Data Science Methodology

Understand steps and tasks needed for designing and building a Data Driven AI engagement

4.75 (124 reviews)
Udemy
platform
English
language
Other
category
instructor
Data Science Methodology
262
students
2.5 hours
content
Jun 2022
last update
$64.99
regular price

What you will learn

Learn to articulate data science process and methodology steps

Understand how to analyze data sources

Create and validate data science models by applying analytics techniques

Explore how users and experts will be engaged for model measurement and monitoring

Learn how to apply the methodology on a practice problem

Why take this course?

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:

  1. 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

  2. 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.

  3. 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: Optimize Model - How would you fine-tune the model and optimize its performance over time using feedback from production use?  What guide rails would you need to make sure field use does not result in biases or sabotage.

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". 

Content

Introduction

Introduction
Course Outline
Introduction to data science methodology course will provide you

Step 1: Describe Use Case

Data Science Challenges & Enhanced data Science Methodology
Key challenges of AI solutions
Define a use case
Key criteria for selecting a use case
Step 1 - Describe Use Case - Use Case Example
Documentation of a use case

Step 2- Describe Data

Characteristics of Big Data
Four common vectors describing Big Data
Data Sources and Contents
Data content classification
Step 2- Describe Data - Use Case Example
Good authentic data sources

Step 3 - Prepare Data

Step 3 - Prepare Data - Initial Tasks
Data source selection
Step 3 - Prepare Data - Final Tasks
Feature engineering
Step 3 - Prepare Data - Use Case Example
Good authentic sources to prepare your data, if applicable.

Step 4 - Develop Model

Step 4 - Develop Model - Overview
AutoML
Step 4 - Develop Model - Time Series Forecasting
What is Time Series Forecasting
Step 4 - Develop Model - Classification
Decision Tree based Categorization
Step 4 - Develop Model - Regression
Regression
Step 4 - Develop Model - Graph Analysis
Graph

Step 5 - Evaluate Model

Step 5 - Evaluate Model - Model Measurements
F1 score
Step 5 - Evaluate Model - Model Management
Role of Model Librarian for model management

Step 6 - Deploy Model

Step 6 - Deploy Model - Deployment Framework
Key benefit of Artemis Project
Step 6 - Deploy Model - Deployment Options
Typical mechanism for deploying an AI Model
Step 6 - Deploy Model - Use Case Example

Step 7 - Monitor Model

Step 7 - Monitor Model
Purpose of Step 7 - Monitor Model

Summary

Final Review
Bonus Lecture

Screenshots

Data Science Methodology - Screenshot_01Data Science Methodology - Screenshot_02Data Science Methodology - Screenshot_03Data Science Methodology - Screenshot_04

Reviews

Dominic
July 18, 2022
There are some gaps in terms of doing the work vs understanding what the work will be, but I assume this is covered more in depth in later learnings.
Milton
July 13, 2022
I found the content to be very useful and practical. It will be great to have more examples added to the content to show all their applicability in all business process areas (i.e. finance, strategy, operations). Instructors were very clear on delivering the content.
Claire
July 7, 2022
Fantastic content that sets the stage well for understanding data science, this allows students to effectively deepend data science knowledge.
Melissa
May 30, 2022
Great course to learn about data science methodology. Thorough job on breaking down each of the areas and providing a use case on how each step would work on it.
Akua
April 19, 2022
Overall, I think this course was good. I would have liked to see why an answer was wrong for the quizzes to further my understanding of the concepts.
Boluwatife
March 24, 2022
It was good taking this course and learning more about the different areas of data science. I found it especially relevant coming from a business background and working as a consultant
Matt
March 19, 2022
The course is very robust and covers some fairly complex topics in a short amount of time. It does a good job of laying a foundation for more in-depth learning across topics in the future. It'd be beneficial to have a quick reference guide/pre-work on some of the terms/topics to create a better baseline understanding and reference as the training is completed.

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3827010
udemy ID
2/5/2021
course created date
3/10/2021
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