minutes
• Histogram Design • 10 minutes
• Box Plots • 10 minutes
• How to Build a Box Plot • 10 minutes
• Box Plot Design • 10 minutes
• Week 3 Recap • 10 minutes
• Statistical Analysis • 30 minutes
• Histograms • 30 minutes
• Box Plots • 30 minutes
• Statistical Analysis • 60 minutes

Introduction to Predictive Analytics

Welcome to the fourth and final week of Data Analysis with Tableau! This week begins with an external module on correlation and regression, which are key for understanding predictive trends. You'll then apply these concepts in Tableau by creating and interpreting scatter plots and enhancing visualizations with reference lines, bands, and trend lines. The week culminates in a comprehensive project using the Superstore dataset, where you'll apply not only predictive analytics techniques but also integrate concepts learned in previous weeks.

3 videos 19 readings 4 quizzes 2 assignments

3 videos • Total 14 minutes

• Welcome to Week 4 • 1 minute • Preview module
• Scatter Plots, Trend Lines, and Custom Analytics • 12 minutes
• Congratulations • 0 minutes

19 readings • Total 145 minutes

• Intro to Predictive Analytics • 10 minutes
• External Module: Correlation and Regression • 10 minutes
• Understanding Scatter Plots • 10 minutes
• How to Build a Scatter Plot • 5 minutes
• Scatter Plot Design • 5 minutes
• Intro to Custom Analytics • 10 minutes
• Understanding Reference Lines • 5 minutes
• How to Add a Reference Line • 5 minutes
• Understanding Reference Bands • 5 minutes
• How to Add a Reference Band • 5 minutes
• Understanding Distribution Bands • 5 minutes
• How to Add Distribution Bands • 5 minutes
• What Are Trend Lines? • 10 minutes
• Trend Lines, Correlation, and Regression • 10 minutes
• How to Create a Trend Line • 5 minutes
• Forecasting • 10 minutes
• How to Add a Forecast • 10 minutes
• Week 4 Recap • 10 minutes
• Project Overview • 10 minutes

4 quizzes • Total 125 minutes

• Practice - Predictive Analytics • 30 minutes
• Practice - Scatter Plots • 30 minutes
• Practice - Custom Analytics • 35 minutes
• Practice - Trend Lines and Forecasts • 30 minutes

2 assignments • Total 240 minutes

• Predictive Analytics • 60 minutes
• Prepare and Analyze the Superstore Dataset • 180 minutes

Founded in 2003, Tableau helps people and organizations be more data-driven. As the market-leading choice for modern business intelligence, our analytics platform makes it easier for people to explore and manage data, and faster to discover and share insights that can change businesses and the world. Everything we do is driven by our mission to help people see and understand data, which is why our products are designed to put the user first—whether they’re an analyst, data scientist, student, teacher, executive, or business user. From connection through collaboration, Tableau is the most powerful, secure, and flexible end-to-end analytics platform.

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Tableau Desktop and Web Authoring Help

Analyze data.

This section describes the various ways you can perform more advanced analysis in Tableau. Read the following articles for information on how to create calculated fields, find clusters in data, calculate percentages, and use various tools to explore and inspect data.

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6.894 : Interactive Data Visualization

Assignment 2: exploratory data analysis.

In this assignment, you will identify a dataset of interest and perform an exploratory analysis to better understand the shape & structure of the data, investigate initial questions, and develop preliminary insights & hypotheses. Your final submission will take the form of a report consisting of captioned visualizations that convey key insights gained during your analysis.

Step 1: Data Selection

First, you will pick a topic area of interest to you and find a dataset that can provide insights into that topic. To streamline the assignment, we've pre-selected a number of datasets for you to choose from.

However, if you would like to investigate a different topic and dataset, you are free to do so. If working with a self-selected dataset, please check with the course staff to ensure it is appropriate for the course. Be advised that data collection and preparation (also known as data wrangling ) can be a very tedious and time-consuming process. Be sure you have sufficient time to conduct exploratory analysis, after preparing the data.

