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Module 3: Transformation

Data Preparation and Transformation

Objective:​

  • Teach participants data cleaning and preprocessing techniques in Tableau
  • Guide participants in joining and blending data from multiple sources
  • Provide hands-on experience in working with calculated fields and parameters
  • Help participants handle missing data and outliers effectively

3.1 Data Cleaning and Preprocessing​

  • Understanding the importance of data cleaning and data quality
  • Identifying and handling missing values, duplicates, and outliers
  • Techniques for data filtering, sorting, and aggregation

Exercise 3.1: Cleaning and Preprocessing Data​

  • Participants are given a dataset with various data quality issues
  • Participants use Tableau to clean and preprocess the data by applying appropriate techniques.

3.2 Joining and Blending Data​

  • Different types of data joins: inner, left, right, and full outer joins
  • Joining tables based on common keys or relationships
  • Blending data from multiple sources using data blending techniques

Exercise 3.2: Joining and Blending Data​

  • Participants are provided with two or more datasets to join or blend.
  • Participants use Tableau to perform the necessary joins or blending to combine the datasets.

3.3 Calculated Fields and Parameters​

  • Creating calculated fields using basic mathematical operations and functions
  • Using logical expressions and conditional statements in calculated fields
  • Introduction to Tableau parameters and their role in dynamic analysis

Exercise 3.3: Creating Calculated Fields and Parameters​

  • Participants are given specific analysis requirements.
  • Participants use Tableau to create calculated fields and parameters to meet the analysis needs.

3.4 Handling Missing Data and Outliers​

  • Techniques for handling missing data: imputation, filtering, or excluding
  • Identifying and addressing outliers in data visualizations
  • Utilizing Tableau's features for missing data and outlier management

Exercise 3.4: Handling Missing Data and Outliers​

  • Participants work with a dataset that contains missing values and outliers.
  • Participants use Tableau to implement appropriate strategies for handling missing data and outliers.

3.5 Data Aggregation and Transformation​

  • Aggregating data using dimensions and measures
  • Transforming data using pivot, split, and custom transformations
  • Grouping data and creating hierarchies for better analysis

Exercise 3.5: Data Aggregation and Transformation​

  • Participants manipulate the provided dataset using Tableau's data aggregation and transformation features.
  • Participants aggregate data, perform transformations, and create hierarchies as instructed.