Name: Data Science Techniques for High Dimensional Data Visualization in Engineering
Price: 2200 INR
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Rating: 4.0 (3 reviews)

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Data Science Techniques for High Dimensional Data Visualization in Engineering

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Amit Rawool

Data Science & Analysis

7-day money-back guarantee
Session recordings included
Certificate of completion

Volume pricing for groups of 5+

Why enroll

1. Applications of dimensionality reduction in engineering.

2. What different techniques are available.

3. Hands-on demo of dimensionality reduction to practical engineering problem.

Your instructor

Amit Rawool

Data Analytics Engineer

4+ yrs experience

2Courses

4Avg rating

Is this course for you?

You should take this if

You're a Data Science & Analysis professional
You prefer live, instructor-led training with Q&A

You should skip if

You need a different specialisation outside Data Science & Analysis
You need fully self-paced, on-demand content

Course details

Dimensionality reduction a widely used data science techniques, with application in all fields of engineering and science. This course will offer introduction to the field and a survey of various data science techniques used for dimensionality reduction, followed by a case study.

Course suitable for

Key topics covered

1. Introduction

2. Why dimensionality reduction is necessary

3. Techniques for dimensionality reduction

4. Case study

Opportunities that await you!

Career opportunities

Training details

This is a live course that has a scheduled start date.

COMPLETED

Questions and Answers

Q: You're on site for pre-commissioning and searching 'how to validate PCA visualization with incomplete P&ID during loop check' while the model suggests a new temperature set point; what do you verify first before accepting the projection?

A: Accepting the plot early can drive a set point outside physical limits and trip interlocks, re-running the math hides a wiring or scaling fault and burns time, historian-only checks miss live mis-terminations, confirming scaling and units catches mA/V errors that invalidate the visualization.

Q: During start-up you're Googling 'why t-SNE cluster shifts when one flow transmitter drifts' as a single feed flow increases; what downstream behavior should you expect and how do you respond?

A: Controller retuning can amplify the disturbance and mask the real cause, freezing visuals hides a real deviation that still propagates, tuning the model aesthetics doesn't change physics, checking raw trends avoids reacting to an algorithm that over-emphasizes local variance.

Q: With commissioning scheduled today and searching 'IEC 61511 data driven model use during commissioning' you need to justify limits on ML-driven set points; what principle from the standard applies?

A: Tying accuracy to SIL confuses statistical fit with risk reduction, bypassing safety because it's indirect erodes independence, assuming exemptions before SAT invites unsafe changes, keeping analytics advisory preserves the safety lifecycle intent.

Q: While troubleshooting and searching 'sensor drift corrosion effect on PCA features refinery' the visualization shows slow feature rotation over months; what degradation mechanism fits best?

A: Shell corrosion shifts hydraulics slowly and broadly rather than single features, cracking produces abrupt discontinuities not smooth rotation, MIC adds noise not bias, probe corrosion steadily biases readings and rotates feature space.