After selecting a topic and dataset – but prior to analysis – you should write down an initial set of at least three questions you'd like to investigate.

Part 2: Exploratory Visual Analysis

Next, you will perform an exploratory analysis of your dataset using a visualization tool such as Tableau. You should consider two different phases of exploration.

In the first phase, you should seek to gain an overview of the shape & stucture of your dataset. What variables does the dataset contain? How are they distributed? Are there any notable data quality issues? Are there any surprising relationships among the variables? Be sure to also perform "sanity checks" for patterns you expect to see!

In the second phase, you should investigate your initial questions, as well as any new questions that arise during your exploration. For each question, start by creating a visualization that might provide a useful answer. Then refine the visualization (by adding additional variables, changing sorting or axis scales, filtering or subsetting data, etc. ) to develop better perspectives, explore unexpected observations, or sanity check your assumptions. You should repeat this process for each of your questions, but feel free to revise your questions or branch off to explore new questions if the data warrants.

• Final Deliverable

Your final submission should take the form of a Google Docs report – similar to a slide show or comic book – that consists of 10 or more captioned visualizations detailing your most important insights. Your "insights" can include important surprises or issues (such as data quality problems affecting your analysis) as well as responses to your analysis questions. To help you gauge the scope of this assignment, see this example report analyzing data about motion pictures . We've annotated and graded this example to help you calibrate for the breadth and depth of exploration we're looking for.

Each visualization image should be a screenshot exported from a visualization tool, accompanied with a title and descriptive caption (1-4 sentences long) describing the insight(s) learned from that view. Provide sufficient detail for each caption such that anyone could read through your report and understand what you've learned. You are free, but not required, to annotate your images to draw attention to specific features of the data. You may perform highlighting within the visualization tool itself, or draw annotations on the exported image. To easily export images from Tableau, use the Worksheet > Export > Image... menu item.

The end of your report should include a brief summary of main lessons learned.

Recommended Data Sources

To get up and running quickly with this assignment, we recommend exploring one of the following provided datasets:

World Bank Indicators, 1960–2017 . The World Bank has tracked global human developed by indicators such as climate change, economy, education, environment, gender equality, health, and science and technology since 1960. The linked repository contains indicators that have been formatted to facilitate use with Tableau and other data visualization tools. However, you're also welcome to browse and use the original data by indicator or by country . Click on an indicator category or country to download the CSV file.

Chicago Crimes, 2001–present (click Export to download a CSV file). This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system.

Daily Weather in the U.S., 2017 . This dataset contains daily U.S. weather measurements in 2017, provided by the NOAA Daily Global Historical Climatology Network . This data has been transformed: some weather stations with only sparse measurements have been filtered out. See the accompanying weather.txt for descriptions of each column .

Social mobility in the U.S. . Raj Chetty's group at Harvard studies the factors that contribute to (or hinder) upward mobility in the United States (i.e., will our children earn more than we will). Their work has been extensively featured in The New York Times. This page lists data from all of their papers, broken down by geographic level or by topic. We recommend downloading data in the CSV/Excel format, and encourage you to consider joining multiple datasets from the same paper (under the same heading on the page) for a sufficiently rich exploratory process.

The Yelp Open Dataset provides information about businesses, user reviews, and more from Yelp's database. The data is split into separate files ( business , checkin , photos , review , tip , and user ), and is available in either JSON or SQL format. You might use this to investigate the distributions of scores on Yelp, look at how many reviews users typically leave, or look for regional trends about restaurants. Note that this is a large, structured dataset and you don't need to look at all of the data to answer interesting questions. In order to download the data you will need to enter your email and agree to Yelp's Dataset License .

Additional Data Sources

If you want to investigate datasets other than those recommended above, here are some possible sources to consider. You are also free to use data from a source different from those included here. If you have any questions on whether your dataset is appropriate, please ask the course staff ASAP!

