Data Cleansing for Data Analytics

Data cleaning to reduce the noise data into reliable insights with Data Cleansing for Data Analytics on-demand course. Enroll now in the ASME e-learning.

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This Self Study course is designed to be taken at your convenience and on your own schedule. You have 365 days from the time of purchase to finish the course.

Description

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The primary topics provided in this course include data cleaning to reduce noise in the data followed by data interpolation. These are the necessary a priori steps in performing a data analytics investigation with clean data. The approach taken for data cleaning and interpolation consists of two primary paths. The first is the necessary mathematics, namely linear algebra and coding linear algebra. The second is the programming syntax and logic associated with data analytics.

The most efficient way to learn programming syntax and logic is to develop code that is impactful to your career. That is, programming with purpose. Finally, you will practice the use of your codes to further your understanding of data cleaning and interpolation. Beyond these two topics in data manipulation, there is a much larger learning experience waiting for you. You are learning a process that is based on mathematics to produce algorithms that are coded in programs to perform data analytics. This process will be the vehicle that carries you to lifelong learning in data analytics.

By participating in this course, you will learn how to successfully:

Write computer codes to smooth and interpolate sampled data.
Use these programs for the purpose of sampled data cleaning and interpolation.

Who should attend?
This course is developed for both the experienced and beginning users interested in acquiring knowledge command in data analytics and the associated programming skills need to perform data analytics investigations.

Course Materials (included in purchase of course)

Digital course notes via ASME's Learning Hub
Recorded tutorials via ASME's Learning Hub

A Certificate of Completion will be issued to registrants who successfully complete the course by coding data cleansing and interpolation algorithms followed by completing the case studies.

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Outline

Module 0 - Course Introduction
Module 1 - Smooth Algorithm Description
Module 2 - Coding the Smoothing Algorithm
Module 3 - Smoothing Case Studies
Module 4 - Linear Interpolation Introduction, Coding and Case Studies
Module 5 - Cubic Interpolation Introduction, Coding and Case Studies

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