Data Science Methodologies: Making Business Sense

Data Science Methodologies: Making Business Sense

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 49m | 261 MB

There is an increasing recognition that data science needs to go beyond small-scale experimentation to a large-scale implementation. In this course, Neelam Dwivedi brings software engineering and data mining methodologies to data scientists, then applies these ideas by taking a simple business need through an entire life cycle—hosting a model, consuming it in a web application, and setting up its CI/CD pipeline. Neelam begins by explaining the methodologies used in the course and how they are combined. She shows you where to begin in developing architecture and deploying a model, then explains how larger web applications may consume the model as a service. Neelam covers how to stage your model and the app, as well as how to plan ahead with an overall roadmap. She concludes with thoughts on how to further applications of data science methodologies.

Table of Contents

1 Models and the real world
2 What you should know
3 Why methodologies
4 Data science vs. software engineering
5 Data mining methodologies
6 Software engineering methodologies in data science
7 Where to begin
8 Development architecture
9 Deploy the model
10 Consume the model
11 Challenge 1
12 Solution 1
13 Set up a CICD pipeline
14 Deploy the app
15 The roadmap
16 Challenge 2
17 Solution 2
18 Furthering applications of data science methodologies