• data.boston.gov - City of Boston Open Data
• MassData - State of Masachussets Open Data
• data.gov - U.S. Government Open Datasets
• U.S. Census Bureau - Census Datasets
• IPUMS.org - Integrated Census & Survey Data from around the World
• Federal Elections Commission - Campaign Finance & Expenditures
• Federal Aviation Administration - FAA Data & Research
• fivethirtyeight.com - Data and Code behind the Stories and Interactives
• Buzzfeed News
• Socrata Open Data
• 17 places to find datasets for data science projects

Visualization Tools

You are free to use one or more visualization tools in this assignment. However, in the interest of time and for a friendlier learning curve, we strongly encourage you to use Tableau . Tableau provides a graphical interface focused on the task of visual data exploration. You will (with rare exceptions) be able to complete an initial data exploration more quickly and comprehensively than with a programming-based tool.

• Tableau - Desktop visual analysis software . Available for both Windows and MacOS; register for a free student license.
• Data Transforms in Vega-Lite . A tutorial on the various built-in data transformation operators available in Vega-Lite.
• Data Voyager , a research prototype from the UW Interactive Data Lab, combines a Tableau-style interface with visualization recommendations. Use at your own risk!
• R , using the ggplot2 library or with R's built-in plotting functions.
• Jupyter Notebooks (Python) , using libraries such as Altair or Matplotlib .

Data Wrangling Tools

The data you choose may require reformatting, transformation or cleaning prior to visualization. Here are tools you can use for data preparation. We recommend first trying to import and process your data in the same tool you intend to use for visualization. If that fails, pick the most appropriate option among the tools below. Contact the course staff if you are unsure what might be the best option for your data!

Graphical Tools

• Tableau Prep - Tableau provides basic facilities for data import, transformation & blending. Tableau prep is a more sophisticated data preparation tool
• Trifacta Wrangler - Interactive tool for data transformation & visual profiling.
• OpenRefine - A free, open source tool for working with messy data.

Programming Tools

• JavaScript data utilities and/or the Datalib JS library .
• Pandas - Data table and manipulation utilites for Python.
• dplyr - A library for data manipulation in R.
• Or, the programming language and tools of your choice...

The assignment score is out of a maximum of 10 points. Submissions that squarely meet the requirements will receive a score of 8. We will determine scores by judging the breadth and depth of your analysis, whether visualizations meet the expressivenes and effectiveness principles, and how well-written and synthesized your insights are.

We will use the following rubric to grade your assignment. Note, rubric cells may not map exactly to specific point scores.

Submission Details

This is an individual assignment. You may not work in groups.

Your completed exploratory analysis report is due by noon on Wednesday 2/19 . Submit a link to your Google Doc report using this submission form . Please double check your link to ensure it is viewable by others (e.g., try it in an incognito window).

Resubmissions. Resubmissions will be regraded by teaching staff, and you may earn back up to 50% of the points lost in the original submission. To resubmit this assignment, please use this form and follow the same submission process described above. Include a short 1 paragraph description summarizing the changes from the initial submission. Resubmissions without this summary will not be regraded. Resubmissions will be due by 11:59pm on Saturday, 3/14. Slack days may not be applied to extend the resubmission deadline. The teaching staff will only begin to regrade assignments once the Final Project phase begins, so please be patient.

• Due: 12pm, Wed 2/19
• Recommended Datasets
• Example Report
• Visualization & Data Wrangling Tools
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Retail & Consumer Goods — Maxime Cohen (Member) asked a question.

Prof. Maxime Cohen (McGill University), Prof. Daniel Guetta (Columbia Business School), and Matthieu Reed (McGill University) recently wrote a case entitled " Modern Retail Analytics: Data Visualization Using Tableau ." The case includes three detailed tutorials that can be used by instructors in the classroom to introduce students to Tableau in the context of retail strategy and operations (using the Global Superstore dataset that is made available with Tableau). We also wrote a homework assignment with detailed solutions. All the material can be found here .

We're making this case available free of charge to all instructors. Speaking for ourselves, we know that when we initially downloaded the dataset, we felt it would provide a tremendous teaching opportunity, but we felt it didn't come with any "polished" resources we could easily distribute to students for use in a classroom. We hope this case will fill that gap.

We hope you find it useful.

Maxime, Daniel, and Matthieu

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