Data Preparation for Analytics A-Z™: Alteryx Hands-on

Master the Art of Data Preparation using Alteryx

Instructed by Shokat Ali

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  • In-depth understanding of common data sources and types of Data
  • How to identify and correct common issues with Data
  • To format data in useful ways for analysis
  • To blend data from multiple sources together
  • Throughout this course you'll also learn the techniques to apply your knowledge in a data analytics program called Alteryx. At the end of the course, you'll complete a project based on the principles in the course.

 This is specially crafted course that provides in-depth knowledge of Data Preparation fundamentals.

Data preparation is an integral step to generate insights. It is one of the most time-consuming and crucial processes in data mining. In simple words, data preparation is the method of collecting, cleaning, processing and consolidating the data for use in analysis. It enriches the data, transforms it and improves the accuracy of the outcome. Some of the key challenges faced by analysts and data scientists in dealing with data preparation include:

  •       Multiple data formats

  •       Data inconsistency

  •       Limited/large access to data

  •       Lack of data integration infrastructure


Data preparation is mostly done through analytical or traditional extract, transform, and load (ETL) tools. Both of which have their own advantages and limitations. In order to effectively integrate a variety of data sources, organizations should align the data, transform it and promote the development and adoption of data standards. All these things should effectively manage the volume, variety, veracity and velocity of the data.

  • Basic knowledge of computers
  • You are new to the world of data and starting in the field of problem solving with Data
  • You should take this course if you are as aspiring Business Analyst
  • You should take this course if you are as aspiring Data Analyst
  • You should take this course if you are as aspiring Data Scientist
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Section 1 : Understanding Data

  • Lecture 1 :
  • Lecture 2 :
  • Lesson Introduction
  • Lecture 3 :
  • Structure of Data
  • Lecture 4 :
  • Three Types of Data Structure
  • Lecture 5 :
  • Course Outline
  • Lecture 6 :
  • Data Sources - Files
  • Lecture 7 :
  • Data Sources - File Example
  • Lecture 8 :
  • Data Sources - File Example Continued
  • Lecture 9 :
  • Alteryx Exercise - Solution
  • Lecture 10 :
  • Data Sources - Databases
  • Lecture 11 :
  • Data Sources - Web-based Sources
  • Lecture 12 :
  • Data Sources - Web-scrapping Solution
  • Lecture 13 :
  • Data Sources - Web-scrapping Exercise

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Lecture 6


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Lecture 15


Statistics for Data Scientists and ...

By : Phikolomzi Gugwana

Lecture 31


Big Data and Hadoop

By : Saheb Singh

Lecture 26


Machine Learning from Scratch using...

By : Saheb Singh

Lecture 14


Technical Writing: How to Write Sof...

By : Jordan Stanchev

Lecture 21



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