Tag: Data Wrangling

Day 1

1. Intoruduction
2. Overview
3. Adding Data Table (Flat-file, database) – different options
4. Working with Information Links (optional, needed access to Spotfire Server and some test database)
5. Data Wrangling
6. Filtering, Marking, Table Relationship
7. Visualization types (Table, Cross Table, Pie Chart, Bar Chart, Line Chart, Combination Chart, Waterflow Chart…)

Day 2

1. Geo Visualizations – optional, if requested
2. Calculated Columns
3. Property Controls, Text Area
4. Report Custom Styling (Corporate design)
5. Spotfire Library Concepts, Report sharing and access concepts
6. Web Player, Scheduled Updates
7. Wrap up

• Introduction
• Tydyverse vs traditional R plotting
• Setting up your working environment
• Preparing the dataset
• Importing and filtering data
• Wrangling the data
• Visualizing the data (graphs, scatter plots)
• Grouping and summarizing the data
• Visualizing the data (line plots, bar plots, histograms, boxplots)
• Working with non-standard data
• Closing remarks

Introduction

Alteryx Overview

• What is Alteryx?
• Alteryx features

Preparing the Development Environment

• Installing and configuring Alteryx

Input and Read

• Connecting data sources to Alteryx
• Importing data
• Using the input tool

Data Wrangling

• Using data preparation tools and parsing tools
• Working with joins and unions

Transformations

• Using various pivot methods
• Aggregating data

Layouts

• Reporting layouts
• Exporting reports

Summary and Conclusion

Introduction

Alteryx Overview

• What is Alteryx
• Alteryx features

Preparing the Development Environment

• Installing and configuring Alteryx
• Navigating the interface

Alteryx Quick Start

• Importing data
• Creating and executing a workflow
• Working with documentation tools and cumulative tools

Data Management

• Preparing and cleansing data

Data Wrangling

• Creating custom formulas, algorithms, and calculations
• Performing data transformations

Data visualization

• Integrating a BI platform (Tableau, Power BI, etc.)
• Creating data visualizations
• Automating reporting

Summary and Conclusion

Perform Data Wrangling with the Python Programming Language. Practice and Solution Notebooks included.

Requirements

• You will need to have basic python programming proficiency.
• You will need a modern browser i.e. Google Chrome or Mozilla Firefox.

Description

By the end of this course, you will be able to:

• Load a local dataset from CSV and Excel files.
• Import a dataset from CSV and Excel files via a URL.
• Determine the size of a dataset.
• Explore the first and last records of a dataset.
• Explore the datatypes of the features of a dataset.
• Check for missing data in a dataset.
• Deal with missing data in a dataset.
• Filter for records with certain values from a dataset.
• Filter records with multiple filters from a dataset.
• Filter for records from a dataset through the use of conditions.
• Perform sorting in ascending and descending order.
• Split a column in a dataset.
• Merge data frames to form a dataset.
• Concatenate two columns to one column in a dataset.
• Export a dataset in CSV and Excel formats.

Who this course is for:

• This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.

Course content

14 sections • 14 lectures • 1h 23m total lengthExpand all sections