Accelerate your Alteryx knowledge by exploring data preparation techniques! You’ll learn why clean and prepared data is important for optimal analytics and how to utilize the main data types. You’ll also discover how to recognize “dirty data” and techniques for cleansing data, such as managing missing values and removing unnecessary data.

What is data cleaning and preparation?

Why is clean data important?

Preparing and cleansing fields

Importing data and modifying fields

Cleansing data fields

Removing rows and sorting

Data Cleaning and Preparation

Expand your data preparation skills in Alteryx by uncovering ways to filter data and write expressions to create subsets of data. Basic and chained filters will be explored, along with custom filters, to get right to the data you need. You’ll learn best practices in writing expressions, including understanding the differences between AND and OR statements, along with helpful tips for correct syntax.

Filtering and Using Expressions

Options

Correct

Incorrect

Understanding filter expressions

AND, OR and more!

Basic and chained filters

Custom filters and expressions

Combining sort and unique tools

Go further into the layers of data preparation by diving deeper into various formulas and how they can be quickly applied in Alteryx. Discover the wealth of pre-structured formulas available and ready to use in the Formula tool, along with techniques for writing efficient expressions based on data types. You’ll learn the difference between length and position-based formulas and methods to update and create new fields.

Applying Formulas in Alteryx

Making the best expression

Formulas and best practices

Existing fields and new fields

Length and position-based formulas

Contains formula and flags

Applying Formulas

Explore the processes of sampling and summarizing your data, which are essential aspects of data preparation. You’ll learn many options for sampling data, such as finding the top 10 records or obtaining the first N percentage of data, and much more with the Sample tool. You’ll then expand your knowledge of summarizing data using numerical methods like sum, average, and median, as well as string functions like grouping by specific fields hierarchically in the Summarize tool.

Sampling and Summarizing

Applying the right concepts

Grouping insights and creating output

Top 10 and average sale prices

Summary and data output

Input data and sampling top 25%

Congratulations!

Exercises and Datasets

DataCamp vs. Local Experience

Discover how to prepare, clean, and analyze data in Alteryx Designer! Whether you are new to data analytics in general or an experienced analyst in other platforms, this course will guide you to new levels of understanding the essentials of solid data preparation. You’ll learn why clean and prepared data is important for optimal analytics and how to recognize what aspects of your datasets need cleaning. <br><br>
Using the Preparation Toolset, you’ll explore removing null data, filtering on specific criteria, applying various formulas, analyzing duplicate data, and many more features that will advance you to perform more intermediate-level analysis in the versatile Alteryx Designer platform. Achieve deeper knowledge of Alteryx Designer best practices and take your knowledge to the next level with hands-on experience. Now is the time to get started on your Alteryx journey!

Embark on a transformative journey through the world of data analytics with our comprehensive course on data preparation in Alteryx. Through engaging content and practical exercises, you will learn the critical importance of preparing and cleansing data for analysis, enabling you to extract more accurate and insightful information from your datasets.

<h2>Foundations of Data Preparation</h2> 

Dive into the essentials of data preparation by understanding the significance of clean data and learning to identify and rectify common data quality issues. This section covers techniques for managing missing values, eliminating redundant data, and understanding data types, laying a solid foundation for more advanced data preparation tasks.<br><br>

<h2>Enhancing Data with Filters and Formulas</h2> 

Advance your skill set by mastering filtering techniques and the art of writing expressions in Alteryx. You'll explore a range of filters, from basic to custom, and learn how to craft expressions that refine your data for analysis. Additionally, the course delves into the application of formulas, teaching you to utilize pre-structured formulas and write efficient expressions tailored to specific data types, thereby expanding your capabilities in data manipulation.<br><br>

<h2> Data Sampling and Summarization Techniques</h2> 

The course culminates with strategies for sampling and summarizing data, essential skills for any data analyst. You'll learn various sampling methods to extract meaningful subsets of data and employ numerical and string functions to summarize and derive insights from your datasets. <br><br>

Upon completing this course, you'll grasp the importance of clean data for analytics, gain practical experience with Alteryx Designer, and acquire skills for intermediate analysis, laying a solid groundwork for your data analytics journey.

Introduction to Alteryx

Learn to prepare, clean, and analyze data with Alteryx Designer. Ideal for beginners and experienced analysts aiming to master data analytics essentials.

Data Preparation in Alteryx

Master data preparation, cleaning, and analysis in Alteryx Designer, whether you are a new or seasoned analyst.

Alteryx Grundlagen

An AND statement means that BOTH criteria are met.

Custom expressions for the same field must have the field name stated before each criteria, such as [Date] > 2010 AND [Date] < 2024.

Data types do not matter in writing expressions.

When writing expressions for string values, no quotes are needed.

An OR statement means that BOTH criteria are met.

When writing expressions for string fields, if the criteria appears to be a number, do not use quotes.

Understanding filter expressions

Data Preparation in Alteryx

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