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Data Science Methodology

Understand steps and tasks needed for designing and building a Data Driven AI engagement
5.0
5.0/5
(1 reviews)
7 students
Created by Neena Sathi

9.9

CourseMarks Score®

10.0

Freshness

9.9

Feedback

9.2

Content

Platform: Udemy
Price: $11.99
Video: 4h 20m
Language: English
Next start: On Demand

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Detailed Analysis

CourseMarks Score®

9.9 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 4/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.9 / 10
We analyzed factors such as the rating (5.0/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.2 / 10
Video Score: 8.2 / 10
The course includes 4h 20m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
Detail Score: 9.9 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
24 resources.
0 exercise.
0 test.

Table of contents

Description

Data Science grew through our experiences with Business Intelligence or BI, a field that became popular in 1990s. However, the last 20 years have seen unprecedented improvement in our ability to take actions using Artificial Intelligence. As we adopt the BI methodologies to AI deployments, how will these methodologies morph to add considerations needed for model deployment, and machine learning.
Today’s Data Science work deals with big data. It introduces three major challenges:
•How to deal with large volumes of data. Data understanding and data preparation must deal with large scale observations about the population. In the world of BI on small samples, the art of data science was to find averages and trends using a sample and then projecting it using universal population measures such as census to project to the overall population. Most of the big data provides significant samples where such a projection may not be needed. However, bias and outliers become the real issues
•Data is now available in high velocity. Using scoring engines, we can embed insights into high velocity. Data Science techniques offer significant real-time analytics techniques to make it possible. As you interact with a web site or a product, the marketer or services teams can provide help to you as a user. This is due to insight embedded in high velocity.
•Most of the data is in speech, unstructured text or videos. This is high variety. How do we interpret an image of a driver license and extract driver license. Understanding and interpreting such data is now a central part of data science.
As these deployed models ingest learning in real-time and adjust their models, it is important to monitor their performance for biases and inaccuracies. We need measurement and monitoring that is no longer project-based one-time activity. It is continuous, automated and closely monitored. The methodology must be extended to include continuous measurement and monitoring.
The course describes 7 steps methodology for conducting data science /AI driven engagement.
•Step 1: Understand Use Case – We use illustrative examples and case studies to show the power of data science engagement and will provide strategies for defining use case and data science objectives.
•Step 2: Understand Data – We will define various characteristics of big data and how one should go about understanding and selecting right data sources for a use case from data science perspective
•Step 3: Prepare Data – How should one go about selecting, cleaning and constructing big data for data modeling purposes using analytics or AI techniques
•Step 4: Develop Model – Once you have ingested structured and un-structured data from many sources, how do you go about building models to gain data insights using AI and Analytics
•Step 5: Evaluate Model – How do you engage users and evaluate decisions? What measurements do you need on models?
•Step 6: Deploy Model- How do you deploy your AI models and apply learning of AI system from production use for enhancing your model.
•Step 7: Monitor Model – What measurements and guard-rails should be in place for continuous monitoring and learning of AI system for production use?
If you are a developer and are interested in learning how to do a data science project using Python, we have designed another course titled “Data Science in Action using Python”. 

Requirements

• None

You will learn

✓ They will be articulate data science process and methodology – BI vs AI differences
✓ They will understand how to analyze data sources and enhancements needed
✓ They will explore how users and experts will be engaged for model measurement and monitoring
✓ Finally they will understand the controls and governance aspects

This course is for

• The course is must for those embarking on a data science projects for the first time in their organization.
• Example: Project managers and IT executives responsible for the data science project execution.
• Example: Business analyst who would like to learn about how to incorporate data science in their business analysis.
• Example: Developers can use it to get exposure to data science and CRISP-DM methodology and enhancements
• There are four possible careers where this course can be used as introductory material, such as Data Scientist, Data, AI or Automation Engineer, Test Engineer

How much does the Data Science Methodology course cost? Is it worth it?

The course costs $11.99. And currently there is a 76% discount on the original price of the course, which was $49.99. So you save $38 if you enroll the course now.

Does the Data Science Methodology course have a money back guarantee or refund policy?

YES, Data Science Methodology has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Data Science Methodology course, but there is a $38 discount from the original price ($49.99). So the current price is just $11.99.

Who is the instructor? Is Neena Sathi a SCAM or a TRUSTED instructor?

Neena Sathi has created 8 courses that got 47 reviews which are generally positive. Neena Sathi has taught 205 students and received a 4.1 average review out of 47 reviews. Depending on the information available, Neena Sathi is a TRUSTED instructor.

More info about the instructor, Neena Sathi

Principal, Applied AI Institute
Neena Sathi is a principal at Applied AI Institute. She has 30+ years of experience envisioning, designing, developing and implementing AI solutions associated with enhancing customer experience, back office automation and risk and compliance for many Fortune 100 organizations. She has worked in senior technical positions at Carnegie Group, Inc, an AI startup, Accenture, KPMG, and IBM.Neena has three masters degrees including MBA from leading US universities. She is Master certified integration architect from IBM and Open Group as well as certified Project management professional (PMP) from Project management institute. She is also certified in many Cloud and Cognitive technologies. She has widely presented and published many papers in AAAI, IEEE, WCF, ECF, IBM Information on Demand, IBM Insight, World of Watson, IBM Developer Works and various academic journals.

9.9

CourseMarks Score®

10.0

Freshness

9.9

Feedback

9.2

Content

Platform: Udemy
Price: $11.99
Video: 4h 20m
Language: English
Next start: On Demand

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