Introduction to Modeling Success

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En provenance du cours de University of Illinois at Urbana-Champaign

Data Analytics Foundations for Accountancy II

3 notes

University of Illinois at Urbana-Champaign

Data Analytics Foundations for Accountancy II

3 notes

Welcome to Data Analytics Foundations for Accountancy II! I'm excited to have you in the class and look forward to your contributions to the learning community. To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

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Module 3: Practical Concepts in Machine Learning

This module introduces several important and practical concepts in machine learning. First, you will learn about the challenges inherent in applying data analytics (and machine learning in particular) to real world data sets. This also introduces several methodologies that you may encounter in the future that dictate how to approach, tackle, and deploy data analytic solutions. Next, you will learn about a powerful technique to combine the predictions from many weak learners to make a better prediction via a process known as ensemble learning. Specifically, this module will introduce two of the most popular ensemble learning techniques: bagging and boosting and demonstrate how to employ them in a Python data analytics script. Finally, the concept of a machine learning pipeline is introduced, which encapsulates the process of creating, deploying, and reusing machine learning models.

Introduction to Modeling Success6:11

Rencontrer les enseignants

Robert Brunner
Professor
Accountancy

Hello and welcome to this lesson on Production Data Analytics.

This lesson is going to focus on real world issues in data analytics,

in particular, the challenges that we

often don't face too heavily in a classroom setting.

This includes how to achieve modeling success, in particular,

when you're faced with issues of data cleaning and

data preparation and how you can understand

whether your model has achieved success based

on the goals you define at the start of the process.

We're also going to introduce Chris Diem.

This is an optional reading,

but I strongly encourage you to look at it.

The idea is to develop a formal procedure that

you can follow when confronting

a problem that's going to involve data analytics challenges.

So, specifically, at the end of this lesson,

you should understand the challenges of building successful models in the real world,

understand some of the differences between

classroom analytic problems and those in the real world and hopefully,

you'll also understand why we have a difficult task of trying to teach you

analytics in the classroom because companies rarely share real-world datasets with us,

and also hopefully, you can articulate issues that hinder

the promotion of data analytic processes

and when you try to move them into a production environment.

In the end, that's what you want.

You don't want to do your data analytics on a small dataset on

your laptop and then have that whole entire knowledge disappear.

You want to do something that's going to be impactful in the end.

It's going to have to move into a production environment.

We won't talk too much about production environments in this class mostly because that

involves much larger computational resources and big data and big data lakes.

And so, in this course, we're going to focus more on

the algorithms and how to apply them and where to apply them.

But you do need to be aware of the fact that you will need, in the real world,

to be working with teams who are going to be taking

what you do and putting it into a production environment.

So there's a number of readings for this particular week.

I mentioned the optional reading here that talks about Chris Diem.

Briefly, I'll walk through these websites.

You, of course, are expected to read them all.

The first one is modeling analytics successes,

and this article talks about how you can actually go from the start of

your data analytics task to actually

successfully implementing and knowing you've implemented appropriately.

That's why he has these steps that you can follow.

This is not the Chris Diem that we're going to talk about later,

but it is an idea of how to actually go through

this data analytics task that can lead you to success.

The second article is an interesting one.

It talks basically about universities doing a bad job of teaching.

And there are some points in here that I think are true.

Of course, I would counter that the argument of we are not doing a good job is because we

don't have that real-world datasets to actually demonstrate how to do data analytics on.

The main point that these articles make or are these type

of articles make is that in the real world,

in other words, in industry,

you're confronted with a question which is not clear cut.

So for instance, we may say,

make a prediction on this dataset whether it's the iris class A,

iris class B, or iris class C. That's a clean dataset.

It's a very well-defined problem.

You're often faced with a more difficult task where

you actually have to define what it is you're supposed to measure.

You also then have to find the datasets.

You may have to merge the datasets.

You may have to clean the data, fix missing data,

fix dates and time stamps,

identify missing entries that don't even fall into a normal category.

All of that work has to take place before you can

even think about applying a machine learning algorithm,

and that's the basic of this particular article, and many like it.

And that's because they're looking at it from the real world,

where the challenges they're facing,

and students they say are not coming out realizing these problems.

But, of course, here I am talking about these problems.

So, hopefully, you are somewhat aware of them.

Next one, article talks about moving from practice to production.

So there's lots of things that people do,

and they talk about how great these offerings are,

how great these machine learning techniques are,

but you have to think that it only matters

if they can be deployed to real world problems.

And so, this article talks about that challenge.

This other algorithm also talks about

building a machine learning or data science pipeline.

The whole idea is that the entire data analytics process is large and so,

you actually need to think about it from end to end.

Too often, we want to just jump right into

that machine learning algorithm and see what great things it's going to teach us.

And the point is that you have to think about it all from the start to the end.

And that's what this last one does.

This, of course, is an optional reading,

but I highly recommend you at least skim it.

And that is, it talks about

the entire way of approaching a data mining or data analytics process.

And that is, you first have to understand what it is you're actually trying to do,

then you have to understand your data,

then you have to prepare the data,

then you can build your model,

you can evaluate your model,

and then you can actually deploy it into a production environment.

Ideally then, this process would be continued because you're going to say,

is that model doing a good job in the production environment?

Did my new data that has been accumulating since I first built the model,

is it telling me something different?

Do I need to answer a different question?

All of those are important things that you need to understand and approach in

a systematic manner in order to have success in a data analytics project.

So, with that, I'm going to go ahead and stop.

If you have any questions,

please let us know. And of course, good luck.

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